A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms
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paper_title: Optimal Control Policies for Power Demand Scheduling in the Smart Grid

paper_content:

We study the problem of minimizing the long-term average power grid operational cost through power demand scheduling. A controller at the operator side receives consumer power demand requests with different power requirements, durations and time flexibilities for their satisfaction. Flexibility is modeled as a deadline by which a demand is to be activated. The cost is a convex function of total power consumption, which reflects the fact that each additional unit of power needed to serve demands is more expensive to provision, as demand load increases. We develop a stochastic model and introduce two online demand scheduling policies. In the first one, the Threshold Postponement (TP), the controller serves a new demand request immediately or postpones it to the end of its deadline, depending on current power consumption. In the second one, the Controlled Release (CR), a new request is activated immediately if power consumption is lower than a threshold, else it is queued. Queued demands are activated when deadlines expire or when consumption drops below the threshold. These policies admit an optimal control with switching curve threshold structure, which involves active and postponed demand. The CR policy is asymptotically optimal as deadlines increase, namely it achieves a lower bound on average cost, and the threshold depends only on active demand. Numerical results validate the benefit of our policies compared to the default one of serving demands upon arrival.
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paper_title: Demand response resources: Who is responsible for implementation in a deregulated market?

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Demand response resources (DRR) have potential to offer substantial benefits in the form of improved economic efficiency in wholesale electricity markets. Those benefits include better capacity factors for existing capacity, reductions in requirements for new capacity, enhanced reliability, relief of congestion and transmission constraints, reductions in price volatility, mitigation of market power and lower electricity prices for consumers. However, DRR has been slow to penetrate. There has been substantial disagreement as to which entities in a restructured market should promote the expanded implementation of DRR. This paper contends that no single entity can perform this function. But rather, wider implementation will need to accrue from coordinated actions along the electricity supply chain.
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paper_title: Knowing when to act: an optimal stopping method for smart grid demand response

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A major benefit of the smart grid is that it can provide real-time pricing, which enables residential electricity customers to reduce their electricity expenses by scheduling their appliance use. A commonly utilized technique is to operate electrical appliances when the price of electricity is low. Although this technique is simple in principle and easy to apply, there are several issues that need to be addressed: Studies have shown that residents do not know how, or have the time to take advantage of real-time price information; residents seek to save money by delaying device usage but do not want the inconvenience of long wait times; and a lack of automated energy management systems - industry trials of real-time pricing programs requiring manual user intervention have performed poorly. In this work, we address these issues by the means of an optimal stopping approach, which can balance electricity expense and waiting time. We formulate the problem of deciding when to start home appliances as an optimal stopping problem, and combine optimal stopping rules with appliance energy usage profiles. Our result is an automated residential energy management and scheduling platform, which can reduce energy bills and minimize peak loads. Reduced peak loads result in lower usage rates of dirty coal-based peaker plants, reducing carbon emissions. Additional benefits include lower domestic energy use by controlling vampire loads. Simulation results show that our approach can reduce energy costs by about 10-50 percent depending on the appliance type, and demonstrate the usefulness of our approach in managing residential energy costs.
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paper_title: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

paper_content:

Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
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paper_title: Impact of demand response on distribution system reliability

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Demand response (DR) is a market driven and sometimes semi-emergency action performed at the utility level or at the Demand Response Service Provider (aggregator) with the objective of reducing the overall demand of the system during peak load hours. If implemented successfully, DR helps postpone the capacity expansion projects related to the distribution network, and provides a collaborative framework for the liberalized energy market of the Smart Grid. Customers subscribed to the DR program are requested to reduce their demand or turn off one or more energy consuming appliances in exchange for financial incentives such as extra payments or discounted electricity rates. This would change the concept of distribution system reliability as is traditionally known. From one hand, DR could lead to a higher amount of unserved energy; on the other hand, it does not qualify as an unwanted lost load. This paper tries to provide a qualitative analysis on the impact of demand response on distribution system reliability.
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paper_title: Demand side management program evaluation based on industrial and commercial field data

paper_content:

Demand Response is increasingly viewed as an important tool for use by the electric utility industry in meeting the growing demand for electricity. There are two basic categories of demand response options: time varying retail tariffs and incentive Demand Response Programs. Electricity Saudi Company (ESC) is applying the time varying retail tariffs program, which is not suitable according to the studied load curves captured from the industrial and commercial sectors. Different statistical studies on daily load curves for consumers connected to 22kV lines are classified. The load curve criteria used for classification is based on peak ratio and night ratio. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the King Saudi Arabia (KSA)) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas with larger commercial and industrial customers. The study proved that the suitable Demand Response for the ESC is the incentive program.
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paper_title: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

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Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
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paper_title: A review on distributed energy resources and MicroGrid

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The distributed energy resources (DER) comprise several technologies, such as diesel engines, micro turbines, fuel cells, photovoltaic, small wind turbines, etc. The coordinated operation and control of DER together with controllable loads and storage devices, such as flywheels, energy capacitors and batteries are central to the concept of MicroGrid (MG). MG can operate interconnected to the main distribution grid, or in an islanded mode. This paper reviews the researches and studies on MG technology. The operation of MG and the MG in the market environment are also described in the paper.
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paper_title: Smart Operation of Smart Grid: Risk-Limiting Dispatch

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The drastic reduction of carbon emission to combat global climate change cannot be realized without a significant contribution from the electricity sector. Renewable energy resources must take a bigger share in the generation mix, effective demand response must be widely implemented, and high-capacity energy storage systems must be developed. A smart grid is necessary to manage and control the increasingly complex future grid. Certain smart grid elements-renewables, storage, microgrid, consumer choice, and smart appliances-increase uncertainty in both supply and demand of electric power. Other smart gird elements-sensors, smart meters, demand response, and communications-provide more accurate information about the power system and more refined means of control. Simply building hardware for renewable generators and the smart grid, but still using the same operating paradigm of the grid, will not realize the full potential for overall system efficiency and carbon reduction. In this paper, a new operating paradigm, called risk-limiting dispatch, is proposed. It treats generation as a heterogeneous commodity of intermittent or stochastic power and uses information and control to design hedging techniques to manage the risk of uncertainty.
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paper_title: A summary of demand response in electricity markets

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Abstract This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.
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paper_title: Demand response in smart electricity grids equipped with renewable energy sources: A review

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Dealing with Renewable Energy Resources (RERs) requires sophisticated planning and operation scheduling along with state of art technologies. Among many possible ways for handling RERs, Demand Response (DR) is investigated in the current review. Because of every other year modifications in DR definition and classification announced by Federal Energy Regulatory Commission (FERC), the latest DR definition and classification are scrutinized in the present work. Moreover, a complete benefit and cost assessment of DR is added in the paper. Measurement and evolution methods along with the effects of DR in electricity prices are discussed. Next comes DR literature review of the recent papers majorly published after 2008. Eventually, successful DR implementations, around the world, are analyzed.
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paper_title: A user-mode distributed energy management architecture for smart grid applications

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Future smart grids will require a flexible, observable, and controllable network architecture for reliable and efficient energy delivery under uncertain conditions. They will also necessitate variability in distributed energy generators and demand-side loads. This study presents a tree-like user-mode network architecture responding to these requirements. The approaches presented for the next-generation grid architecture facilitate the management of distributed generation strategies based on renewable sources, distributed storage, and demand-side load management.
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paper_title: Demand Response and Electricity Market Efficiency

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Customer response is a neglected way of solving electricity industry problems. Historically, providers have focused on supply, assuming that consumers are unwilling or unable to modify their consumption. Contrary to these expectations, customers respond to higher prices that they expect to continue by purchasing more efficient appliances and taking other efficiency measures, a review of published studies indicates.
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paper_title: Demand response and smart grids—A survey

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The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.
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paper_title: Coordinating Storage and Demand Response for Microgrid Emergency Operation

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Microgrids are assumed to be established at the low voltage distribution level, where distributed energy sources, storage devices, controllable loads and electric vehicles are integrated in the system and need to be properly managed. The microgrid system is a flexible cell that can be operated connected to the main power network or autonomously, in a controlled and coordinated way. The use of storage devices in microgrids is related to the provision of some form of energy buffering during autonomous operating conditions, in order to balance load and generation. However, frequency variations and limited storage capacity might compromise microgrid autonomous operation. In order to improve microgrid resilience in the moments subsequent to islanding, this paper presents innovative functionalities to run online, which are able to manage microgrid storage considering the integration of electric vehicles and load responsiveness. The effectiveness of the proposed algorithms is validated through extensive numerical simulations.
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paper_title: Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization

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A gridable vehicle (GV) can be used as a small portable power plant (S3P) to enhance the security and reliability of utility grids. Vehicle-to-grid (V2G) technology has drawn great interest in the recent years and its success depends on intelligent scheduling of GVs or S3Ps in constrained parking lots. V2G can reduce dependencies on small expensive units in existing power systems, resulting in reduced operation cost and emissions. It can also increase reserve and reliability of existing power systems. Intelligent unit commitment (UC) with V2G for cost and emission optimization in power system is presented in this paper. As number of gridable vehicles in V2G is much higher than small units of existing systems, UC with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to balance between cost and emission reductions for UC with V2G. PSO can reliably and accurately solve this complex constrained optimization problem easily and quickly. In the proposed solution model, binary PSO optimizes on/off states of power generating units easily. Vehicles are presented by integer numbers instead of zeros and ones to reduce the dimension of the problem. Balanced hybrid PSO optimizes the number of gridable vehicles of V2G in the constrained parking lots. Balanced PSO provides a balance between local and global searching abilities, and finds a balance in reducing both operation cost and emission. Results show a considerable amount of cost and emission reduction with intelligent UC with V2G. Finally, the practicality of UC with V2G is discussed for real-world applications.
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paper_title: Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

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Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.
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paper_title: 1 Smart Distribution: Coupled Microgrids

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The distribution system provides major opportunities for smart grid concepts. One way to approach distribution system problems is to rethinking our distribution system to include the integration of high levels of distributed energy resources, using microgrid concepts. Basic objectives are improved reliability, promote high penetration of renewable sources, dynamic islanding, and improved generation efficiencies through the use of waste heat. Managing significant levels of distributed energy resources (DERs) with a wide and dynamic set of resources and control points can become overwhelming. The best way to manage such a system is to break the distribution system down into small clusters or microgrids, with distributed optimizing controls coordinating multimicrogrids. The Consortium for Electric Reliability Technology Solutions (CERTSs) concept views clustered generation and associated loads as a grid resource or a “microgrid.” The clustered sources and loads can operate in parallel to the grid or as an island. This grid resource can disconnect from the utility during events (i.e., faults, voltage collapses), but may also intentionally disconnect when the quality of power from the grid falls below certain standards. This paper focuses on DER-based distribution, the basics of microgrids, possibility of smart distribution systems using coupled microgrid and the current state of autonomous microgrid technology.
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paper_title: Autonomous Distributed V2G (Vehicle-to-Grid) Satisfying Scheduled Charging

paper_content:

To integrate large scale renewable energy sources in the power grid, the battery energy storage performs an important role for smoothing their natural intermittency and ensuring grid-wide frequency stability. Electric vehicles have not only large introduction potential but also much available time for control because they are almost plugged in the home outlets as distributed battery energy storages. Therefore, vehicle-to-grid (V2G) is expected to be one of the key technologies in smart grid strategies. This paper proposes an autonomous distributed V2G control scheme. A grid-connected electric vehicle supplies a distributed spinning reserve according to the frequency deviation at the plug-in terminal, which is a signal of supply and demand imbalance in the power grid. As a style of EV utilization, it is assumed that vehicle use set next plug-out timing in advance. In such assumption, user convenience is satisfied by performing a scheduled charging for the plug-out, and plug-in idle time is available for the V2G control. Therefore a smart charging control is considered in the proposed scheme. Satisfaction of vehicle user convenience and effect to the load frequency control is evaluated through a simulation by using a typical two area interconnected power grid model and an automotive lithium-ion battery model.
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paper_title: Pool-Based Demand Response Exchange—Concept and Modeling

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In restructured power systems, there are many independent players who benefit from demand response (DR). These include the transmission system operator (TSO), distributors, retailers, and aggregators. This paper proposes a new concept-demand response eXchange (DRX)-in which DR is treated as a public good to be exchanged between DR buyers and sellers. Buyers need DR to improve the reliability of their own electricity-dependent businesses and systems. Sellers have the capacity to significantly modify electricity demand on request. Microeconomic theory is applied to model the DRX in the form of a pool-based market. In this market, a DRX operator (DRXO) collects DR bids and offers from the buyers and sellers, respectively. It then clears the market by maximizing the total market benefit subject to certain constraints including: demand-supply balance, and assurance contracts related to individual buyer contributions for DR. The DRX model is also tested on a small power system, and its efficiency is reported.
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paper_title: Distributed Demand and Response Algorithm for Optimizing Social-Welfare in Smart Grid

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This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart girds. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.
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paper_title: The Smart Grid – A saucerful of secrets?

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To many, a lot of secrets are at the bottom of the often-cited catchphrase "Smart Grid". This article gives an overview of the options that information and communication technology (ICT) offers for the restructuring and modernisation of the German power system, in particular with a view towards its development into a Smart Grid and thus tries to reveal these secrets. After a short outline on the development of ICT in terms of technology types and their availability, the further analysis highlights upcoming challenges in all parts of the power value chain and possible solutions for these challenges through the intensified usage of ICT applications. They are examined with regard to their effectiveness and efficiency in the fields of generation, transmission, distribution and supply. Finally, potential obstacles that may defer the introduction of ICT into the power system are shown. The analysis suggests that if certain hurdles are taken, the huge potential of ICT can create additional value in various fields of the whole power value chain. This ranges from increased energy efficiency and the more sophisticated integration of decentralised (renewable) energy plants to a higher security of supply and more efficient organisation of market processes. The results are true for the German power market but can in many areas also be transferred to other industrialised nations with liberalised power markets.
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paper_title: Integrated Voltage, Var Control and demand response in distribution systems

paper_content:

This paper addresses the requirements for utilizing Voltage and Var Control for demand response, in an operating environment which includes Smart-Grid, Distribution Management Systems, Advanced Metering Infrastructure, Demand Response, and Distributed Energy Resources.
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paper_title: Optimal pricing of default customers in electrical distribution systems: Effect behavior performance of demand response models

paper_content:

The response of a non-linear mathematical model is analyzed for the calculation of the optimal prices for electricity assuming default customers under different scenarios and using five different mathematical functions for the consumer response: linear, hyperbolic, potential, logarithmic and exponential. The mathematical functions are defined to simulate the hourly changes in the consumer response according to the load level, the price of electricity, and also depending on the elasticity at every hour. The behavior of the optimization model is evaluated separately under two different objective functions: the profit of the electric utility and the social welfare. The optimal prices as well as the served load are calculated for two different operation schemes: in an hourly basis and also assuming a single constant price for the 24 h of the day. Results obtained by the optimization model are presented and compared for the five different consumer load functions.
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paper_title: Demand side management program evaluation based on industrial and commercial field data

paper_content:

Demand Response is increasingly viewed as an important tool for use by the electric utility industry in meeting the growing demand for electricity. There are two basic categories of demand response options: time varying retail tariffs and incentive Demand Response Programs. Electricity Saudi Company (ESC) is applying the time varying retail tariffs program, which is not suitable according to the studied load curves captured from the industrial and commercial sectors. Different statistical studies on daily load curves for consumers connected to 22kV lines are classified. The load curve criteria used for classification is based on peak ratio and night ratio. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the King Saudi Arabia (KSA)) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas with larger commercial and industrial customers. The study proved that the suitable Demand Response for the ESC is the incentive program.
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paper_title: Demand Response Architecture: Integration into the Distribution Management System

paper_content:

Demand Response (DR) refers to actions taken by the utility to respond to a shortage of supply for a short duration of time in the future. DR is one of the enablers of the Smart Grid paradigm as it promotes interaction and responsiveness of the customers and changes the grid from a vertically integrated structure to one that is affected by the behavior of the demand side. In Principle, it is possible to perform DR at the substation level for the customers connected to the feeders downstream or at the demand response service provider (aggregator) for the customers under its territory. This would allow for an area based solution driven mostly by the financial aspects as well as terms and conditions of the mutual agreements between the individual customers and the utility. However, as the penetration of DR increases, incorporating the network model into the DR analysis algorithm becomes necessary. This ensures the proper performance of the DR process and achieves peripheral objectives in addition to achieving the target demand reduction. The added value to the DR algorithm by incorporating the model of the distribution network can only be realized if the engine is developed as an integrated function of the Distribution Management System (DMS) at the network control center level. This paper focuses on the demand response architecture implemented at the DMS level and discusses some practical considerations associated with this approach
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paper_title: Residential Demand Response model and impact on voltage profile and losses of an electric distribution network

paper_content:

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).
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paper_title: Predictive power dispatch through negotiated locational pricing

paper_content:

A predictive mechanism is proposed in order to reduce price volatility linked to large fluctuations from demand and renewable energy generation in competitive electricity markets. The market participants are modelled as price-elastic units, price-inelastic units, and storage operators. The distributed control algorithm determines prices over a time horizon through a negotiation procedure in order to maximize social welfare while satisfying network constraints. A simple flow allocation method is used to assign responsibility for constraint violations on the network to individual units and a control rule is then used to adjust nodal prices accordingly. Such a framework is appropriate for the inclusion of aggregated household appliances or other ‘virtual’ market participants realized through smart grid infrastructure. Results are examined in detail for a 4-bus network and then success is demonstrated for a densely-populated 39-bus network. Formal convergence requirements are given under a restricted subset of the demonstrated conditions. The scheme is shown to allow storage to reduce price volatility in the presence of fluctuating demand.
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paper_title: Real-time multi-agent support for decentralized management of electric power

paper_content:

Establishing clean or renewable energy sources involves the problem of adequate management for the networked power sources, in particular since producers are at the same time also consumers, and vice versa. We describe the first phases of the joint R&D project DEZENT between the School of Computer Science and the College of Electrical Engineering at the University of Dortmund, devoted to decentralized and adaptive electric power management through a distributed real-time multi-agent architecture. Unpredictable consumer requests or producer problems, under distributed control or local autonomy will be the major novelty. We present a distributed real-time negotiation algorithm involving agents on different levels of negotiation, on behalf of producers and consumers of electric energy. Despite the lack of global overview we are able to prove that in our model no coalition of malicious users could take advantage of extreme situations like arising from an abundance as much as from any (artificial) shortage of electric power that are typical problems in "free" or deregulated markets. Our multi-agent system exhibits a very high robustness against power failures compared to centrally controlled architectures. In extensive experiments we demonstrate how, in realistic settings of the German power system structure, the novel algorithms can cope with unforeseen needs and production specifics in a very flexible and adaptive way, taking care of most of the potentially hard deadlines already on the local group level (corresponding to a small subdivision). We further demonstrate that under our decentralized approach customers pay less than under any conventional (global) management policy or structure.
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paper_title: Demand Response and Distribution Grid Operations: Opportunities and Challenges

paper_content:

Demand response (DR) is becoming an integral part of power system and market operations. Smart grid technologies will further increase the use of DR in everyday operations. Once the volume of the DR reaches a certain threshold, the effect of the DR events on the distribution and transmission system operations will be hard to ignore. This paper proposes changing the business process of DR scheduling and implementation by integrating DR with distribution grid topology. Study cases using OATI webDistribute show the potential DR effect on distribution grid operations and the distribution grid changing the effectiveness of the DR. These examples illustrate the need of integrating demand response with the distribution grid.
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paper_title: Agent-Based Control Framework for Distributed Energy Resources Microgrids

paper_content:

Distributed energy resources (DERs) provide many benefits for the electricity users and utilities. However, the electricity distribution system traditionally was not designed to accommodate active power generation and storage at the distribution level. The microgrid provides an effective approach to integrating many small-scale distributed energy resources into the bulk electric grid. This paper presents an agent-based control framework for distributed energy resources microgrids. The features of agent technology are first discussed. An agent-based control framework for DER microgrids is then presented. To demonstrate the effectiveness of the proposed agent-based control framework, simulation studies have been performed on a dc distributed energy system that can be used in a microgrid as a modular power generation unit. Simulation results clearly indicate that the agent-based control framework is effective to coordinate the various distributed energy resources and manage the power and voltage profiles.
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paper_title: PowerMatcher: multiagent control in the electricity infrastructure

paper_content:

Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.
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paper_title: An Agent-based Market Platform for Smart Grids

paper_content:

The trend towards renewable, decentralized, and highly fluctuating energy suppliers (e.g. photovoltaic, wind power, CHP) introduces a tremendous burden on the stability of future power grids. By adding sophisticated ICT and intelligent devices, various Smart Grid initiatives work on concepts for intelligent power meters, peak load reductions, efficient balancing mechanisms, etc. As in the Smart Grid scenario data is inherently distributed over different, often non-cooperative parties, mechanisms for efficient coordination of the suppliers, consumers and intermediators is required in order to ensure global functioning of the power grid. In this paper, a highly flexible market platform is introduced for coordinating self-interested energy agents representing power suppliers, customers and prosumers. These energy agents implement a generic bidding strategy that can be governed by local policies. These policies declaratively represent user preferences or constraints of the devices controlled by the agent. Efficient coordination between the agents is realized through a market mechanism that incentivizes the agents to reveal their policies truthfully to the market. By knowing the agent's policies, an efficient solution for the overall system can be determined. As proof of concept implementation the market platform D'ACCORD is presented that supports various market structures ranging from a single local energy exchange to a hierarchical energy market structure (e.g. as proposed in [10]).
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paper_title: Dynamic Residential Demand Response and Distributed Generation Management in Smart Microgrid with Hierarchical Agents

paper_content:

Abstract Smart grid has been a significant development trend of power system. Within smart grid, microgrids share the burden of traditional grids, reduce energy consumption cost and alleviate environment deterioration. This paper proposes a dynamic Demand Response (DR) and Distributed Generation (DG) management approach in the context of smart microgrid for a residential community. With a dynamic update mechanism, the DR operates automatically and allows manual interference. The DG management coordinates with DR and considers stochastic elements, such as stochastic load and wind power, to reduce the energy consumption cost of the community. Simulation and numerical results show the effectiveness of the system on reducing the energy consumption cost while keeping users’ satisfaction at a high level.
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paper_title: Residential Demand Response model and impact on voltage profile and losses of an electric distribution network

paper_content:

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).
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paper_title: Evaluation and assessment of demand response potential applied to the meat industry

paper_content:

Demand response has proven to be a useful mechanism that produces important benefits for both the customer and the power system. In the context of an increasingly competitive electricity market, where prices are constantly rising and the presence of renewable energy resources is gaining prominence, this paper analyzes the flexibility potential of customers in the meat industry, based on the management of the most energy consuming process in this type of segment: cooling production and distribution.
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paper_title: Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

paper_content:

Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.
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paper_title: Smart grid technologies and applications for the industrial sector

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Abstract Smart grids have become a topic of intensive research, development, and deployment across the world over the last few years. The engagement of consumer sectors—residential, commercial, and industrial—is widely acknowledged as crucial for the projected benefits of smart grids to be realized. Although the industrial sector has traditionally been involved in managing power use with what today would be considered smart grid technologies, these applications have mostly been one-of-a-kind, requiring substantial customization. Our objective in this article is to motivate greater interest in smart grid applications in industry. We provide an overview of smart grids and of electricity use in the industrial sector. Several smart grid technologies are outlined, and automated demand response is discussed in some detail. Case studies from aluminum processing, cement manufacturing, food processing, industrial cooling, and utility plants are reviewed. Future directions in interoperable standards, advances in automated demand response, energy use optimization, and more dynamic markets are discussed.
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paper_title: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

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Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
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paper_title: Intelligent demand response scheme for energy management of industrial systems

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Electric demand side management (DSM) focuses on changing the electricity consumption patterns of end-use customers through improving energy efficiency and optimizing allocation of power. Demand response (DR) is a DSM solution that targets residential, commercial and industrial customers, and is developed for demand reduction or demand shifting at a specific time for a specific duration. In the absence of on-site generation or possibility of demand shifting, consumption level needs to be lowered. While non-criticality of loads at the residential and commercial levels allows for demand reduction with relative ease, demand reduction of industrial processes requires a more sophisticated solution. Production constraints, inventory constraints, maintenance schedules and crew management are some of the many factors that have to be taken into account before one or more processes can be temporarily shut down. An intelligent system is designed in this paper for implementation of DR at an industrial site. Based on the various operational constraints of the industrial process, it determines the loads that could be potentially curtailed. Fuzzy/expert systems are used to derive a priority factor for different candidate loads. This information can then be used by the plant operator/DR client to make a comply/opt-out decision during a utility-initiated DR event.
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paper_title: Utilizing Automated Demand Response in commercial buildings as non-spinning reserve product for ancillary services markets

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In 2009, a pilot program was conducted to investigate the technical feasibility of bidding non-residential demand response (DR) resources into the California Independent System Operator's (CAISO) day-ahead ancillary services market as non-spinning reserves product. Three facilities, a retail store, a local government office building, and a bakery, were recruited into the pilot program and moved from automated price responsive programs to CAISO's participating load program. For each facility, hourly demand, and load curtailment potential were forecasted two days ahead and submitted to the CAISO the day before the trading day as an available resource. These DR resources were optimized against all other generation resources in the CAISO ancillary services market. Each facility was equipped with four-second real time telemetry equipment to ensure resource accountability and visibility to CAISO operators. When CAISO requests DR resources, OpenADR (Open Automated DR) communications infrastructure was utilized to deliver DR signals to the facilities' energy management and control systems. The pre-programmed DR strategies were triggered without a human in the loop. This paper describes the automated system architecture with detailed description of meter feedback in the DR signaling to maintain demand reduction at the government office building. The results showed that OpenADR infrastructure could be used for some products for the ancillary services market and DR strategies for heating ventilation and air conditioning and lighting provide fast enough response to participate in non-spinning reserve product in the ancillary services market.
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paper_title: Quantifying Changes in Building Electricity Use, With Application to Demand Response

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We present methods for analyzing commercial and industrial facility 15-min-interval electric load data. These methods allow building managers to better understand their facility's electricity consumption over time and to compare it to other buildings, helping them to “ask the right questions” to discover opportunities for demand response, energy efficiency, electricity waste elimination, and peak load management. We primarily focus on demand response. Methods discussed include graphical representations of electric load data, a regression-based electricity load model that uses a time-of-week indicator variable and a piecewise linear and continuous outdoor air temperature dependence and the definition of various parameters that characterize facility electricity loads and demand response behavior. In the future, these methods could be translated into easy-to-use tools for building managers.
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paper_title: Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings

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California is a leader in automating demand response (DR) to promote low-cost, consistent, and predictable electric grid management tools. Over 250 commercial and industrial facilities in California participate in fully-automated programs providing over 60 MW of peak DR savings. This paper presents a summary of Open Automated DR (OpenADR) implementation by each of the investor-owned utilities in California. It provides a summary of participation, DR strategies and incentives. Commercial buildings can reduce peak demand from 5 to 15percent with an average of 13percent. Industrial facilities shed much higher loads. For buildings with multi-year savings we evaluate their load variability and shed variability. We provide a summary of control strategies deployed, along with costs to install automation. We report on how the electric DR control strategies perform over many years of events. We benchmark the peak demand of this sample of buildings against their past baselines to understand the differences in building performance over the years. This is done with peak demand intensities and load factors. The paper also describes the importance of these data in helping to understand possible techniques to reach net zero energy using peak day dynamic control capabilities in commercial buildings. We present an example in which the electric load shape changed as a result of a lighting retrofit.
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paper_title: Methodology for validating technical tools to assess customer Demand Response: Application to a commercial customer

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The authors present a methodology, which is demonstrated with some applications to the commercial sector, in order to validate a Demand Response (DR) evaluation method previously developed and applied to a wide range of industrial and commercial segments, whose flexibility was evaluated by modeling. DR is playing a more and more important role in the framework of electricity systems management for the effective integration of other distributed energy resources. Consequently, customers must identify what they are using the energy for in order to use their flexible loads for management purposes. Modeling tools are used to predict the impact of flexibility on the behavior of customers, but this result needs to be validated since both customers and grid operators have to be confident in these flexibility predictions. An easy-to-use two-steps method to achieve this goal is presented in this paper.
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paper_title: Opportunities, Barriers and Actions for Industrial Demand Response in California

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LBNL-XXXXX Opportunities, Barriers and Actions for Industrial Demand Response in California Aimee T. McKane, Mary Ann Piette, David Faulkner, Girish Ghatikar, Anthony Radspieler Jr., Bunmi Adesola, Scott Murtishaw and Sila Kiliccote Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley, California 94720 January 2008 The work described in this report was coordinated by the Demand Response Research Center and funded by the California Energy Commission, Public Interest Energy Research Program under Work for Others Contract No. 500-03-026 and by the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231.
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paper_title: Load management for refrigeration systems: Potentials and barriers

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As a strategy to deal with the increasing intermittent input of renewable energy sources in Germany, the adaptation of power consumption is complementary to power-plant regulation, grid expansion and physical energy storage. One demand sector that promises strong returns for load management efforts is cooling and refrigeration. In these processes, thermal inertia provides a temporal buffer for shifting and adjusting the power consumption of cooling systems. We have conducted an empirical investigation to obtain a detailed and time-resolved bottom-up analysis of load management for refrigeration systems in the city of Mannheim, Germany. We have extrapolated our results to general conditions in Germany. Several barriers inhibit the rapid adoption of load management strategies for cooling systems, including informational barriers, strict compliance with legal cooling requirements, liability issues, lack of technical experience, an inadequate rate of return and organizational barriers. Small commercial applications of refrigeration in the food-retailing and cold storage in hotels and restaurants are particularly promising starting points for intelligent load management. When our results are applied to Germany, suitable sectors for load management have theoretical and achievable potential values of 4.2 and 2.8Â GW, respectively, amounting to about 4-6% of the maximum power demand in Germany.
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paper_title: Opportunities for Energy Efficiency and Demand Response in the California Cement Industry

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This study examines the characteristics of cement plants and their ability to shed or shift load to participate in demand response (DR). Relevant factors investigated include the various equipment and processes used to make cement, the operational limitations cement plants are subject to, and the quantities and sources of energy used in the cement-making process. Opportunities for energy efficiency improvements are also reviewed. The results suggest that cement plants are good candidates for DR participation. The cement industry consumes over 400 trillion Btu of energy annually in the United States, and consumes over 150 MW of electricity in California alone. The chemical reactions required to make cement occur only in the cement kiln, and intermediate products are routinely stored between processing stages without negative effects. Cement plants also operate continuously for months at a time between shutdowns, allowing flexibility in operational scheduling. In addition, several examples of cement plants altering their electricity consumption based on utility incentives are discussed. Further study is needed to determine the practical potential for automated demand response (Auto-DR) and to investigate the magnitude and shape of achievable sheds and shifts.
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paper_title: The industry demands better demand response

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The promise and hype surrounding the Smart Grid far exceeds its current capabilities. In no area is this truer than with Demand Response (DR) programs for the commercial building sector, which is responsible for 20 percent of energy demand and emissions in the United States. These concentrated pools of demand are a nightmare for ever-more strained utility grids, especially in major cities. As a result, utilities have created DR programs to provide financial incentives for building owners to reduce energy consumption during peak periods. This paper explores what these programs currently entail and why they are not working. It then presents and discusses an alternative that can break the major barriers to adoption of the Smart Grid - moving from a manual to the Optimized Demand Response system.
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paper_title: Agent-based electricity market simulation with demand response from commercial buildings

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With the development of power system deregulation and smart metering technologies, price-based demand response (DR) becomes an alternative solution to improving power system reliability and efficiency by adjusting the load profile. In this paper, we simulate an electricity market with DR from different types of commercial buildings by using agent-based modeling and simulation (ABMS) techniques. We focus on the consumption behavior of commercial buildings with different levels of DR penetration in different market structures. The results indicate that there is a noticeable impact from commercial buildings with price-responsive demand on the electricity market, and this impact differs with different scales of DR participation under different levels of market competitions.
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paper_title: A System Architecture for Autonomous Demand Side Load Management in Smart Buildings

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This paper presents a system architecture for load management in smart buildings which enables autonomous demand side load management in the smart grid. Being of a layered structure composed of three main modules for admission control, load balancing, and demand response management, this architecture can encapsulate the system functionality, assure the interoperability between various components, allow the integration of different energy sources, and ease maintenance and upgrading. Hence it is capable of handling autonomous energy consumption management for systems with heterogeneous dynamics in multiple time-scales and allows seamless integration of diverse techniques for online operation control, optimal scheduling, and dynamic pricing. The design of a home energy manager based on this architecture is illustrated and the simulation results with Matlab/Simulink confirm the viability and efficiency of the proposed framework.
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paper_title: A Survey on Cyber Security for Smart Grid Communications

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A smart grid is a new form of electricity network with high fidelity power-flow control, self-healing, and energy reliability and energy security using digital communications and control technology. To upgrade an existing power grid into a smart grid, it requires significant dependence on intelligent and secure communication infrastructures. It requires security frameworks for distributed communications, pervasive computing and sensing technologies in smart grid. However, as many of the communication technologies currently recommended to use by a smart grid is vulnerable in cyber security, it could lead to unreliable system operations, causing unnecessary expenditure, even consequential disaster to both utilities and consumers. In this paper, we summarize the cyber security requirements and the possible vulnerabilities in smart grid communications and survey the current solutions on cyber security for smart grid communications.
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paper_title: Cyber Security and Privacy Issues in Smart Grids

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Smart grid is a promising power delivery infrastructure integrated with communication and information technologies. Its bi-directional communication and electricity flow enable both utilities and customers to monitor, predict, and manage energy usage. It also advances energy and environmental sustainability through the integration of vast distributed energy resources. Deploying such a green electric system has enormous and far-reaching economic and social benefits. Nevertheless, increased interconnection and integration also introduce cyber-vulnerabilities into the grid. Failure to address these problems will hinder the modernization of the existing power system. In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented. Based on current literatures, several potential research fields are discussed at the end of this paper.
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paper_title: A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges

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A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.
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paper_title: Scalable and Robust Demand Response With Mixed-Integer Constraints

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A demand response (DR) problem is considered entailing a set of devices/subscribers, whose operating conditions are modeled using mixed-integer constraints. Device operational periods and power consumption levels are optimized in response to dynamic pricing information to balance user satisfaction and energy cost. Renewable energy resources and energy storage systems are also incorporated. Since DR becomes more effective as the number of participants grows, scalability is ensured through a parallel distributed algorithm, in which a DR coordinator and DR subscribers solve individual subproblems, guided by certain coordination signals. As the problem scales, the recovered solution becomes near-optimal. Robustness to random variations in electricity price and renewable generation is effected through robust optimization techniques. Real-time extension is also discussed. Numerical tests validate the proposed approach.
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paper_title: Smart Grid — The New and Improved Power Grid: A Survey

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The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.
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paper_title: A Reliability Perspective of the Smart Grid

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Increasing complexity of power grids, growing demand, and requirement for greater reliability, security and efficiency as well as environmental and energy sustainability concerns continue to highlight the need for a quantum leap in harnessing communication and information technologies. This leap toward a ?smarter? grid is widely referred to as ?smart grid.? A framework for cohesive integration of these technologies facilitates convergence of acutely needed standards, and implementation of necessary analytical capabilities. This paper critically reviews the reliability impacts of major smart grid resources such as renewables, demand response, and storage. We observe that an ideal mix of these resources leads to a flatter net demand that eventually accentuates reliability challenges further. A gridwide IT architectural framework is presented to meet these challenges while facilitating modern cybersecurity measures. This architecture supports a multitude of geographically and temporally coordinated hierarchical monitoring and control actions over time scales from milliseconds and up.
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paper_title: The Progressive Smart Grid System from Both Power and Communications Aspects

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The present electric power system structure has lasted for decades; it is still partially proprietary, energy-inefficient, physically and virtually (or cyber) insecure, as well as prone to power transmission congestion and consequent failures. Recent efforts in building a smart grid system have focused on addressing the problems of global warming effects, rising energy-hungry demands, and risks of peak loads. One of the major goals of the new system is to effectively regulate energy usage by utilizing the backbone of the prospectively deployed Automatic Meter Reading (AMR), Advanced Meter Infrastructure (AMI), and Demand Response (DR) programs via the advanced distribution automation and dynamic pricing models. The function of the power grid is no longer a system that only supplies energy to end users, but also allows consumers to contribute their clean energy back to the grid in the future. In the meantime, communications networks in the electric power infrastructure enact critical roles. Intelligent automation proposed in smart grid projects include the Supervisory Control And Data Acquisition/Energy Management Systems (SCADA/EMS) and Phasor Management Units (PMU) in transmission networks, as well as the AMR/AMI associated with field/neighborhood area networks (FAN/NAN) and home area networks (HAN) at the distribution and end-use levels. This article provides an overview of the essentials of the progressive smart grid paradigm and integration of different communications technologies for the legacy power system. Additionally, foreseeable issues and challenges in designing communications networks for the smart grid system are also rigorously deliberated in this paper.
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paper_title: Managing smart grid information in the cloud: opportunities, model, and applications

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Smart grid (SG), regarded as the next-generation electric grid, will use advanced power, communication, and information technologies to create an automated, intelligent, and widely distributed energy delivery network. In this article, we explore how cloud computing (CC), a next-generation computing paradigm, can be used for information management of the SG and present a novel SG information management paradigm, called Cloud Service-Based SG Information Management (CSSGIM). We analyze the benefits and opportunities from the perspectives of both the SG and CC domains. We further propose a model for CSSGIM and present four motivating applications.
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paper_title: A Survey of Communication Protocols for Automatic Meter Reading Applications

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Utility companies (electricity, gas, and water suppliers), governments, and researchers have been urging to deploy communication-based systems to read meters, known as automatic meter reading (AMR). An AMR system is envisaged to bring on benefits to customers, utilities, and governments. The advantages include reducing peak demand for energy, supporting the time-of-use concept for billing, enabling customers to make informed decisions, and reducing the cost of meter reading, to name a few. A key element in an AMR system is communications between meters and utility servers. Though several communication technologies have been proposed and implemented at a small scale, with the wide proliferation of wireless communication, it is the right time to critique the old proposals and explore new possibilities for the next generation AMR. We provide a comprehensive review of the AMR technologies proposed so far. Next, we present how future AMRs will benefit from third generation (3G) communication systems, the DLMS/COSEM (Data Language Messaging Specification/Companion Specification for Energy Metering) standard and Internet Protocol-based SIP (Session Initiation Protocol) signaling at the application level. The DLMS/COSEM standard provides a framework for meters to report application data (i.e. meter readings) to a utility server in a reliable manner. The SIP protocol is envisaged to be used as the signaling protocol between application entities running on meters and servers. The DLMS/COSEM standard and the SIP protocol are expected to provide an application level communication abstraction to achieve reliability and scalability. Finally, we identify the challenges at the application level that need to be tackled. The challenges include handling failure, gathering meter data under different time constraints (ranging from real-time to delay-tolerance), disseminating (i.e., unicasting, multicasting, broadcasting, and geocasting) control data to the meters, and achieving secure communication.
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paper_title: Communication services and data model for demand response

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Demand response (DR) is becoming an integral part of the modern power distribution systems. By partially reducing the consumption level or shifting it from peak hours to off-peak hours, DR can function as a tool for the utility to respond to shortage of supply for a short duration of time. It helps stabilize the electricity market, postpone capacity expansion projects for building new power generation units, and can potentially lead to mutual financial benefits for the utility as well as the customers. DR is one of the enablers of the Smart Grid paradigm as it promotes the interaction and responsiveness of the customers and changes the grid from a vertically integrated structure to one that is also affected by the behavior of the demand side. Essential to successful implementation of demand response is an efficient communication infrastructure capable of providing a secure two-way data exchange means between the utility and the customers. The focus of this paper is on the communication services and data models that are needed for realization of DR at the residential and commercial levels. The proposed model is platform independent, promoting interoperability as one of the key operational requirements of demand response implementation.
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paper_title: Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

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Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.
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paper_title: Integrated Voltage, Var Control and demand response in distribution systems

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This paper addresses the requirements for utilizing Voltage and Var Control for demand response, in an operating environment which includes Smart-Grid, Distribution Management Systems, Advanced Metering Infrastructure, Demand Response, and Distributed Energy Resources.
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paper_title: Dynamic Pricing? Not So Fast! A Residential Consumer Perspective

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With the installation of smart metering, will residential customers be moved to "dynamic" pricing? Some supporters of changing residential rate design from a fixed and stable rate structure believe customers should be required to take electric service with time-variant price signals. Not so fast, though! There are real implications associated with this strategy.
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paper_title: Demand Response and Distribution Grid Operations: Opportunities and Challenges

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Demand response (DR) is becoming an integral part of power system and market operations. Smart grid technologies will further increase the use of DR in everyday operations. Once the volume of the DR reaches a certain threshold, the effect of the DR events on the distribution and transmission system operations will be hard to ignore. This paper proposes changing the business process of DR scheduling and implementation by integrating DR with distribution grid topology. Study cases using OATI webDistribute show the potential DR effect on distribution grid operations and the distribution grid changing the effectiveness of the DR. These examples illustrate the need of integrating demand response with the distribution grid.
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paper_title: A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption

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The advent of advanced sensor technology and the breakthroughs in telecommunication open up several new possibilities for demand-side management. More recently, there has been greater interest from utilities as well as system operators in utilizing load as a system resource through the application of new technologies. With the wider application of demand-side management, there is an increasing emphasis on control of loads with minimum disruption. This paper develops a new framework for designing as well as assessing such an advanced direct load control program with the objective of minimizing end-user discomfort and is formulated as an optimization problem. With a fairly general setup for demand-side management, a simulation-based framework is developed for the stochastic optimization problem. Next, using this framework, insights into the effect of different parameters and constraints in the model on load control are developed.
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paper_title: QoE-driven power scheduling in smart grid: architecture, strategy, and methodology

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Smart grid is a new emerging technology which is able to intelligently control the power consumption via network. Therefore, the efficiency of the information exchange between the power suppliers (or control centers) and power customers is an important issue for smart grid. Moreover, the performance of the smart grid usually depends on the customer's satisfaction degree which belongs to the field of quality of experience. In this article, we propose a QoE-driven power scheduling in the context of smart grid from the perspectives of architecture, strategy, and methodology. Specifically, it takes into account the QoE requirement when designing the power allocation scheme. For obtaining the QoE requirement, we analyze the fluctuation of the power load and the impact of the transmission delay. In particular, the power allocation is formulated as an optimization problem that maximizes the social welfare of the system. Based on the given QoE model, an efficient power scheduling scheme is proposed by jointly considering the admission control and QoE expectation. Extensive simulation results indicate that the proposed scheme can efficiently allocate the power according to the dynamic QoE requirements in a practical smart grid system.
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paper_title: From Packet to Power Switching: Digital Direct Load Scheduling

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At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.
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paper_title: From Packet to Power Switching: Digital Direct Load Scheduling

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At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.
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paper_title: Load recognition for automated demand response in microgrids

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Microgrids are well-suited for electrification of remote off-grid areas. This paper sketches the concept of a plug-and-play microgrid with a minimum of configuration effort needed for setup. When the load of such an off-grid microgrid grows over the generation capacity and energy storage is not sufficient, demand has to be reduced to prevent a blackout. In order to decide which loads are inessential and can be shedded, automated load recognition on the basis of measured power consumption profiles is needed. Two promising approaches from the area of speech recognition, Dynamic Time Warping and Hidden Markov Models, are compared for this application. It is found that a key feature to achieve good recognition efficiency is a careful selection of the features extracted from the measured power data.
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paper_title: Distributed demand response and user adaptation in smart grids

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This paper proposes a distributed framework for demand response and user adaptation in smart grid networks. In particular, we borrow the concept of congestion pricing in Internet traffic control and show that pricing information is very useful to regulate user demand and hence balance network load. User preference is modeled as a willingness to pay parameter which can be seen as an indicator of differential quality of service. Both analysis and simulation results are presented to demonstrate the dynamics and convergence behavior of the algorithm.
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paper_title: An Optimal and Distributed Demand Response Strategy With Electric Vehicles in the Smart Grid

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In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or the energy stored in their plug-in electric vehicles. A distributed optimization algorithm based on the alternating direction method of multipliers is developed to solve the optimization problem, in which consumers need to report their aggregated loads only to the utility company, thus ensuring their privacy. Consumers can update their loads scheduling simultaneously and locally to speed up the optimization computing. Using numerical examples, we show that the demand curve is flattened after the optimization, even though there are uncertainties in the model, thus reducing the cost paid by the utility company. The distributed algorithms are also shown to reduce the users' daily bills.
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paper_title: A novel charging-time control method for numerous EVs based on a period weighted prescheduling for power supply and demand balancing

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To establish a sustainable energy supply system, renewable energy sources and low-carbon vehicles will have to become more widespread. However, it is often pointed out that the dissemination of these technologies will cause difficulties in balancing supply and demand in a power system, due to the fluctuation in the amounts of renewable energy generated and the fluctuation in the power demanded for numerous electric vehicles (EVs). The numerous EVs charging control seems to be difficult due to the difficulties in predicting EV trip behaviors, which vary depending on individual EV users. However, if we can control the total demand of numerous EVs, we can not only level their total load shape but also improve the supply-demand balancing capability of a power system to create new ancillary service businesses in the power market. This paper proposes a novel centralized EV-charging-control method to modify the total demand of EV charging by scheduling EV charging times. The proposed method is expected to be a powerful tool for a power aggregator (PAG), which will supply EV charging services to EV users and load leveling services to transmission system operators (TSOs) without inconveniencing EV users. The simulation showed that under the assumed EV trip patterns, the total charging demand of numerous EVs was successfully shaped so that the differences between watt-hours of the requirement and those of the controlled results were less than 4%.
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paper_title: Real-time vehicle-to-grid control algorithm under price uncertainty

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The vehicle-to-grid (V2G) system enables energy flow from the electric vehicles (EVs) to the grid. The distributed power of the EVs can either be sold to the grid or be used to provide frequency regulation service when V2G is implemented. A V2G control algorithm is necessary to decide whether the EV should be charged, discharged, or provide frequency regulation service in each hour. The V2G control problem is further complicated by the price uncertainty, where the electricity price is determined dynamically every hour. In this paper, we study the real-time V2G control problem under price uncertainty. We model the electricity price as a Markov chain with unknown transition probabilities and formulate the problem as a Markov decision process (MDP). This model features implicit estimation of the impact of future electricity prices and current control operation on long-term profits. The Q-learning algorithm is then used to adapt the control operation to the hourly available price in order to maximize the profit for the EV owner during the whole parking time. We evaluate our proposed V2G control algorithm using both the simulated price and the actual price from PJM in 2010. Simulation results show that our proposed algorithm can work effectively in the real electricity market and it is able to increase the profit significantly compared with the conventional EV charging scheme.
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paper_title: Distributed algorithms for control of demand response and distributed energy resources

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This paper proposes distributed algorithms for control and coordination of loads and distributed energy resources (DERs) in distribution networks. These algorithms are relevant for load curtailment control in demand response programs, and also for coordination of DERs for provision of ancillary services. Both the distributed load-curtailment and DER coordination problems can be cast as distributed resource allocation problems with constraints on resource capacity. We focus on linear iterative algorithms in which each resource j maintains a set of values that is updated to be a weighted linear combination of the resource's own previous set of values and the previous sets of values of its neighboring resources. This set of values can be used by each node to determine its own contribution to load curtailment or to resource request.
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paper_title: Demand-Response Management for Dependable Power Grids

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The national electricity markets in Europe, Asia, and the Americas are evolving towards decentralized structures mainly based on renewable energies, essentially rooted in political decisions to counter the worldwide climate change. The increase of production based on renewable energy implies drastically higher volatility in available electric power. The problem has two challenging facets, namely grid stability, and economy of energy production and consumption. Stability is a priority concern, because reliable energy is a prerequisite for economic use of energy, whether or not renewable. This asks for improved and better coordinated diagnostic and prediction techniques, as well as orchestrated demand-side mechanisms to counter critical grid or market situations. This typically does not save energy per se, it alters the consumption characteristics: peaks are shifted or certain load patterns are avoided. Demand-response mechanisms are event-driven reactions based on, for instance, frequency deviations or end user online pricing. With demand-response, consumption is temporarily reduced or increased, possibly even with reducing the respective customer service levels (e.g. indoor climate). The incentive for customers to participate in such programs is usually of monetary nature. This chapter explains the different types of demand-response, and discusses established and emerging technologies, focusing on mechanisms that operate decentralized, and without collecting information centrally. We compare a set of approaches that take up and combine ideas from communication protocol design, taking a flaw in the current German grid regulations as our prime motivation.
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paper_title: Potential-Function Based Control of a Microgrid in Islanded and Grid-Connected Modes

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This paper introduces the potential-function based method for secondary (as well as tertiary) control of a microgrid, in both islanded and grid-connected modes. A potential function is defined for each controllable unit of the microgrid such that the minimum of the potential function corresponds to the control goal. The dynamic set points are updated, using communication within the microgrid. The proposed potential function method is applied for the secondary voltage control of two microgrids with single and multiple feeders. Both islanded and grid-connected modes are investigated. The studies are conducted in the time-domain, using the PSCAD/EMTDC software environment. The study results demonstrate feasibility of the proposed potential function method and viability of the secondary voltage control method for a microgrid.
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paper_title: Decentralized Demand-Side Contribution to Primary Frequency Control

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Frequency in large power systems is usually controlled by adjusting the production of generating units in response to changes in the load. As the amount of intermittent renewable generation increases and the proportion of flexible conventional generating units decreases, a contribution from the demand side to primary frequency control becomes technically and economically desirable. One of the reasons why this has not been done was the perceived difficulties in dealing with many small loads rather than a limited number of generating units. In particular, the cost and complexity associated with two-way communications between many loads and the control center appeared to be insurmountable obstacles. This paper argues that this two-way communication is not essential and that the demand can respond to the frequency error in a manner similar to the generators. Simulation results show that, using this approach, the demand side can make a significant and reliable contribution to primary frequency response while preserving the benefits that consumers derive from their supply of electric energy.
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paper_title: Demand response with functional buildings using simplified process models

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Smart Grids ideally interconnect intelligent grid members. One big share of grid presence is with buildings. Flexible and grid-friendly buildings would improve grid management and are an important contribution to the integration of renewable energy sources. Classical buildings, however, are passive and not cooperative. This article describes how electro-thermal processes in buildings can be used for demand response and how such intelligent behavior can be enabled via communication technology. Experiments and simulations on typical mid-European buildings were done to estimate the potential time constants.
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paper_title: Design Considerations of a Centralized Load Controller Using Thermostatically Controlled Appliances for Continuous Regulation Reserves

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This paper presents design considerations for a centralized load controller to control thermostatically controlled appliances (TCAs) for continuous regulation reserves (CRRs). The controller logics for setting up the baseline load, generating priority lists, issuing dispatch commands, and tuning the simplified forecaster model using measurement data are described. To study the impacts of different control parameter settings on control performance and device lifetimes, a system consisting of 1000 heating, ventilating, and air-conditioning (HVAC) units in their heating modes is modeled to provide a CRR 24 hours a day. Four cases are modeled to evaluate the impact of forecasting errors, minimum HVAC turn-off times, response delays, and consumer overrides. The results demonstrate that a centralized TCA load controller can provide robust, good quality CRRs with reduced communication needs for the two-way communication network and inexpensive load control devices. Most importantly, because the controller precisely controls the aggregated HVAC load shapes while maintaining load diversity, the controllable and measurable load services that it provides can be used for many other demand response applications, such as peak shaving, load shifting, and arbitrage.
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paper_title: A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids

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This paper proposes a distributed framework for demand response and user adaptation in smart grid networks. In particular, we borrow the concept of congestion pricing in Internet traffic control and show that pricing information is very useful to regulate user demand and hence balance network load. User preference is modeled as a willingness to pay parameter which can be seen as an indicator of differential quality of service. Both analysis and simulation results are presented to demonstrate the dynamics and convergence behavior of the algorithm. Based on this algorithm, we then propose a novel charging method for plug-in hybrid electric vehicles (PHEVs) in a smart grid, where users or PHEVs can adapt their charging rates according to their preferences. Simulation results are presented to demonstrate the dynamic behavior of the charging algorithm and impact of different parameters on system performance.
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paper_title: Active Load Control in Islanded Microgrids Based on the Grid Voltage

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In the islanded operating condition, the microgrid has to maintain the power balance independently of a main grid. Because of the specific characteristics of the microgrid, such as the resistive lines and the high degree of power-electronically interfaced generators, new power control methods for the generators have been introduced. For the active power control in this paper, a variant of the conventional droop P/f control strategy is used, namely the voltage-droop controller. However, because of the small size of the microgrid and the high share of renewables with an intermittent character, new means of flexibility in power balancing are required to ensure stable operation. Therefore, a novel active load control strategy is presented in this paper. The aim is to render a proof of concept for this control strategy in an islanded microgrid. The active load control is triggered by the microgrid voltage level. The latter is enabled by using the voltage-droop control strategy and its specific properties. It is concluded that the combination of the voltage-droop control strategy with the presented demand dispatch allows reliable power supply without interunit communication for the primary control, leads to a more efficient usage of the renewable energy and can even lead to an increased share of renewables in the islanded microgrid.
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paper_title: Influence of variable supply and load flexibility on Demand-Side Management

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Demand-Side Management (DSM) refers to the shaping of the electrical load to improve the fit with the supply side [1]. One way to achieve this is to confront the user with a dynamic financial incentive to influence the demand. In previous work (see [2]) a crude reactive pricing algorithm has been shown to shave the peak of the aggregated load by over 25%. In this paper this approach is extended and evaluated in more detail. The algorithm is extended to cover scenarios where the supply is no longer fixed; its performance is investigated under varying conditions such as the percentage of the flexible load as well as the extent of the load's flexibility. The expected benefit of applying the algorithm to real world scenarios is predicted, allowing for an informed decision on whether or not to employ it in specific situations and under which pricing conditions.
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paper_title: Simulating integrated volt/var control and distributed demand response using GridSpice

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This paper proposes VVCDDR, an integrated volt/var control scheme which also uses distributed demand response capacity on distribution networks to improve the reliability and efficiency of the distribution network. The paper uses a new simulation platform, GridSpice, to show that demand response resources can be used to maintain a flat and stable voltage profile over the feeder without increasing the allocation of voltage regulators and capacitor banks. The improved voltage profile allows for a safe reduction of voltage while ensuring that all loads remain within the standard acceptable voltage range. Previous studies have shown that a reduction of the load voltage translates into lower power consumption, a technique known as conservation voltage reduction (CVR). The simulation platform, GridSpice, built on top of Gridlab-D [11], is the first distribution simulator to consider volt/var control, demand response, and distribution automation in a single simulation.
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paper_title: Residential Demand Response model and impact on voltage profile and losses of an electric distribution network

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This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).
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paper_title: Demand side management program evaluation based on industrial and commercial field data

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Demand Response is increasingly viewed as an important tool for use by the electric utility industry in meeting the growing demand for electricity. There are two basic categories of demand response options: time varying retail tariffs and incentive Demand Response Programs. Electricity Saudi Company (ESC) is applying the time varying retail tariffs program, which is not suitable according to the studied load curves captured from the industrial and commercial sectors. Different statistical studies on daily load curves for consumers connected to 22kV lines are classified. The load curve criteria used for classification is based on peak ratio and night ratio. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the King Saudi Arabia (KSA)) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas with larger commercial and industrial customers. The study proved that the suitable Demand Response for the ESC is the incentive program.
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paper_title: Customer behavior based demand response model

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An important benefit of demand response (DR) is avoided need to build power plants to serve heightened demand that occurs in just a few hours per year. There are two basic categories of DR options: price-based and incentive-based DR programs. In this paper, both categories of DR measures are modeled based on the demand-price elasticity concept. It has been shown that customers' reaction against implementing price-based or incentive-based DR programs are not similar, so that incentive-based programs have key impact on customer habit formation in response to DR programs. An improved DR model is developed which considers the customer's behavior. The proposed model extinguishes between customers' behavior with respect to electricity price change and his/her behavior against variation of incentive. The performance of the model has been justified by implementation on the IEEE reliability test system.
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paper_title: Impact of demand response contracts on load forecasting in a smart grid environment

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Load forecasting is highly important for power system operation and planning. Demand response, as a valuable feature in smart grid, is growing dramatically as an effective demand management method. However, traditional load forecasting tools have limitations to reflect demand response customer behaviors into load predictions. The energy consumption by demand response customers is mostly guided by their signed contracts. Therefore, existing demand response contracts are reviewed in this study for both wholesale and retail markets. An illustrative example is provided to explore the impact of these contracts on load forecasting. A concept of proactive load forecasting considering contract types is then proposed and discussed for forecasting loads in a smart grid environment.
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paper_title: Behavior Modification

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This article describes how retail electricity demand can be made price-responsive through either dynamic, time-based retail pricing or DR programs offered by utilities and/or regional ISOs. PRD can provide the crucial link between wholesale and retail electricity markets that is missing under traditional fixed retail rates. Parties generally agree on the need for greater PRD to improve the efficiency of wholesale power markets. There is no mechanical formula for determining how much consumers will alter their usage patterns for any given event. Price response varies considerably across customers and it tends to be greater for customers with relatively high usage levels. Consumers can choose to save money by reducing consumption during periods of high prices or "buy through" at prices that reflect wholesale market conditions.
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paper_title: Lessons learned from smart grid enabled pricing programs

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Dynamic pricing is often considered an essential part of demand response programs, particularly when considering the advent of new consumer-facing technologies, which will eventually reshape the relationship between utility and consumer. This paper presents and analyzes case studies of several dynamic pricing programs, including different proposed rates, enabling technologies and incentives. Program successes are evaluated based on a combination of peak load reduction, customer bill impacts and customer satisfaction. An analysis of lessons learned is provided on how various factors can affect the success, scalability and applicability of smart grid demand response programs.
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paper_title: Residential response to critical-peak pricing of electricity: California evidence

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This paper analyzes data from 483 households that took part in a critical-peak pricing (CPP) experiment between July and September 2004. Using a regression-based approach to quantify hourly baseline electric loads that would have occurred absent CPP events, we show a statistically significant average participant response in each hour. Average peak response estimates are provided for each of twelve experimental strata, by climate zone and building type. Results show that larger users respond more in both absolute and percentage terms, and customers in the coolest climate zone respond most as a percentage of their baseline load. Finally, an analysis involving the two different levels of critical-peak prices – $0.50/kWh and $0.68/kWh – indicates that households did not respond more to the higher CPP rate.
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paper_title: Modeling and prioritizing demand response programs in power markets

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Abstract One of the responsibilities of power market regulator is setting rules for selecting and prioritizing demand response (DR) programs. There are many different alternatives of DR programs for improving load profile characteristics and achieving customers’ satisfaction. Regulator should find the optimal solution which reflects the perspectives of each DR stakeholder. Multi Attribute Decision Making (MADM) is a proper method for handling such optimization problems. In this paper, an extended responsive load economic model is developed. The model is based on price elasticity and customer benefit function. Prioritizing of DR programs can be realized by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Considerations of ISO/utility/customer regarding the weighting of attributes are encountered by entropy method. An Analytical Hierarchy Process (AHP) is used for selecting the most effective DR program. Numerical studies are conducted on the load curve of the Iranian power grid in 2007.
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paper_title: The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review

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Peak demand for electricity in North America is expected to grow, challenging electrical utilities to supply this demand in a cost-effective, reliable manner. Therefore, there is growing interest in strategies to reduce peak demand by eliminating electricity use, or shifting it to non-peak times. This strategy is commonly called "demand response". In households, common strategies are time-varying pricing, which charge more for energy use on peak, or direct load control, which allows utilities to curtail certain loads during high demand periods. We reviewed recent North American studies of these strategies. The data suggest that the most effective strategy is a critical peak price (CPP) program with enabling technology to automatically curtail loads on event days. There is little evidence that this causes substantial hardship for occupants, particularly if they have input into which loads are controlled and how, and have an override option. In such cases, a peak load reduction of at least 30% is a reasonable expectation. It might be possible to attain such load reductions without enabling technology by focusing on household types more likely to respond, and providing them with excellent support. A simple time-of-use (TOU) program can only expect to realise on-peak reductions of 5%.
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paper_title: Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

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In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
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paper_title: Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

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Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.
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paper_title: Real-time pricing demand response in operations

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Dynamic pricing schemes have been implemented in commercial and industrial application settings, and recently they are getting attention for application to residential customers. Time-of-use and critical-peak-pricing rates are in place in various regions and are being piloted in many more. These programs are proving themselves useful for balancing energy during peak periods; however, real-time (5 minute) pricing signals combined with automation in end-use systems have the potential to deliver even more benefits to operators and consumers. Besides system peak shaving, a real-time pricing system can contribute demand response based on the locational marginal price of electricity, reduce load in response to a generator outage, and respond to local distribution system capacity limiting situations. The US Department of Energy (DOE) is teaming with a mid-west electricity service provider to run a distribution feeder-based retail electricity market that negotiates with residential automation equipment and clears every 5 minutes, thus providing a signal for lowering or raising electric consumption based on operational objectives of economic efficiency and reliability. This paper outlines the capability of the real-time pricing system and the operational scenarios being tested as the system is rolled-out starting in the first half of 2012.
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paper_title: Demand response in electrical energy supply: An optimal real time pricing approach

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In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities.
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paper_title: The Value of Dynamic Pricing in Mass Markets

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Abstract The simpler forms of dynamic pricing, in which prices vary only during extreme supply conditions, may capture many of the economic benefits of real-time pricing, and may be suitable for wide-scale deployment to mass-market consumers, for whom dynamic pricing options have largely been ignored.
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paper_title: Dynamic Pricing? Not So Fast! A Residential Consumer Perspective

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With the installation of smart metering, will residential customers be moved to "dynamic" pricing? Some supporters of changing residential rate design from a fixed and stable rate structure believe customers should be required to take electric service with time-variant price signals. Not so fast, though! There are real implications associated with this strategy.
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paper_title: Coupon incentive-based demand response (CIDR) in smart grid

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A new type of demand response (DR) program referred to as coupon incentive-based demand response (CIDR) is presented as an alternative to residential consumer demand response programs. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer residential consumers coupon incentives to reduce power consumption in a given period of time, offsetting potential losses due to wholesale electricity price spikes. In contrast with real-time pricing or peak load pricing, CIDR program maintains simple flat retail rate structure on consumer side while providing effective voluntary-based incentives for DR. An iterative procedure is designed to realize the real-time interaction between the independent system operator, the LSEs and consumers. CIDR can increase the profit of the LSEs and achieve almost the same social welfare as under the real-time pricing scheme. CIDR is compatible with current flat retail rate pricing scheme so the implementation is straightforward. A numerical experiment demonstrates the potential benefits of CIDR programs.
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paper_title: The integration of Price Responsive Demand into Regional Transmission Organization (RTO) wholesale power markets and system operations

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A number of states and utilities are pursuing demand response based on dynamic and time-differentiated retail prices and utility investments in Advanced Metering Infrastructure (AMI), often as part of Smart Grid initiatives. These developments could produce large amounts of Price Responsive Demand, demand that predictably responds to changes in wholesale prices. Price Responsive Demand could provide significant reliability and economic benefits. However, existing RTO tariffs present potential barriers to the development of Price Responsive Demand. Effectively integrating Price Responsive Demand into RTO markets and operations will require changes in demand forecasting, scarcity pricing reform, synchronization of scarcity pricing with capacity markets, tracking voluntary hedging by price responsive loads, and a non-discriminatory approach in curtailments in capacity emergencies. The article describes changes in RTO policies and systems needed incorporate Price Responsive Demand.
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paper_title: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

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Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
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paper_title: A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program

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Spinning Reserve is one of the ancillary services which is essential to satisfy system security constraints when the power system faces with a contingency. In this paper, Day Ahead Demand Response Program as one of the incentive-based Demand Response programs is implemented as a source of spinning reserve. In this regard, certain number of demands are selected according to a sensitivity analysis, and simulated as virtual generation units. The reserve market is cleared for Spinning Reserve allocation considering a probabilistic technique. A comparison is performed between the absence and existence of Day Ahead Demand Response Program from both economical and reliability viewpoints. Numerical studies based on IEEE 57 bus test system is conducted for evaluation of the proposed method.
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paper_title: Optimal Scheduling of Demand Response Events for Electric Utilities

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Electric utilities have been investigating methods to reduce peak power demand. Demand response (DR) is one such method which intends to reduce peak electricity demand. DR programs typically have limits on the number and timing of events that may be triggered for a selected group of customers. This paper presents a methodology for optimizing the scheduling of DR events for various DR programs. The proposed optimization mechanism establishes a policy that triggers DR events according to the criteria that govern the cost to the utility and based on probability distributions of exogenous information that is accessible to utilities a priori, for decision making. The policy determines a dynamic threshold for triggering events that optimizes the expected savings over the planning horizon. Case studies using real utility data show that our solutions are better than current industrial practices, and close to ex-post optimality.
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paper_title: Price it Right: Household Response to a Time-of-Use Electricity Pricing Experiment in Auckland, New Zealand

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III ACKNOWLEDGEMENTS IV LIST OF TABLES IVII LIST OF FIGURES IVIII LIST OF EQUATIONS IX LIST OF PROBLEMS X
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paper_title: Smart grid technologies and applications for the industrial sector

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Abstract Smart grids have become a topic of intensive research, development, and deployment across the world over the last few years. The engagement of consumer sectors—residential, commercial, and industrial—is widely acknowledged as crucial for the projected benefits of smart grids to be realized. Although the industrial sector has traditionally been involved in managing power use with what today would be considered smart grid technologies, these applications have mostly been one-of-a-kind, requiring substantial customization. Our objective in this article is to motivate greater interest in smart grid applications in industry. We provide an overview of smart grids and of electricity use in the industrial sector. Several smart grid technologies are outlined, and automated demand response is discussed in some detail. Case studies from aluminum processing, cement manufacturing, food processing, industrial cooling, and utility plants are reviewed. Future directions in interoperable standards, advances in automated demand response, energy use optimization, and more dynamic markets are discussed.
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paper_title: Flexible demand response programs modeling in competitive electricity markets

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In recent years, extensive researches have been conducted on implementation of demand response programs (DRPs), aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, DRPs are divided into two main categories namely, incentive-based programs (IBPs) and time-based rate programs (TBRPs). Mathematical modeling of these programs helps regulators and market policy makers to evaluate the impact of price responsive loads on the market and system operational conditions. In this paper, an economic model of price/incentive responsive loads is derived based on the concept of flexible price elasticity of demand and customer benefit function. The mathematical model for flexible price elasticity of demand is presented to calculate each of the demand response (DR) program’s elasticity based on the electricity price before and after implementing DRPs. In the proposed model, a demand ratio parameter has been introduced to determine the appropriate values of incentive and penalty in IBPs according to the level of demand. Furthermore, the importance of determining optimum participation level of customers in different DRPs has been investigated. The proposed model together with the strategy success index (SSI) has been applied to provide an opportunity for major players of the market, i.e. independent system operator (ISO), utilities and customers to select their favorite programs that satisfy their desires. In order to evaluate the performance of the proposed model, numerical studies are conducted on the Iranian interconnected network load profile on the annual peak day of the year 2007.
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paper_title: Residential Customer Response to Real-time Pricing: The Anaheim Critical Peak Pricing Experiment

paper_content:

This paper analyzes the results of a critical peak pricing (CPP) experiment involving 123 residential customers of the City of Anaheim Public Utilities (APU) over the period June 1, 2005 to October 14, 2005. Using a nonparametric condition mean estimation framework that allows for customer-specific fixed effects and day-of-sample fixed effects, I find that customers in the treatment group consumed an average of 12 percent less electricity during the peak hours of the day on CPP days than customers in the control group. There is also evidence that this reduction in consumption for customers in the treatment group relative to customers in the control group is larger on higher temperature CPP days. The impact of CPP events is confined to the peak periods of CPP days. Mean electricity consumption by customers in the treatment group is not significantly different from that of customers in the control group during the peak or off-peak periods of the day before or day after a CPP event. Much of the estimated consumption reduction of treatment consumers relative to control group consumers during peak periods of CPP days is due to reductions from a higher level of consumption by treatment group customers in non-CPP days. The consumption reductions paid rebates during CPP days are almost 7 times the reduction in consumption due to CPP events predicted by the treatment effects estimate, which provides strong evidence of an overly generous method for setting the reference level for peak period consumption relative to which customers are issued refunds during CPP days. The paper closes with a discussion of the challenges associated with implementing a CPP rate with a rebate mechanism as the default rate for residential customers.
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paper_title: Communication limitations in iterative real time pricing for power systems

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Iterative, or negotiated, pricing mechanisms have been of interest for decades as a systematic means of operating electricity markets, and have more recently been proposed as a way of dealing with the increased diversity of responsive market participants brought about by smart grid technologies and the increasing share of intermittent energy sources. One possibility is for the market coordinator to communicate iteratively with market participants to determine energy prices, on a receding horizon basis. Until now, little work has been carried out on the practicalities of implementing such pricing mechanisms, and yet because a market outcome must be cleared in real time, adequate communication speed and reliability is essential. The authors present a model of lossy and delayed communication over a network for such a negotiated pricing mechanism, derived from practical consideration of message timing, collection strategies and security. The convergence of the algorithm to an optimal dispatch is shown to be quite robust to frequent communication failures and delays. Results are demonstrated for price negotiation over a 20-step time horizon on a densely-populated IEEE 39 bus network.
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paper_title: A day-ahead electricity pricing model based on smart metering and demand-side management

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Several factors support more deployment of real-time pricing (RTP); including recent developments in the area of smart metering, regulators interest in promoting demand response programs and well-organized electricity markets. This paper first reviews time-based electricity pricing and the main barriers and issues to fully unleash benefits of RTP programs. Then, a day-ahead real-time pricing (DA-RTP) model is proposed, which addresses some of these issues. The proposed model can assist a retail energy provider and/or a distribution company (DISCO) to offer optimal DA hourly prices using smart metering. The real-time prices are determined through an optimization problem which seeks to maximize the electricity provider's profit, while considering consumers' benefit, minimum daily energy consumption, consumer response to posted electricity prices, and distribution network constraints. The numerical results associated with Ontario electricity tariffs indicate that instead of directly posting DA market prices to consumers, it would be better to calculate optimal prices which would yield higher benefit both for the energy provider and consumers.
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paper_title: Optimal energy consumption scheduling using mechanism design for the future smart grid

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In the future smart grid, both users and power companies can benefit from real-time interactions and pricing methods which can reflect the fluctuations of the wholesale price into the demand side. In addition, smart pricing can be used to seek social benefits and to achieve social objectives. However, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. That is, users may not be willing to reveal their local information unless there is an incentive for them to do so. In this paper, we propose an efficient pricing algorithm to tackle this problem. The benefit that each user obtains from each appliance can be modeled in form of a utility function, a concept from microeconomics. We propose a Vickrey-Clarke-Groves (VCG) based mechanism for our problem formulation aiming to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's payment for electricity. The payment of each user is structured in such a way that it is in each user's self interest to reveal its local information truthfully. Finally, we present simulation results to show that both the energy provider and the individual users can benefit from the proposed pricing algorithm.
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paper_title: The role of incentive based Demand Response programs in smart grid

paper_content:

Smart Grid promises the highest efficiency in the history of electrical energy and one of its key components is Demand Response (DR). Incentive based programs however would have a different path in smart grid and their usage would be different in this new environment. In this paper, incentive based DR programs are briefly introduced, smart grid is presented and the influence of smart grid on incentive based DR programs is described considering the effect of Real Time Pricing (RTP). Finally, an existing model has been upgraded in order to analyze incentive based programs in smart grid. Eventually the influence of a typical demand bidding program is simulated on the load curve of a day in smart grid in the presence of RTP program.
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paper_title: Demand Response Scheduling by Stochastic SCUC

paper_content:

Considerable developments in the real-time telemetry of demand-side systems allow independent system operators (ISOs) to use reserves provided by demand response (DR) in ancillary service markets. Currently, many ISOs have designed programs to utilize the reserve provided by DR in electricity markets. This paper presents a stochastic model to schedule reserves provided by DR in the wholesale electricity markets. Demand-side reserve is supplied by demand response providers (DRPs), which have the responsibility of aggregating and managing customer responses. A mixed-integer representation of reserve provided by DRPs and its associated cost function are used in the proposed stochastic model. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer programming (SMIP) problem. The first-stage involves network-constrained unit commitment in the base case and the second-stage investigates security assurance in system scenarios. The proposed model would schedule reserves provided by DRPs and determine commitment states of generating units and their scheduled energy and spinning reserves in the scheduling horizon. The proposed approach is applied to two test systems to illustrate the benefits of implementing demand-side reserve in electricity markets.
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paper_title: Demand-Side Bidding Agents: Modeling and Simulation

paper_content:

Problems such as price volatility have been observed in electric power markets. Demand-side participation is frequently offered as a potential solution by promising to increase market efficiency when hockey-stick-type offer curves are present. However, the individual end-consumer will surely value electricity differently, which makes demand-side participation difficult as a group and at a bus. In this paper demand is categorized into two groups: one that highly values reliability and one that does not. The two types are modeled separately and a new optimal bidding function is developed and tested based on this model.
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paper_title: A Direct Load Control Model for Virtual Power Plant Management

paper_content:

In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability.
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paper_title: Residential implementation of critical-peak pricing of electricity

paper_content:

This paper investigates how critical-peak pricing (CPP)affects households with different usage and income levels, with the goalof informing policy makers who are considering the implementation of CPPtariffs in the residential sector. Using a subset of data from theCalifornia Statewide Pricing Pilot of 2003-2004, average load changeduring summer events, annual percent bill change, and post-experimentsatisfaction ratings are calculated across six customer segments,categorized by historical usage and income levels. Findings show thathigh-use customers respond significantly more in kW reduction than dolow-use customers, while low-use customers save significantly more inpercentage reduction of annual electricity bills than do high-usecustomers results that challenge the strategy of targeting only high-usecustomers for CPP tariffs. Across income levels, average load and billchanges were statistically indistinguishable, as were satisfaction ratesresults that are compatible with a strategy of full-scale implementationof CPP rates in the residential sector. Finally, the high-use customersearning less than $50,000 annually were the most likely of the groups tosee bill increases about 5 percent saw bill increases of 10 percent ormore suggesting that any residential CPP implementation might considertargeting this customer group for increased energy efficiencyefforts.
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paper_title: Demand participation in the restructured Electric Reliability Council of Texas market

paper_content:

Does an electricity market which has been restructured to foster competition provide greater opportunities for demand response than a traditional regulated utility industry? The experiences of the restructured Electric Reliability Council of Texas (ERCOT) market over the past eight years provide some hope that it is possible to design a competitive market which will properly value and accommodate demand response. While the overall level of demand response in ERCOT is below the levels enjoyed prior to restructuring, there have nonetheless been some promising advances, including the integration of demand-side resources into competitive markets for ancillary services. ERCOT's experiences demonstrate that the degree of demand participation in a restructured market is highly sensitive to the market design. But even in a market which has been deregulated to a large degree, regulatory intervention and special demand-side programs may be needed in order to bolster demand response.
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paper_title: Present status and future trends in enabling demand response programs

paper_content:

This paper addresses implementation of Demand Response (DR) programs in competitive electricity markets. An overview of present status of the application of DR programs in major electricity markets is provided. In this paper, An objective-wised classification of DR measures is proposed which is rooted in practical DR experiences. Market opportunities and associated barriers are investigated. Further, enabling technologies for implementing DR programs are discussed. Finally, the role of smart grid in enabling DR is highlighted.
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paper_title: A summary of demand response in electricity markets

paper_content:

Abstract This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.
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paper_title: Direct Load Control (DLC) Considering Nodal Interrupted Energy Assessment Rate (NIEAR) in Restructured Power Systems

paper_content:

A direct load control (DLC) scheme of air conditioning loads (ACL) considering direct monetary compensation to ACL customers for the service interruption caused by the DLC program is proposed in this paper for restructured power systems. The nodal interrupted energy assessment rate (NIEAR), which is used as the bids from the ACL customers, is utilized to determine the direct monetary compensation to the ACL customers. The proposed scheme was investigated for the PoolCo electricity market. The optimal DLC scheme is determined based on the minimum system operating cost which is comprised of the system energy cost, the system spinning reserve cost and the compensation cost to the ACL customers. Dynamic programming (DP) was used to obtain the optimal DLC scheme. The IEEE reliability test system (RTS) was studied to illustrate the proposed DLC scheme.
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paper_title: Demand response in smart electricity grids equipped with renewable energy sources: A review

paper_content:

Dealing with Renewable Energy Resources (RERs) requires sophisticated planning and operation scheduling along with state of art technologies. Among many possible ways for handling RERs, Demand Response (DR) is investigated in the current review. Because of every other year modifications in DR definition and classification announced by Federal Energy Regulatory Commission (FERC), the latest DR definition and classification are scrutinized in the present work. Moreover, a complete benefit and cost assessment of DR is added in the paper. Measurement and evolution methods along with the effects of DR in electricity prices are discussed. Next comes DR literature review of the recent papers majorly published after 2008. Eventually, successful DR implementations, around the world, are analyzed.
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paper_title: Smart (in-home) power scheduling for demand response on the smart grid

paper_content:

This paper proposes a power scheduling-based communication protocol for in-home appliances connected over home area network and receiving real-time electricity prices via a smart meter. Specifically, a joint media access and appliance scheduling approach is developed to allow appliances to coordinate power usage so that total demand for the home is kept below a target value. Two types of appliances are considered: 1) “real-time” which consume power as they desire; and 2) “schedulable” which can be turned on at a later time. Simulation results indicate that for an appropriate target total power consumption, our scheme leads to a reduced peak demand for the home and produces a demand that is more level over time.
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paper_title: Stochastic Control for Smart Grid Users With Flexible Demand

paper_content:

In this paper, we study the optimal control problem for the demand-side of the smart grid under time-varying prices with general structures. We assume that users are equipped with smart appliances that allow delay in satisfying demands, and one central controller that makes energy usage decisions on when and how to satisfy the scheduled demands. We formulate a dynamic programming model for the control problem. The model deals with stochastic demand arrivals and schedules the demands based on their own allowable delays, which are specified by users. However, the dynamic programming model encounters the “curses of dimensionality” and some other difficulties, thus is hard to solve. We develop a decentralization-based heuristic first, and also propose an approximation approach based on Q-learning. Finally, we conduct numerical studies on a testing problem. The simulation results show that both the Q-learning and the decentralization based heuristic approaches work well, but they have their own advantages and disadvantages under different scenarios. Lastly, we conclude the paper with some discussions on future extension directions.
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paper_title: Coupon Incentive-Based Demand Response: Theory and Case Study

paper_content:

This paper presents the formulation and critical assessment of a novel type of demand response (DR) program targeting retail customers (such as small/medium size commercial, industrial, and residential customers) who are equipped with smart meters yet still face a flat rate. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer retail customers coupon incentives via near-real-time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping and/or price spikes. This scheme is referred to as coupon incentive-based demand response (CIDR). In contrast to the real-time pricing or peak load pricing DR programs, CIDR continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response. Theoretical analysis shows the benefits of the proposed scheme in terms of social welfare, consumer surplus, LSE profit, the robustness of the retail electricity rate, and readiness for implementation. The pros and cons are discussed in comparison with existing DR programs. Numerical illustration is performed based on realistic supply and demand data obtained from the Electric Reliability Council of Texas (ERCOT).
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paper_title: Demand Response Architecture: Integration into the Distribution Management System

paper_content:

Demand Response (DR) refers to actions taken by the utility to respond to a shortage of supply for a short duration of time in the future. DR is one of the enablers of the Smart Grid paradigm as it promotes interaction and responsiveness of the customers and changes the grid from a vertically integrated structure to one that is affected by the behavior of the demand side. In Principle, it is possible to perform DR at the substation level for the customers connected to the feeders downstream or at the demand response service provider (aggregator) for the customers under its territory. This would allow for an area based solution driven mostly by the financial aspects as well as terms and conditions of the mutual agreements between the individual customers and the utility. However, as the penetration of DR increases, incorporating the network model into the DR analysis algorithm becomes necessary. This ensures the proper performance of the DR process and achieves peripheral objectives in addition to achieving the target demand reduction. The added value to the DR algorithm by incorporating the model of the distribution network can only be realized if the engine is developed as an integrated function of the Distribution Management System (DMS) at the network control center level. This paper focuses on the demand response architecture implemented at the DMS level and discusses some practical considerations associated with this approach
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paper_title: An introduction to load management

paper_content:

Abstract Permanent availability of electricity is nowadays taken for granted, but grid reliability and sustainability is an everyday process of supply and demand balancing. In this document we provide a comprehensive study of the load management methods, techniques and programs theoretically described or practically used in developed and developing countries. Not only experience and actual situation, but also evaluation, future goals and challenges are described.
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paper_title: An innovative RTP-based residential power scheduling scheme for smart grids

paper_content:

This paper proposes a Real-Time Pricing (RTP)-based power scheduling scheme as demand response for residential power usage. In this scheme, the Energy Management Controller (EMC) in each home and the service provider form a Stackelberg game, in which the EMC who schedules appliances' operation plays the follower level game, and the provider who sets the real-time prices according to current power usage profile plays the leader level game. The sequential equilibrium is obtained through the information exchange between them. Simulation results indicate that our scheme can not only save money for consumers, but also reduce peak load and the variance between demand and supply, while avoiding the “rebound” peak problem.
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paper_title: Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility

paper_content:

Smart grids are capable of two-way communication between individual user devices and the electricity provider, enabling providers to create a control-feedback loop using time-dependent pricing. By charging users more in peak and less in off-peak hours, the provider can induce users to shift their consumption to off-peak periods, thus relieving stress on the power grid and the cost incurred from large peak loads. We formulate the electricity provider's cost minimization problem in setting these prices by considering consumers' device-specific scheduling flexibility and the provider's cost structure of purchasing electricity from an electricity generator. Consumers' willingness to shift their device usage is modeled probabilistically, with parameters that can be estimated from real data. We develop an algorithm for computing day-ahead prices, and another algorithm for estimating and refining user reaction to the prices. Together, these two algorithms allow the provider to dynamically adjust the offered prices based on user behavior. Numerical simulations with data from an Ontario electricity provider show that our pricing algorithm can significantly reduce the cost incurred by the provider.
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paper_title: Demand response implementation in a home area network: A conceptual hardware architecture

paper_content:

Demand response (DR) is an important demand-side resource that allows for lower electricity consumption when the system is under stress. This paper presents a DR framework that can be implemented within a home area network, as well as a conceptual hardware architecture for a Home Management System (HMS) and appliance interface units that enable in-home DR implementation. The proposed DR strategy allows for controlling energy-intensive loads taking into consideration both consumers' comfort and load priority. The HMS acts as the central monitoring and decision-making unit for all energy-intensive loads within a home. The appliance interface unit communicates with the HMS while capturing electric power consumption data and performing local load control. Standby electric power consumption of each element in the network is also discussed.
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paper_title: Piloting the Smart Grid

paper_content:

To address the likely impact of the smart grid on customers, utilities, and society as a whole, it may be necessary to conduct a pilot. When should a pilot be conducted and how should it be conducted? What validity criteria should the pilot satisfy? Here are issues to consider.
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paper_title: A demand-side response smart grid scheme to mitigate electrical peak demands and access renewable energy sources

paper_content:

Growing demands are causing increased pressure on the electricity infrastructure and perpetually escalated energy prices. Typically, the re are daily and seasonal demand fluctuations oscillating between excessive- peak an d equally excessive-low demands. Peak demands, at times, are causing congestions on the transmission and distribution network associated with compromised quality, risk o f outages and high-priced energy supply. Expensive-to-run power plants are usually operated for short periods of time to meet peak demands what makes their operation even more expensive. Low-demands, usually supplied by base-load power stations, could be driving the electrical capacity and network to be operated well below a sustainable economic feasibility. Spreading out the demand profile on a moderated level would achieve an improved utilization of the electrical infrastructure. This research presents a demand-side response scheme to be implemented at end-user’s premises contributing shi fting loads to the right time of the day targeting spreading out the demand profile and allowing utilization of renewable energy sources. The technology uses programmable internet relays controlling appliance switches to operate loads automatically. The paper presents simulations of the economic model corresponding to the above described scheme representing an incentive-based demand response. In the simulation the impact of th ese programs on load shape and peak load magnitudes, financial benefit to users as well as reduction of energy consumption are shown. The results demonstrated more moderated load profile at lesser peak load magnitude and reduced energy cost.
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paper_title: Demand side management—A simulation of household behavior under variable prices

paper_content:

Within the next years, consumer households will be increasingly equipped with smart metering and intelligent appliances. These technologies are the basis for households to better monitor electricity consumption and to actively control loads in private homes. Demand side management (DSM) can be adopted to private households. We present a simulation model that generates household load profiles under flat tariffs and simulates changes in these profiles when households are equipped with smart appliances and face time-based electricity prices.
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paper_title: An Event-Driven Demand Response Scheme for Power System Security Enhancement

paper_content:

Demand response has become a key feature of the future smart grid. In addition to having advanced communication and computing infrastructures, a successful demand response program must respond to the needs of a power system. In other words, the efficiency and security of a power system dictate the locations, amounts and speeds of the load reductions of a demand response program. In this paper, we propose an event-driven emergency demand response scheme to prevent a power system from experiencing voltage collapse. A technique to design such a scheme is presented. This technique is able to provide key setting parameters such as the amount of demand reductions at various locations to arm the demand response infrastructure. The validity of the proposed technique has been verified by using several test power systems.
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paper_title: A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption

paper_content:

The advent of advanced sensor technology and the breakthroughs in telecommunication open up several new possibilities for demand-side management. More recently, there has been greater interest from utilities as well as system operators in utilizing load as a system resource through the application of new technologies. With the wider application of demand-side management, there is an increasing emphasis on control of loads with minimum disruption. This paper develops a new framework for designing as well as assessing such an advanced direct load control program with the objective of minimizing end-user discomfort and is formulated as an optimization problem. With a fairly general setup for demand-side management, a simulation-based framework is developed for the stochastic optimization problem. Next, using this framework, insights into the effect of different parameters and constraints in the model on load control are developed.
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paper_title: Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration

paper_content:

A smart grid introduces new opportunities and challenges to electric power grids especially at the distribution level. Advanced metering infrastructure (AMI) and information portals enable customers to have access to real-time electricity pricing information, thus facilitating customer participation in demand response. The objective of this paper is to analyze the impact of time-of-use (TOU) electricity rates on customer behaviors in a residential community. Research findings indicate that the TOU rate can be properly designed to reduce the peak demand even when PHEVs are present. This result is insensitive to seasons, PHEV penetration levels and PHEV charging strategies. It is expected that this paper can give policy makers, electric utilities and other relevant stakeholders an insight into the impacts of various TOU pricing schemes on distribution load shapes in a smart grid with PHEV penetration.
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paper_title: Demand response model and its effects on voltage profile of a distribution system

paper_content:

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These Price Elasticity Matrices (PEMs) are utilized to calculate the level of demand response for a given consumer. DR thus obtained is applied to a real world distribution network considering a day-ahead real time pricing scenario to study the effects of demand reduction on system voltage. Results show considerable boost in system voltage that paves way for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).
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paper_title: Rethinking Real-Time Electricity Pricing

paper_content:

Most US consumers are charged a near-constant retail price for electricity, despite substantial hourly variation in the wholesale market price. This paper evaluates the first program to expose residential consumers to hourly real-time pricing (RTP). I find that enrolled households are statistically significantly price elastic and that consumers responded by conserving energy during peak hours, but remarkably did not increase average consumption during off-peak times. The program increased consumer surplus by $10 per household per year. While this is only one to two percent of electricity costs, it illustrates a potential additional benefit from investment in retail Smart Grid applications, including the advanced electricity meters required to observe a household’s hourly consumption.
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paper_title: Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

paper_content:

In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.
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paper_title: The Power of Dynamic Pricing

paper_content:

Using data from a generic California utility, it can be shown that it is feasible to develop dynamic pricing rates for all customer classes. These rates have the potential to reduce system peak demands from 1 to 9 percent.
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paper_title: QoE-driven power scheduling in smart grid: architecture, strategy, and methodology

paper_content:

Smart grid is a new emerging technology which is able to intelligently control the power consumption via network. Therefore, the efficiency of the information exchange between the power suppliers (or control centers) and power customers is an important issue for smart grid. Moreover, the performance of the smart grid usually depends on the customer's satisfaction degree which belongs to the field of quality of experience. In this article, we propose a QoE-driven power scheduling in the context of smart grid from the perspectives of architecture, strategy, and methodology. Specifically, it takes into account the QoE requirement when designing the power allocation scheme. For obtaining the QoE requirement, we analyze the fluctuation of the power load and the impact of the transmission delay. In particular, the power allocation is formulated as an optimization problem that maximizes the social welfare of the system. Based on the given QoE model, an efficient power scheduling scheme is proposed by jointly considering the admission control and QoE expectation. Extensive simulation results indicate that the proposed scheme can efficiently allocate the power according to the dynamic QoE requirements in a practical smart grid system.
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paper_title: Cooperative multi-residence demand response scheduling

paper_content:

This paper is concerned with scheduling of demand response among different residences and a utility company. The utility company has a cost function representing the cost of providing energy to end-users, and this cost can be varying across the scheduling horizon. Each end-user has a “must-run” load, and two types of adjustable loads. The first type must consume a specified total amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type of load has adjustable power consumption without a total energy requirement, but operation of the load at reduced power results in dissatisfaction of the end-user. The problem amounts to minimizing the total cost electricity plus the total user dissatisfaction (social welfare), subject to the individual load consumption constraints. The problem is convex and can be solved by a distributed subgradient method. The utility company and the end-users exchange Lagrange multipliers—interpreted as pricing signals—and hourly consumption data through the Advanced Metering Infrastructure, in order to converge to the optimal amount of electricity production and the optimal power consumption schedule.
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paper_title: From Packet to Power Switching: Digital Direct Load Scheduling

paper_content:

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.
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paper_title: Optimal Control Policies for Power Demand Scheduling in the Smart Grid

paper_content:

We study the problem of minimizing the long-term average power grid operational cost through power demand scheduling. A controller at the operator side receives consumer power demand requests with different power requirements, durations and time flexibilities for their satisfaction. Flexibility is modeled as a deadline by which a demand is to be activated. The cost is a convex function of total power consumption, which reflects the fact that each additional unit of power needed to serve demands is more expensive to provision, as demand load increases. We develop a stochastic model and introduce two online demand scheduling policies. In the first one, the Threshold Postponement (TP), the controller serves a new demand request immediately or postpones it to the end of its deadline, depending on current power consumption. In the second one, the Controlled Release (CR), a new request is activated immediately if power consumption is lower than a threshold, else it is queued. Queued demands are activated when deadlines expire or when consumption drops below the threshold. These policies admit an optimal control with switching curve threshold structure, which involves active and postponed demand. The CR policy is asymptotically optimal as deadlines increase, namely it achieves a lower bound on average cost, and the threshold depends only on active demand. Numerical results validate the benefit of our policies compared to the default one of serving demands upon arrival.
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paper_title: A Water-Filling Based Scheduling Algorithm for the Smart Grid

paper_content:

The processing and communication capabilities of the smart grid provide a solid foundation for enhancing its efficiency and reliability. These capabilities allow utility companies to adjust their offerings in a way that encourages consumers to reduce their peak hour consumption, resulting in a more efficient system. In this paper, we propose a method for scheduling a community's power consumption such that it becomes almost flat. Our methodology utilizes distributed schedulers that allocate time slots to soft loads probabilistically based on precalculated and predistributed demand forecast information. This approach requires no communication or coordination between scheduling nodes. Furthermore, the computation performed at each scheduling node is minimal. Obtaining a relatively constant consumption makes it possible to have a relatively constant billing rate and eliminates operational inefficiencies. We also analyze the fairness of our proposed approach, the effect of the possible errors in the demand forecast, and the participation incentives for consumers.
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paper_title: Power consumption scheduling for peak load reduction in smart grid homes

paper_content:

This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes, aiming at reducing the peak load in individual homes as well as in the system-wide power transmission network. Following the task model consist of actuation time, operation length, deadline, and a consumption profile, the scheduler copies or maps the profile according to the task type, which can be either preemptive or nonpreemptive. The proposed scheme expands the search space recursively to traverse all the feasible allocations for a task set. A pilot implementation of this scheduling method reduces the peak load by up to 23.1% for the given task set. The execution time greatly depends on the search space of a preemptive task, as its time complexity is estimated to be O (MNnp · (MM/2)Np), where M, Nnp, and Np are the number of time slots, preemptive tasks, and nonpreemptive tasks, respectively. However, it can not only be reduced almost to 2% but also made stable with a basic constraint processing mechanism which prunes a search branch when the partial peak value already exceeds the current best.
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paper_title: A summary of demand response in electricity markets

paper_content:

Abstract This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.
---
paper_title: Smart (in-home) power scheduling for demand response on the smart grid

paper_content:

This paper proposes a power scheduling-based communication protocol for in-home appliances connected over home area network and receiving real-time electricity prices via a smart meter. Specifically, a joint media access and appliance scheduling approach is developed to allow appliances to coordinate power usage so that total demand for the home is kept below a target value. Two types of appliances are considered: 1) “real-time” which consume power as they desire; and 2) “schedulable” which can be turned on at a later time. Simulation results indicate that for an appropriate target total power consumption, our scheme leads to a reduced peak demand for the home and produces a demand that is more level over time.
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paper_title: Residential task scheduling under dynamic pricing using the multiple knapsack method

paper_content:

A key component of the smart grid is the ability to enable dynamic residential pricing to incentivize the customer and the overall community to utilize energy more uniformly. However, the complications involved require that automated strategies be provided to the customer to achieve this goal. This paper presents a solution to the problem of optimally scheduling a set of residential appliances under day-ahead variable peak pricing in order to minimize the customer's energy bill (and also, simultaneously spread out energy usage). We map the problem to a well known problem in computer science - the multiple knapsack problem - which enables cheap and efficient solutions to the scheduling problem. Results show that this method is effective in meeting its goals.
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paper_title: Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid

paper_content:

In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system.
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paper_title: Demand-side load scheduling incentivized by dynamic energy prices

paper_content:

Demand response is an important part of the smart grid technologies. This is a particularly interesting problem with the availability of dynamic energy pricing models. Electricity consumers are encouraged to consume electricity more prudently in order to minimize their electric bill, which is in turn calculated based on dynamic energy prices. In this paper, task scheduling policies that help consumers minimize their electrical energy cost by setting the time of use (TOU) of energy in the facility. Moreover, the utility companies can reasonably expect that their customers reduce their consumption at critical times in response to higher energy prices during those times. These policies target two different scenarios: (i) scheduling with a TOU-dependent energy pricing function subject to a constraint on total power consumption; and (ii) scheduling with a TOU and total power consumption-dependent pricing function for electricity consumption. Exact solutions (based on Branch and Bound) are presented for these task scheduling problems. In addition, a rank-based heuristic and a force directed-based heuristic are presented to efficiently solve the aforesaid problems. The proposed heuristic solutions are demonstrated to have very high quality and competitive performance compared to the exact solutions. Moreover, ability of demand shaping utilizing the aforementioned pricing schemes is demonstrated by the simulation results.
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paper_title: Distributed Demand and Response Algorithm for Optimizing Social-Welfare in Smart Grid

paper_content:

This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart girds. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.
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paper_title: Convex Analysis and Nonlinear Optimization, Theory and Examples

paper_content:

Background * Inequality constraints * Fenchel duality * Convex analysis * Special cases * Nonsmooth optimization * The Karush-Kuhn-Tucker Theorem * Fixed points * Postscript: infinite versus finite dimensions * List of results and notation.
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paper_title: Distributed Demand and Response Algorithm for Optimizing Social-Welfare in Smart Grid

paper_content:

This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart girds. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.
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paper_title: Demand Response Management via Real-Time Electricity Price Control in Smart Grids

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This paper proposes a real-time pricing scheme that reduces the peak-to-average load ratio through demand response management in smart grid systems. The proposed scheme solves a two-stage optimization problem. On one hand, each user reacts to prices announced by the retailer and maximizes its payoff, which is the difference between its quality-of-usage and the payment to the retailer. On the other hand, the retailer designs the real-time prices in response to the forecasted user reactions to maximize its profit. In particular, each user computes its optimal energy consumption either in closed forms or through an efficient iterative algorithm as a function of the prices. At the retailer side, we develop a Simulated-Annealing-based Price Control (SAPC) algorithm to solve the non-convex price optimization problem. In terms of practical implementation, the users and the retailer interact with each other via a limited number of message exchanges to find the optimal prices. By doing so, the retailer can overcome the uncertainty of users' responses, and users can determine their energy usage based on the actual prices to be used. Our simulation results show that the proposed real-time pricing scheme can effectively shave the energy usage peaks, reduce the retailer's cost, and improve the payoffs of the users.
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paper_title: Intelligent control of vehicle to grid power

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Abstract Vehicle-to-grid (V2G) describes a system in which plug-in electric vehicles (PEV), which includes all electric vehicles and plug-in hybrid electric vehicles, utilize power by plugging into an electric power source and stored in rechargeable battery packs. PEVs significantly increase the load on the grid, much more than you would see in a typical household. The objective of this paper is to demonstrate the use of intelligent solutions for monitoring and controlling the electrical grid when connected to and recharging PEV batteries. In order to achieve this aim, the study examines the distribution of electricity in the power grid of a large-scale city so that PEVs can tap into the system using smart grid electricity. The electricity grid for the large-scale city is modelled, and it can be shown that the vehicle electrification can play a major role in helping to stabilize voltage and load. This developed grid model includes 33 buses, 10 generators, 3 reactors, 6 capacitors, and 33 consumer centers. In addition, the grid model proposes 10 parking servicing 150,000 vehicles per day. The smart grid model uses intelligent controllers. Two intelligent controllers including (i) fuzzy load controllers and (ii) fuzzy voltage controllers have been used in this study to optimize the grid stability of load and voltage. The results show that the smart grid model can respond to any load disturbance in less time, with increased efficiency and improved reliability compared to the traditional grid. In conclusion it is emphasized that smart grid electricity should contribute to PEVs accessing renewable energy. Although the V2G will play a major role in the future portfolio of vehicle technologies, but does not make much sense if the carbon content of the electricity generated by the grid will not be reduced. Thus, the recourse to renewable energy and other alternatives is crucial. The energy is stored in electrochemical power sources (such as battery, fuel cells, supercapacitors, photoelectrochemical) when generated and then delivered to the grid during peak demand times.
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paper_title: Domestic energy management methodology for optimizing efficiency in Smart Grids

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Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached.
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paper_title: Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions

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This study models the CO2 emissions from electric (EV) and plug-in hybrid electric vehicles (PHEV), and compares the results to published values for the CO2 emissions from conventional vehicles based on internal combustion engines (ICE). PHEVs require fewer batteries than EVs which can make them lighter and more efficient than EVs. PHEVs can also operate their onboard ICEs more efficiently than can conventional vehicles. From this, it was theorized that PHEVs may be able to emit less CO2 than both conventional vehicles and EVs given certain power generation mixes of varying CO2 intensities. Amongst the results it was shown that with a highly CO2 intensive power generation mix, such as in China, PHEVs had the potential to be responsible for fewer tank to wheel CO2 emissions over their entire range than both a similar electric and conventional vehicle. The results also showed that unless highly CO2 intensive countries pursue a major decarbonization of their power generation, they will not be able to fully take advantage of the ability of EVs and PHEVs to reduce the CO2 emissions from automotive transport.
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paper_title: Optimal Power Management of Residential Customers in the Smart Grid

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Recently intensive efforts have been made on the transformation of the world's largest physical system, the power grid, into a “smart grid” by incorporating extensive information and communication infrastructures. Key features in such a “smart grid” include high penetration of renewable and distributed energy sources, large-scale energy storage, market-based online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with real-time electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.
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paper_title: Analyzing the system effects of optimal demand response utilization for reserve procurement and peak clipping

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In this paper the effect of demand response (DR) resource utilization in the system operation is analyzed by using an optimization based simultaneous market clearing framework. Two distinct utilization patterns that DR can be involved are reserve supplying by Reserve Supplying Demand Response (RSDR) and providing actual curtailment for demand reduction or peak clipping by Peak Clipping Demand Response (PCDR). The provider of RSDR will sell its ability to tolerate probable curtailment and the PCDR will sell its real curtailment ability. These utilizations have different effects on system indices. Therefore the effect of different DR utilization patterns are analyzed and compared in this paper. IEEE RTS is selected as the test case for studying the effect of DR utilization patterns.
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paper_title: An optimal scheduling problem in distribution networks considering V2G

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This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.
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paper_title: Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

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Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.
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paper_title: Particle swarm optimization

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Particle swarm optimization (PSO) has undergone many changes since its intro- duction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.
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paper_title: Near optimal demand-side energy management under real-time demand-response pricing

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In this paper, we present demand-side energy management under real-time demand-response pricing as a task scheduling problem which is NP-hard. Using minmax as the objective, we show that the schedule produced by our minMax scheduling algorithm has a number of salient advantages: significant peak-shaving, cost reduction, and risk-aversion for the consumers. We prove that our algorithm finds near-optimal solutions and our simulation study show that the actual performance is better than the worst-case bound. The algorithm is simple to implement and efficient at the scale of large enterprises.
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paper_title: Appliance commitment for household load scheduling

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This paper presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts considering users' comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem is described using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load is modeled by physical models; random hot water consumption is modeled with statistical methods. The models are used to predict the appliance operation over the scheduling time horizon. User comfort is transformed to a set of linear constraints. Then, a novel linear-sequential-optimization-enhanced, multiloop algorithm is used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.
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paper_title: Residential demand response with interruptible tasks: Duality and algorithms

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This paper deals with optimal scheduling of demand response in a residential setup when the electricity prices are known ahead of time. Each end-user has a “must-run” load, and two types of adjustable loads. The first type must consume a specified total amount of energy over the scheduling horizon, but its consumption can be adjusted across the horizon. The second type of load has adjustable power consumption without a total energy requirement, but operation of the load at reduced power results in dissatisfaction of the end-user. Each adjustable load is interruptible in the sense that the load can be either operated (resulting in nonzero power consumption), or not operated (resulting in zero power consumption). Examples of such adjustable interruptible loads are charging a plugin hybrid electric vehicle or operating a pool pump. The problem amounts to minimizing the cost of electricity plus user dissatisfaction, subject to individual load consumption constraints. The problem is nonconvex, but surprisingly it is shown to have zero duality gap if a continuous-time horizon is considered. This opens up the possibility of using Lagrangian dual algorithms without loss of optimality in order to come up with efficient demand response scheduling schemes.
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paper_title: Demand Side Management in Smart Grid Using Heuristic Optimization

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Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
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paper_title: Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach

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This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G users' profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.
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paper_title: A demand side management based simulation platform incorporating heuristic optimization for management of household appliances

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Abstract Demand-Side Management (DSM) can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. This paper introduces dynamic distributed resource management and optimized operation of household appliances in a DSM based simulation tool. The principal purpose of the simulation tool is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer’s cost. A heuristic optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for the optimization of DSM operation in the tool. The tool also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the tool is illustrated through an example case study for various household situations.
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paper_title: Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing

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Demand response (DR) is very important in the future smart grid, aiming to encourage consumers to reduce their demand during peak load hours. However, if binary decision variables are needed to specify start-up time of a particular appliance, the resulting mixed integer combinatorial problem is in general difficult to solve. In this paper, we study a versatile convex programming (CP) DR optimization framework for the automatic load management of various household appliances in a smart home. In particular, an L1 regularization technique is proposed to deal with schedule-based appliances (SAs), for which their on/off statuses are governed by binary decision variables. By relaxing these variables from integer to continuous values, the problem is reformulated as a new CP problem with an additional L1 regularization term in the objective. This allows us to transform the original mixed integer problem into a standard CP problem. Its major advantage is that the overall DR optimization problem remains to be convex and therefore the solution can be found efficiently. Moreover, a wide variety of appliances with different characteristics can be flexibly incorporated. Simulation result shows that the energy scheduling of SAs and other appliances can be determined simultaneously using the proposed CP formulation.
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paper_title: The technical, economic and commercial viability of the vehicle-to-grid concept

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The idea that electric vehicles can be used to supply power to the grid for stabilisation and peak time supply is compelling, especially in regions where traditional forms of storage, back up or peaking supply are unavailable or expensive. A number of variants of the vehicle-to-grid theme have been proposed and prototypes have proven that the technological means to deliver many of these are available. This study reviews the most popular variants and investigates their viability using Western Australia, the smallest wholesale electricity market in the world, as an extreme test case. Geographical and electrical isolation prevents the trade of energy and ancillary services with neighbouring regions and the flat landscape prohibits hydroelectric storage. Hot summers and the widespread use of air-conditioning means that peak energy demand is a growing issue, and the ongoing addition to already underutilised generation and transmission capacity is unsustainable. The report concludes that most variants of vehicle-to-grid currently require too much additional infrastructure investment, carry significant risk and are currently too costly to implement in the light of alternative options. Charging electric vehicles can, however, be added to planned demand side management schemes without the need for additional capital investment.
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paper_title: Efficient Utilization of Renewable Energy Sources by Gridable Vehicles in Cyber-Physical Energy Systems

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The main sources of emission today are from the electric power and transportation sectors. One of the main goals of a cyber-physical energy system (CPES) is the integration of renewable energy sources and gridable vehicles (GVs) to maximize emission reduction. GVs can be used as loads, sources and energy storages in CPES. A large CPES is very complex considering all conventional and green distributed energy resources, dynamic data from sensors, and smart operations (e.g., charging/discharging, control, etc.) from/to the grid to reduce both cost and emission. If large number of GVs are connected to the electric grid randomly, peak load will be very high. The use of conventional thermal power plants will be economically expensive and environmentally unfriendly to sustain the electrified transportation. Intelligent scheduling and control of elements of energy systems have great potential for evolving a sustainable integrated electricity and transportation infrastructure. The maximum utilization of renewable energy sources using GVs for sustainable CPES (minimum cost and emission) is presented in this paper. Three models are described and results of the smart grid model show the highest potential for sustainability.
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paper_title: Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization

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A gridable vehicle (GV) can be used as a small portable power plant (S3P) to enhance the security and reliability of utility grids. Vehicle-to-grid (V2G) technology has drawn great interest in the recent years and its success depends on intelligent scheduling of GVs or S3Ps in constrained parking lots. V2G can reduce dependencies on small expensive units in existing power systems, resulting in reduced operation cost and emissions. It can also increase reserve and reliability of existing power systems. Intelligent unit commitment (UC) with V2G for cost and emission optimization in power system is presented in this paper. As number of gridable vehicles in V2G is much higher than small units of existing systems, UC with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to balance between cost and emission reductions for UC with V2G. PSO can reliably and accurately solve this complex constrained optimization problem easily and quickly. In the proposed solution model, binary PSO optimizes on/off states of power generating units easily. Vehicles are presented by integer numbers instead of zeros and ones to reduce the dimension of the problem. Balanced hybrid PSO optimizes the number of gridable vehicles of V2G in the constrained parking lots. Balanced PSO provides a balance between local and global searching abilities, and finds a balance in reducing both operation cost and emission. Results show a considerable amount of cost and emission reduction with intelligent UC with V2G. Finally, the practicality of UC with V2G is discussed for real-world applications.
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paper_title: Using fleets of electric-drive vehicles for grid support

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Electric-drive vehicles can provide power to the electric grid when they are parked (vehicle-to-grid power). We evaluated the economic potential of two utility-owned fleets of battery-electric vehicles to provide power for a specific electricity market, regulation, in four US regional regulation services markets. The two battery-electric fleet cases are: (a) 100 Th!nk City vehicle and (b) 252 Toyota RAV4. Important variables are: (a) the market value of regulation services, (b) the power capacity (kW) of the electrical connections and wiring, and (c) the energy capacity (kWh) of the vehicle's battery. With a few exceptions when the annual market value of regulation was low, we find that vehicle-to-grid power for regulation services is profitable across all four markets analyzed. Assuming now more than current Level 2 charging infrastructure (6.6 kW) the annual net profit for the Th!nk City fleet is from US$ 7000 to 70,000 providing regulation down only. For the RAV4 fleet the annual net profit ranges from US$ 24,000 to 260,000 providing regulation down and up. Vehicle-to-grid power could provide a significant revenue stream that would improve the economics of grid-connected electric-drive vehicles and further encourage their adoption. It would also improve the stability of the electrical grid.
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paper_title: Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid

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The integration of renewable energy systems (RESs) in smart grids (SGs) is a challenging task, mainly due to the intermittent and unpredictable nature of the sources, typically wind or sun. Another issue concerns the way to support the consumers' participation in the electricity market aiming at minimizing the costs of the global energy consumption. This paper proposes an energy management system (EMS) aiming at optimizing the SG's operation. The EMS behaves as a sort of aggregator of distributed energy resources allowing the SG to participate in the open market. By integrating demand side management (DSM) and active management schemes (AMS), it allows a better exploitation of renewable energy sources and a reduction of the customers' energy consumption costs with both economic and environmental benefits. It can also improve the grid resilience and flexibility through the active participation of distribution system operators (DSOs) and electricity supply/demand that, according to their preferences and costs, respond to real-time price signals using market processes. The efficiency of the proposed EMS is verified on a 23-bus 11-kV distribution network.
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paper_title: Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

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Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.
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paper_title: Real-time vehicle-to-grid control algorithm under price uncertainty

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The vehicle-to-grid (V2G) system enables energy flow from the electric vehicles (EVs) to the grid. The distributed power of the EVs can either be sold to the grid or be used to provide frequency regulation service when V2G is implemented. A V2G control algorithm is necessary to decide whether the EV should be charged, discharged, or provide frequency regulation service in each hour. The V2G control problem is further complicated by the price uncertainty, where the electricity price is determined dynamically every hour. In this paper, we study the real-time V2G control problem under price uncertainty. We model the electricity price as a Markov chain with unknown transition probabilities and formulate the problem as a Markov decision process (MDP). This model features implicit estimation of the impact of future electricity prices and current control operation on long-term profits. The Q-learning algorithm is then used to adapt the control operation to the hourly available price in order to maximize the profit for the EV owner during the whole parking time. We evaluate our proposed V2G control algorithm using both the simulated price and the actual price from PJM in 2010. Simulation results show that our proposed algorithm can work effectively in the real electricity market and it is able to increase the profit significantly compared with the conventional EV charging scheme.
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paper_title: A day-ahead electricity pricing model based on smart metering and demand-side management

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Several factors support more deployment of real-time pricing (RTP); including recent developments in the area of smart metering, regulators interest in promoting demand response programs and well-organized electricity markets. This paper first reviews time-based electricity pricing and the main barriers and issues to fully unleash benefits of RTP programs. Then, a day-ahead real-time pricing (DA-RTP) model is proposed, which addresses some of these issues. The proposed model can assist a retail energy provider and/or a distribution company (DISCO) to offer optimal DA hourly prices using smart metering. The real-time prices are determined through an optimization problem which seeks to maximize the electricity provider's profit, while considering consumers' benefit, minimum daily energy consumption, consumer response to posted electricity prices, and distribution network constraints. The numerical results associated with Ontario electricity tariffs indicate that instead of directly posting DA market prices to consumers, it would be better to calculate optimal prices which would yield higher benefit both for the energy provider and consumers.
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paper_title: Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

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Vehicle-to-grid (V2G) has been proposed as a way to increase the adoption rate of electric vehicles (EVs). Unidirectional V2G is especially attractive because it requires little if any additional infrastructure other than communication between the EV and an aggregator. The aggregator in turn combines the capacity of many EVs to bid into energy markets. In this work an algorithm for unidirectional regulation is developed for use by an aggregator. Several smart charging algorithms are used to set the point about which the rate of charge varies while performing regulation. An aggregator profit maximization algorithm is formulated with optional system load and price constraints analogous to the smart charging algorithms. Simulations on a hypothetical group of 10 000 commuter EVs in the Pacific Northwest verify that the optimal algorithms increase aggregator profits while reducing system load impacts and customer costs.
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paper_title: Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

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We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.
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paper_title: Concurrent optimization of consumer's electrical energy bill and producer's power generation cost under a dynamic pricing model

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Demand response is a key element of the smart grid technologies. This is a particularly interesting problem with the use of dynamic energy pricing schemes which incentivize electricity consumers to consume electricity more prudently in order to minimize their electric bill. On the other hand optimizing the number and production time of power generation facilities is a key challenge. In this paper, three models are presented for consumers, utility companies, and a third-part arbiter to optimize the cost to the parties individually and in combination. Our models have high quality and exhibit superior performance, by realistic consideration of non-cooperative energy buyers and sellers and getting real-time feedback from their interactions. Simulation results show that the energy consumption distribution becomes very stable during the day utilizing our models, while consumers and utility companies pay lower cost.
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paper_title: Distributed Demand and Response Algorithm for Optimizing Social-Welfare in Smart Grid

paper_content:

This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart girds. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.
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paper_title: Optimal demand response using mechanism design in the smart grid

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Demand side management is considered to be a key component in the future smart grid that can help to achieve an efficient utilization of energy. In this paper we consider a residential power network, where consumers are asked to report their information about power usage to service provider, then the provider determines the optimal power allocations and charges for each user based on social welfare maximization. The benefit of each user is related to demand for electricity and the quantity of power allocated. Assuming consumers are strategic and selfish, we proposed an efficient pricing algorithm with which consumers cannot achieve greater benefit by mis-reporting and social welfare maximization can be achieved. Finally we present simulation results to show that our proposed method can benefit both of the service provider and consumers.
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paper_title: Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services

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Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits.
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paper_title: Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

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In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.
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paper_title: Multi-period optimal energy procurement and demand response in smart grid with uncertain supply

paper_content:

We propose a simple model that integrates two-period electricity markets, uncertainty in renewable generation, and real-time dynamic demand response. A load-serving entity decides its day-ahead procurement to optimize expected social welfare a day before energy delivery. At delivery time when renewable generation is realized, it sets prices to manage demand and purchase additional power on the real-time market, if necessary, to balance supply and demand. We derive the optimal day-ahead decision, propose real-time demand response algorithm, and study the effect of volume and variability of renewable generation on the social welfare.
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paper_title: Intelligent Scheduling of Hybrid and Electric Vehicle Storage Capacity in a Parking Lot for Profit Maximization in Grid Power Transactions

paper_content:

This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end.
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paper_title: A microgrid energy management system for inducing optimal demand response

paper_content:

This paper focuses on optimal operation schedule of a Microgrid that is interconnected to the power grid. We develop a mathematical model to compute the optimal operation schedule that embodies demand response. Integer Programming optimization is used to this end. Our model incorporates the electricity load into three types: fixed, transferable, and user-action loads. The transferable load plays a key role in molding demand response. Experimental results show that the proposed model exploits the demand elasticity and significantly reduces the total operation cost. Also observed from the experiments are the impact of the uncertainty in renewable distributed generators on operation schedule and total cost and the role of power storages for enhancing the demand elasticity with respect to user-action loads and for reserving power against high price.
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paper_title: An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

paper_content:

Abstract The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD®, is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD®.
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paper_title: Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization

paper_content:

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology's performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.
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paper_title: Optimal control of a residential microgrid

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We propose a generic mixed integer linear programming model to minimize the operating cost of a residential microgrid. We model supply and demand of both electrical and thermal energy as decision variables. The modeled microgrid is operated in grid-connected mode. It covers solar energy, distributed generators, energy storages, and loads, among them controllable load jobs released by home appliances and electric vehicles. We propose a model predictive control scheme to iteratively produce a control sequence for the studied microgrid. Our case study reveals the performance of minimum cost control by comparison with benchmark control policies. We consider three price scenarios in our analyses which include two market-based scenarios. Numerical results from our study indicate savings in annual operating cost between 3.1 and 7.6 percent.
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paper_title: Dynamic Residential Demand Response and Distributed Generation Management in Smart Microgrid with Hierarchical Agents

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Abstract Smart grid has been a significant development trend of power system. Within smart grid, microgrids share the burden of traditional grids, reduce energy consumption cost and alleviate environment deterioration. This paper proposes a dynamic Demand Response (DR) and Distributed Generation (DG) management approach in the context of smart microgrid for a residential community. With a dynamic update mechanism, the DR operates automatically and allows manual interference. The DG management coordinates with DR and considers stochastic elements, such as stochastic load and wind power, to reduce the energy consumption cost of the community. Simulation and numerical results show the effectiveness of the system on reducing the energy consumption cost while keeping users’ satisfaction at a high level.
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paper_title: Control for large scale demand response of thermostatic loads*

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Demand response is an important Smart Grid concept that aims at facilitating the integration of volatile energy resources into the electricity grid. This paper considers a residential demand response scenario and specifically looks into the problem of managing a large number thermostat-based appliances with on/off operation. The objective is to reduce the consumption peak of a group of loads composed of both flexible and inflexible units. The power flexible units are the thermostat-based appliances. We discuss a centralized, model predictive approach and a distributed structure with a randomized dispatch strategy.
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paper_title: Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility

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Smart grids are capable of two-way communication between individual user devices and the electricity provider, enabling providers to create a control-feedback loop using time-dependent pricing. By charging users more in peak and less in off-peak hours, the provider can induce users to shift their consumption to off-peak periods, thus relieving stress on the power grid and the cost incurred from large peak loads. We formulate the electricity provider's cost minimization problem in setting these prices by considering consumers' device-specific scheduling flexibility and the provider's cost structure of purchasing electricity from an electricity generator. Consumers' willingness to shift their device usage is modeled probabilistically, with parameters that can be estimated from real data. We develop an algorithm for computing day-ahead prices, and another algorithm for estimating and refining user reaction to the prices. Together, these two algorithms allow the provider to dynamically adjust the offered prices based on user behavior. Numerical simulations with data from an Ontario electricity provider show that our pricing algorithm can significantly reduce the cost incurred by the provider.
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paper_title: An integer linear programming based optimization for home demand-side management in smart grid

paper_content:

We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.
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paper_title: A linear programming model for reducing system peak through customer load control programs

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A linear programming (LP) model has been developed to optimize the amount of power system peak load reduction through scheduling of control periods in commercial/industrial and residential load control programs at Florida Power and Light Company, USA. The LP model can be used to determine both long and short term control scheduling strategies and for planning the number of customers which should be enrolled in each program. Results of applying the model to a forecasted late 1990s summer peak day load shape are presented. It is concluded that LP solutions provide a relatively inexpensive and powerful approach to planning and scheduling load control. Also, it is not necessary to model completely general scheduling of control periods in order to obtain near best solutions to peak load reduction.
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paper_title: Control method for multi-microgrid systems in smart grid environment—Stability, optimization and smart demand participation

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This paper presents a control strategy for microgrids in smart grid environment. A hierarchical control strategy is developed to ensure stability and to optimize operation of microgrid. Communication, control and advanced metering infrastructure of smart grids are used to facilitate this control strategy. The control strategy incorporates storage device, electric car, various distributed energy resources and loads. Proposed control strategy considers microgrid operation in island and grid-connected mode. Island microgrid is stabilized by managing storage devices, dispatchable energy units and controllable loads. The control strategy is based on demand participation while stability of system has the highest priority. Theoretical discussion beyond presented algorithms reveal evidently the effectiveness of the proposed control method.
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paper_title: Parallel autonomous optimization of demand response with renewable distributed generators

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We propose a framework for demand response in smart grids that integrate renewable distributed generators (DGs). In this framework, some users have DGs and can generate part of their electricity. They can also sell extra generation to the utility company. The goal is to optimize the load schedule of users to minimize the utility company's cost and user payments. We employ parallel autonomous optimization, where each user requires only knowledge of the aggregated load of other users instead of the load profiles of individual users, and can execute distributed optimization simultaneously. We performed numerical examples to validate our algorithm. The results show that our method can significantly lower peak hour load and reduce the costs to users and the utility. Since the autonomous user optimizations are executed in parallel, our method also dramatically decreases the computation time, management complexity, and communication costs.
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paper_title: Optimal Scheduling of Smart Homes Energy Consumption with Microgrid

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Microgrid is taken as the future Smart Grid, and can work as a local energy provider to domestic buildings and reduce energy expenses. To further lower the cost, a Smart Homes idea is suggested. Smart Homes of the future will include automation systems and could provide lower energy consumption costs and comfortable and secure living environment to end users. If the energy consumption tasks across multiple homes can be scheduled based on the users' requirement, the energy cost and peak demand could be reduced. In this paper the optimal scheduling of smart homes' energy consumption is presented using mixed-integer linear programming. In order to minimize a one-day forecasted energy consumption cost, operation and electricity consumption tasks are scheduled based on different electricity tariffs, electricity task time window and forecasted renewable energy output. A case study of thirty homes with their own microgrid indicates the possibility of cost saving and asset utilization increase.
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paper_title: Demand response models with correlated price data: A robust optimization approach

paper_content:

In the electricity industry, the processes through which consumers respond to price signals embedded in tariffs by changing their consumption patterns is generally referred to as demand response. In such a context, consumers are offered an opportunity to maximize the surplus derived from electricity usage by actively scheduling consumption over time periods with potentially different energy prices. The objective of this work is to analyze the role of correlation in prices of successive periods over which consumption is to be scheduled, in a demand response context. We use robust optimization techniques to propose an optimization model for consumption scheduling when prices of different periods are highly correlated, and also suggest approaches to correctly incorporate real-world correlated price data to this model. Positive results from quantitative case studies indicate that it is of great importance to employ a solution approach that correctly models correlation among prices when scheduling consumption.
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paper_title: Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)

paper_content:

Today's, policy makers, governments, and academic experts in flourishing societies are interested in employing power systems considering high reliability, quality, and efficiency factors. Moreover, climatic concerns force power system appliers to utilize these systems more environmental friendly. To obtain the mentioned aims, MGs (microgrids) act as key solutions. MGs are invented not only to operate power systems more reliable and efficient but also to penetrate CHP (combined heat and power)-based DG (distributed generation) into power systems with an optimal control on their generation. This paper presents a new optimal operation of a CHP-based MG comprising ESS (energy storage system), three types of thermal power generation units, and DRPs (demand response programs). In this paper, DRPs are treated as virtual generation units along with all of realization constraints. In a multi-objective self-scheduling optimization problem of a MG, the first objective deals with minimizing total operational cost of the CHP-MG in an OPF-based formulation and the second refers to the emission minimization of DGs. The proposed model implements a simple MIP (mixed-integer programming) that can be easily integrated in the MGCC (MG central controller). The effectiveness of the proposed methodology has been investigated on a typical 24-bus MG.
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paper_title: Optimal dispatching model of Smart Home Energy Management System

paper_content:

In this paper, we developed an optimal dispatching model of Smart Home Energy Management System (SHEMS) with distributed energy resources (DERs) and intelligent domestic appliances. In order to achieve multi-objective optimization between the saving money and living comfortable, we investigate the math models of various components and come up with the new concept “load value” for quantitative measure of users' comfort. Then we set up the control strategies with demand response and adjust the parameters of optimal dispatching model by the load characteristic in this system. Applying this model to one house with Photovoltaic (PV), wind turbine (WT), storage battery and time-of-use prices, the simulation at the end of this paper proves that the energy management system and optimal dispatching model can make users of smart home live in a comfortable and economical way.
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paper_title: A taxonomy of line balancing problems and their solutionapproaches

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Line balancing belongs to a class of intensively studied combinatorial optimization problems known to be NP-hard in general. For several decades, the core problem originally introduced for manual assembly has been extended to suit robotic, machining and disassembly contexts. However, despite various industrial environments and line configurations, often quite similar or even identical mathematical models have been developed. The objective of this survey is to analyze recent research on balancing flow lines within many different industrial contexts in order to classify and compare the means for input data modelling, constraints and objective functions used. This survey covers about 300 studies on line balancing problems. Particular attention is paid to recent publications that have appeared in 2007–2012 to focus on new advances in the state-of-the-art.
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paper_title: Coordinated home energy management for real-time power balancing

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This paper proposes a coordinated home energy management system (HEMS) architecture where the distributed residential units cooperate with each other to achieve real-time power balancing. The economic benefits for the retailer and incentives for the customers to participate in the proposed coordinated HEMS program are given. We formulate the coordinated HEMS design problem as a dynamic programming (DP) and use approximate DP approaches to efficiently handle the design problem. A distributed implementation algorithm based on the convex optimization based dual decomposition technique is also presented. Our focus in the current paper is on the deferrable appliances, such as Plug-in (Hybrid) Electric Vehicles (PHEV), in view of their higher impact on the grid stability. Simulation results shows that the proposed coordinated HEMS architecture can efficiently improve the real-time power balancing.
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paper_title: Cooperative multi-residence demand response scheduling

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This paper is concerned with scheduling of demand response among different residences and a utility company. The utility company has a cost function representing the cost of providing energy to end-users, and this cost can be varying across the scheduling horizon. Each end-user has a “must-run” load, and two types of adjustable loads. The first type must consume a specified total amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type of load has adjustable power consumption without a total energy requirement, but operation of the load at reduced power results in dissatisfaction of the end-user. The problem amounts to minimizing the total cost electricity plus the total user dissatisfaction (social welfare), subject to the individual load consumption constraints. The problem is convex and can be solved by a distributed subgradient method. The utility company and the end-users exchange Lagrange multipliers—interpreted as pricing signals—and hourly consumption data through the Advanced Metering Infrastructure, in order to converge to the optimal amount of electricity production and the optimal power consumption schedule.
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paper_title: Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

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In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
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paper_title: Optimal Power Allocation Under Communication Network Externalities

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Efficient resource allocation is an important problem that aims for a “greener” and more environmentally friendly electric power grid. The smart behavior of the newly emerged grid, combined with two-way communication between users and the operator allows for actions like measurement, monitoring, prediction, and control signaling so as to maximize social welfare. We introduce a framework for optimal resource allocation in smart grids that also considers the uncertainty in message signaling. This introduces communication network externalities, added on top of the existing transmission network ones. The task at hand resembles the so called local public goods problem in mathematical economics terminology, a problem impractical to solve using centralized mechanisms. We propose an iterative, decentralized algorithm for its solution. The algorithm is scalable for deployment in large networks since it requires only messages per network user per iteration, where is the number of users. Moreover, it is guaranteed to converge, does not require revelation of private information from each user and all algorithm actions can be realized by programmable smart devices of the grid.
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paper_title: Optimal demand response based on utility maximization in power networks

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Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.
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paper_title: Vehicle-to-Aggregator Interaction Game

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Electric vehicles (EVs) are likely to become very popular worldwide within the next few years. With possibly millions of such vehicles operating across the country, one can establish a distributed electricity storage system that comprises of the EVs' batteries with a huge total storage capacity. This can help the power grid by providing various ancillary services, once an effective vehicle-to-grid (V2G) market is established. In this paper, we propose a new game-theoretic model to understand the interactions among EVs and aggregators in a V2G market, where EVs participate in providing frequency regulation service to the grid. We develop a smart pricing policy and design a mechanism to achieve optimal frequency regulation performance in a distributed fashion. Simulation results show that our proposed pricing model and designed mechanism work well and can benefit both EVs (in terms of obtaining additional income) and the grid (in terms of achieving the frequency regulation command signal).
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paper_title: Two Market Models for Demand Response in Power Networks

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In this paper, we consider two abstract market models for designing demand response to match power supply and shape power demand, respectively. We characterize the resulting equilibria in competitive as well as oligopolistic markets, and propose distributed demand response algorithms to achieve the equilibria. The models serve as a starting point to include the appliance-level details and constraints for designing practical demand response schemes for smart power grids.
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paper_title: A cheat-proof game theoretic demand response scheme for smart grids

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While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game theoretic scheme is cheat-proof, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game theoretic algorithm.
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paper_title: A differential game approach to distributed demand side management in smart grid

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Smart grid is a visionary user-centric system that will elevate the conventional power grid system to one which functions more cooperatively, responsively, and economically. Dynamic demand side management is one of the key issues that enable the implementation of smart grid. In this paper, we use the framework of dynamic games to model the distribution demand side management. The market price is characterized as the dynamic state using a sticky price model. A two-layer optimization framework is established. At the lower level, for each player (such as one household), different appliances are scheduled for energy consumption. At the upper level, the dynamic game is used to capture the interaction among different players in their demand responses through the market price. We analyze the N-person nonzero-sum stochastic differential game and characterize its feedback Nash equilibrium. A special case of homogeneous users is investigated in detail and we provide a closed-form solution for the optimal demand response. From the simulation results, we demonstrate the use of demand response strategy from the game-theoretic framework and study the behavior of market price and demand responses to different parameters.
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paper_title: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

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Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
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paper_title: A nested game-based optimization framework for electricity retailers in the smart grid with residential users and PEVs

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In the smart grid, real-time pricing policy is an important mechanism for incentivizing the consumers to dynamically change or shift their electricity consumption, thereby improving the reliability of the grid. Retailers are incorporated to the smart grid with distributed control mechanism in order to reduce the amount of communication overhead associated with the direction interaction between utility companies and consumers. The retailer procures electricity from both traditional and renewable energy sources, and sells it to its consumers. The consumers include residential users that can only consume power, and plug-in electric vehicles (PEVs) that can either consume power or supply power stored in its battery to the grid. In this work, a novel four-stage nested game model is proposed to model the interaction of the electricity retailer, utility companies, and consumers. The objective of the retailer is to maximize its overall profit as well as perform frequency regulation, whereas the goal of each consumer is to maximize a predefined utility function. In the game theoretic framework, the retailer should decide the amounts of electricity purchased from the renewable and traditional energy sources, respectively, as well as the real-time pricing scheme for its consumers. The consumers will react to the pricing mechanism and maximize their utility functions by adjusting the electricity demand. The optimal solution of the nested game is provided through: (i) finding the subgame perfect equilibrium (SPE) of all the consumers, and (ii) optimizing the retailer's action using the backward induction method. Experimental results demonstrate the effectiveness of the proposed game theoretic modeling and optimization framework.
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paper_title: A game-theoretic approach for optimal time-of-use electricity pricing

paper_content:

Demand for electricity varies throughout the day, increasing the average cost of power supply. Time-of-use (TOU) pricing has been proposed as a demand-side management (DSM) method to influence user demands. In this paper, we describe a game-theoretic approach to optimize TOU pricing strategies (GT-TOU). We propose models of costs to utility companies arising from user demand fluctuations, and models of user satisfaction with the difference between the nominal demand and the actual consumption. We design utility functions for the company and the users, and obtain a Nash equilibrium using backward induction. In addition to a single-user-type scenario, we also consider a scenario with multiple types of users, each of whom responds differently to time-dependent prices. Numerical examples show that our method is effective in leveling the user demand by setting optimal TOU prices, potentially decreasing costs for the utility companies, and increasing user benefits. An increase in social welfare measure indicates improved market efficiency through TOU pricing.
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paper_title: Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach

paper_content:

Demand Response Management (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists. We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies. We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability.
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paper_title: Economics of Electric Vehicle Charging: A Game Theoretic Approach

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In this paper, the problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game. In this game, on the one hand, the smart grid, which acts as a leader, needs to decide on its price so as to optimize its revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs, which act as followers, need to decide on their charging strategies so as to optimize a tradeoff between the benefit from battery charging and the associated cost. Using variational inequalities, it is shown that the proposed game possesses a socially optimal Stackelberg equilibrium in which the grid optimizes its price while the PEVGs choose their equilibrium strategies. A distributed algorithm that enables the PEVGs and the smart grid to reach this equilibrium is proposed and assessed by extensive simulations. Further, the model is extended to a time-varying case that can incorporate and handle slowly varying environments.
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paper_title: An innovative RTP-based residential power scheduling scheme for smart grids

paper_content:

This paper proposes a Real-Time Pricing (RTP)-based power scheduling scheme as demand response for residential power usage. In this scheme, the Energy Management Controller (EMC) in each home and the service provider form a Stackelberg game, in which the EMC who schedules appliances' operation plays the follower level game, and the provider who sets the real-time prices according to current power usage profile plays the leader level game. The sequential equilibrium is obtained through the information exchange between them. Simulation results indicate that our scheme can not only save money for consumers, but also reduce peak load and the variance between demand and supply, while avoiding the “rebound” peak problem.
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paper_title: Real-time welfare-maximizing regulation allocation in aggregator-EVs systems

paper_content:

The concept of vehicle-to-grid (V2G) has gained recent interest as more and more electric vehicles (EVs) are put to use. In this paper, we consider a dynamic aggregator-EVs system, where an aggregator centrally coordinates a large number of EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among the EVs. The algorithm operates in real time and does not require any prior knowledge on the statistical information of the system. Compared with previous works, WMRA accommodates a wide spectrum of vital system characteristics, including limited EV battery size, EV self charging/discharging, EV battery degradation cost, and the cost of using external energy sources. Furthermore, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.
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paper_title: A bilevel model for electricity retailers' participation in a demand response market environment

paper_content:

Demand response programmes are seen as one of the contributing solutions to the challenges posed to power systems by the large-scale integration of renewable power sources, mostly due to their intermittent and stochastic nature. Among demand response programmes, real-time pricing schemes for small consumers are believed to have significant potential for peak-shaving and load-shifting, thus relieving the power system while reducing costs and risk for energy retailers. This paper proposes a game theoretical model accounting for the Stackelberg relationship between retailers (leaders) and consumers (followers) in a dynamic price environment. Both players in the game solve an economic optimisation problem subject to stochasticity in prices, weather-related variables and must-serve load. The model allows the determination of the dynamic price-signal delivering maximum retailer profit, and the optimal load pattern for consumers under this pricing. The bilevel programme is reformulated as a single-level MILP, which can be solved using commercial off-the-shelf optimisation software. In an illustrative example, we simulate and compare the dynamic pricing scheme with fixed and time-of-use pricing. We find that the dynamic pricing scheme is the most effective in achieving load-shifting, thus reducing retailer costs for energy procurement and regulation in the wholesale market. Additionally, the redistribution of the saved costs between retailers and consumers is investigated, showing that real-time pricing is less convenient than fixed and time-of-use price for consumers. This implies that careful design of the retail market is needed. Finally, we carry out a sensitivity analysis to analyse the effect of different levels of consumer flexibility.
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paper_title: Auctioning game based Demand Response scheduling in smart grid

paper_content:

Matching demand to supply is one of the key features of smart grid infrastructure. Transforming conventional static customers into active participants who interact with the electrical utility in real time is the central idea of Demand Response (DR)\Demand Side Management (DSM) in smart grid. In this paper, we decouple utility cost minimization and customer social welfare maximization into two stages. Since the utility is usually more risk averse than risk neutral in real life, this decoupling approach is more realistic than the usually adopted optimization setup, in which the two objectives are combined in a single weighted sum. With a block processing model introduced, in the first stage a convex optimization problem is formulated to minimize utility's generation cost and delay operation cost. An optimal load demand scheduling solution, of the form of water-filling, is derived analytically. Based on the optimal load profile generated in this first stage, repeated Vickrey auctions over time intervals are adopted to allocate load demands among customers while maximizing the social welfare. Despite the fact that truthful bidding is a weakly dominant strategy for all customers in the auctioning game, collusive equilibria do exist and jeopardize utility's profit severely. Analysis on the structure of the Bayesian Nash equilibrium solutions shows that by introducing a positive reserve price the Vickrey auction can be made to be more robust against such collusion by customers. Moreover the corresponding Bayesian Nash equilibrium is essentially unique and guarantees the basic profit of the utility. We further discuss how customers' valuations and bidding strategies change over time for the repeated Vickrey auction model. Simulation results emphasizing the influences of reserve price and time interval size on utility's profit is also presented.
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paper_title: Lessons learned from smart grid enabled pricing programs

paper_content:

Dynamic pricing is often considered an essential part of demand response programs, particularly when considering the advent of new consumer-facing technologies, which will eventually reshape the relationship between utility and consumer. This paper presents and analyzes case studies of several dynamic pricing programs, including different proposed rates, enabling technologies and incentives. Program successes are evaluated based on a combination of peak load reduction, customer bill impacts and customer satisfaction. An analysis of lessons learned is provided on how various factors can affect the success, scalability and applicability of smart grid demand response programs.
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paper_title: Investing in smart grids within the market paradigm: The case of the Netherlands and its relevance for China

paper_content:

Abstract The introduction of market forces in the energy sector in the Netherlands and elsewhere in Europe has drastically changed the climate for investments in the electricity infrastructure. In contrast with the former public monopoly situation, many new uncertainties need to be taken into account in network investment decisions. Another marked difference is the fragmentation of energy markets and the entrance of new players on the energy market stage, often in new roles. The upgrading of electricity networks to create so-called smart grids has therewith become a multi-actor investment problem, which requires a well-designed and well-managed process. For the specific case of the Netherlands, the challenges of smart grid development are explored in depth. As several competing scenarios are possible for the realization of smart grids, it is likely that the scenario first entering the implementation stage will have a first-mover advantage. This, in combination with the split incentive issue for smart grids, results in the conclusion that the realization of smart grids needs public support. Similar outcomes are observed in other European countries despite markedly different local conditions and drivers for the smart grid. It seems that smart public policy intervention is needed to reduce the overwhelming uncertainties related to smart grid investments, which by far exceed the normal investment risk as encountered in network expansion. Finally, the relevance of these findings is discussed for the future of the electricity sector in China's emerging economy.
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paper_title: Demand Response From Household Customers: Experiences From a Pilot Study in Norway

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This paper presents experiences from a pilot study focusing on daily demand response from households, utilizing smart metering, remote load control, pricing based on the hourly spot price combined with a time of day network tariff, and a token provided to the customers indicating peak hours. The observed demand response was 1 kWh/h for customers with standard electrical water heaters. By aggregating this response, the potential for demand response from 50% of Norwegian households can be estimated at 1000 MWh/h (4.2% of registered peak load demand in Norway). A cost-effective realization of this potential should have high focus when considering smart metering technology. From a market perspective, a potential load reduction of this size should be bid into the day ahead market. Demand response to price (the day after) will not affect the price, but might create imbalances and the need for activating balancing resources, creating additional costs.
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paper_title: How to Engage Consumers in Demand Response: A Contract Perspective

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Nowadays, the European electricity systems are evolving towards a generation mix that is more decentralised, less predictable and less flexible to operate. In this context, additional flexibility is expected to be provided by the demand side. Thus, how to engage consumers to participate in demand response is becoming a pressing issue. In this paper, we provide an analytical framework to assess consumers’ potential and willingness to participate in active demand response from a contract perspective. On that basis, we present policy recommendations to empower and protect consumers in their shift to active demand response participants.
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paper_title: Achieving Optimality and Fairness in Autonomous Demand Response: Benchmarks and Billing Mechanisms

paper_content:

Autonomous demand response (DR) programs are scalable and result in a minimal control overhead on utilities. The idea is to equip each user with an energy consumption scheduling (ECS) device to automatically control the user's flexible load to minimize his energy expenditure, based on the updated electricity pricing information. While most prior works on autonomous DR have focused on coordinating the operation of ECS devices in order to achieve various system-wide goals, such as minimizing the total cost of generation or minimizing the peak-to-average ratio in the load demand, they fall short addressing the important issue of fairness. That is, while they usually guarantee optimality, they do not assure that the participating users are rewarded according to their contributions in achieving the overall system's design objectives. Similarly, they do not address the important problem of co-existence when only a sub-set of users participate in a deployed autonomous DR program. In this paper, we seek to tackle these shortcomings and design new autonomous DR systems that can achieve both optimality and fairness. In this regard, we first develop a centralized DR system to serve as a benchmark. Then, we develop a smart electricity billing mechanism that can enforce both optimality and fairness in autonomous DR systems in a decentralized fashion.
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paper_title: Dynamic Pricing? Not So Fast! A Residential Consumer Perspective

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With the installation of smart metering, will residential customers be moved to "dynamic" pricing? Some supporters of changing residential rate design from a fixed and stable rate structure believe customers should be required to take electric service with time-variant price signals. Not so fast, though! There are real implications associated with this strategy.
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paper_title: DEMAND RESPONSE EXPERIENCE IN EUROPE: POLICIES, PROGRAMMES AND IMPLEMENTATION

paper_content:

Over the last few years, load growth, increases in intermittent generation, declining technology costs and increasing recognition of the importance of customer behaviour in energy markets have brought about a change in the focus of Demand Response (DR) in Europe. The long standing programmes involving large industries, through interruptible tariffs and time of day pricing, have been increasingly complemented by programmes aimed at commercial and residential customer groups. Developments in DR vary substantially across Europe reflecting national conditions and triggered by different sets of policies, programmes and implementation schemes. This paper examines experiences within European countries as well as at European Union (EU) level, with the aim of understanding which factors have facilitated or impeded advances in DR. It describes initiatives, studies and policies of various European countries, with in-depth case studies of the UK, Italy and Spain. It is concluded that while business programmes, technical and economic potentials vary across Europe, there are common reasons as to why coordinated DR policies have been slow to emerge. This is because of the limited knowledge on DR energy saving capacities; high cost estimates for DR technologies and infrastructures; and policies focused on creating the conditions for liberalising the EU energy markets.
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paper_title: Tackling co-existence and fairness challenges in autonomous Demand Side Management

paper_content:

Consider a smart grid system in which every user may or may not choose to participate in Demand Side Management (DSM). This will lead to a general co-existence problem between participant and non-participant users. To gain insights, first, we show that some existing electricity billing mechanisms suffer from severe fairness and co-existence defects. Next, we propose an alternative billing mechanism that can tackle the coexistence and fairness problems by taking into account not only the users' total load, but also the exact shape of their load profiles. Our analytical results provide mild sufficient conditions on the choice of system parameters to assure fairness. Furthermore, our simulation results confirm that the proposed billing mechanism significantly improves the fairness index of the DSM system.
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paper_title: Smart Grids and Beyond: Achieving the Full Potential of Electricity Systems

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This paper explores how electricity systems may evolve in the 21st century. The paper focuses on some funda- mental challenges facing the utilization of electricity today and for years to come. Paralleling the challenges, several directions of how new solutions may emerge are suggested. In this con- text, some new approaches to manage power system develop- ment and deployment are outlined.
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paper_title: Deployment of demand response as a real-time resource in organized markets

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The use of DR as a dispatchable resource in the real-time energy markets should be encouraged, not discouraged. We are fortunate that the smart-grid technology now exists to fully exploit this valuable resource.
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paper_title: Price-Responsive Demand Management for a Smart Grid World

paper_content:

Price-responsive demand is essential for the success of a smart grid. However, existing demand-response programs run the risk of causing inefficient price formation. This problem can be solved if each retail customer could establish a contract-based baseline through demand subscription before joining a demand-response program.
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paper_title: Incorporating fairness within Demand response programs in smart grid

paper_content:

Basic Demand response (DR) programs aim to modulate the demand of electricity in accordance with its supply. The existing DR programs have only been of limited success, though the participation has steadily increased in the recent past. This paper establishes the lack of fairness principles within the DR programs, as perceived by the customers to be one of the key deterrents. Fair DR (FDR) scheme criteria are defined and compared with existing pricing schemes. In this context, a simplified pricing model that takes into consideration fairness criteria for residential category is also proposed in this paper. The proposed pricing model is simulated in Gridlab-D and the results are compared with that of the flat and the price based pricing schemes. Initial results establish that our pricing scheme is fair, it flattens the demand curve over a day and provides a win-win situation for both - the customer and the utility company.
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paper_title: Case studies of smart grid demand response programs in North America

paper_content:

Demand response services that engage consumers are an important emerging aspect of the smart grid. The advent of new consumer-facing technologies that communicate with the electric utility is enabling this transformation. This paper presents and analyzes case studies of different electric utility programs, including enabling technologies and incentives, on smart grid demand response. The program successes are evaluated in terms of reduction in peak load and/or customer energy usage and customer satisfaction. An analysis of lessons learned is provided on how various incentives can affect the success and scalability of smart grid demand response programs.
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paper_title: Piloting the Smart Grid

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To address the likely impact of the smart grid on customers, utilities, and society as a whole, it may be necessary to conduct a pilot. When should a pilot be conducted and how should it be conducted? What validity criteria should the pilot satisfy? Here are issues to consider.
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paper_title: The Ethics of Dynamic Pricing

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Dynamic pricing has garnered much interest among regulators and utilities, since it has the potential for lowering energy costs for society. But the deployment of dynamic pricing has been remarkably tepid. The underlying premise is that dynamic pricing is unfair. But the presumption of unfairness in dynamic pricing rests on an assumption of fairness in today's tariffs.
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paper_title: Intelligent Scheduling of Hybrid and Electric Vehicle Storage Capacity in a Parking Lot for Profit Maximization in Grid Power Transactions

paper_content:

This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end.
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paper_title: Predicting user comfort level using machine learning for Smart Grid environments

paper_content:

Smart Grid with Time-of-Use (TOU) pricing brings new ways of cutting costs for energy consumers and conserving energy. It is done by utilities suggesting the user ways to use devices to lower their energy bills keeping in mind its own benefits in smoothening the peak demand curve. However, as suggested in previous related research, user's comfort need must be addressed in order to make the system work efficiently. In this work, we validate the hypothesis that user preferences and habits can be learned and user comfort level for new patterns of device usage can be predicted. We investigate how machine learning algorithms specifically supervised machine learning algorithms can be used to achieve this. We also compare the prediction accuracies of three commonly used supervised learning algorithms, as well as the effect that the number of training samples has on the prediction accuracy. Further more, we analyse how sensitive prediction accuracies yielded by each algorithm are to the number of training samples.
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paper_title: Artificial neural network for load forecasting in smart grid

paper_content:

It is an irresistible trend of the electric power improvement for developing the smart grid, which applies a large amount of new technologies in power generation, transmission, distribution and utilization to achieve optimization of the power configuration and energy saving. As one of the key links to make a grid smarter, load forecast plays a significant role in planning and operation in power system. Many ways such as Expert Systems, Grey System Theory, and Artificial Neural Network (ANN) and so on are employed into load forecast to do the simulation. This paper intends to illustrate the representation of the ANN applied in load forecast based on practical situation in Ontario Province, Canada.
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paper_title: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

paper_content:

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
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paper_title: A game-theoretic approach for optimal time-of-use electricity pricing

paper_content:

Demand for electricity varies throughout the day, increasing the average cost of power supply. Time-of-use (TOU) pricing has been proposed as a demand-side management (DSM) method to influence user demands. In this paper, we describe a game-theoretic approach to optimize TOU pricing strategies (GT-TOU). We propose models of costs to utility companies arising from user demand fluctuations, and models of user satisfaction with the difference between the nominal demand and the actual consumption. We design utility functions for the company and the users, and obtain a Nash equilibrium using backward induction. In addition to a single-user-type scenario, we also consider a scenario with multiple types of users, each of whom responds differently to time-dependent prices. Numerical examples show that our method is effective in leveling the user demand by setting optimal TOU prices, potentially decreasing costs for the utility companies, and increasing user benefits. An increase in social welfare measure indicates improved market efficiency through TOU pricing.
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paper_title: Forecasting for smart grid applications with Higher Order Neural Networks

paper_content:

This work presents the design of a neural network which combines higher order terms in its input layer and an Extended Kalman Filter (EKF) based algorithm for its training. The neural network based scheme is defined as a Higher Order Neural Network (HONN) and its applicability is illustrated by means of time series forecasting for three important variables present in smart grids: Electric Load Demand (ELD), Wind Speed (WS) and Wind Energy Generation (WEG). The proposed model is trained and tested using real data values taken from a microgrid system in the UADY School of Engineering. The length of the regression vector is determined via the Lipschitz quotients methodology.
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paper_title: Modeling and forecasting hourly electric load by multiple linear regression with interactions

paper_content:

Short-term electric load modeling and forecasting has been intensively studied during the past 50 years. With the emerging development of smart grid technologies, demand side management (DSM) starts to attract the attention of electric utilities again. To perform a decent DSM, beyond when and how much the demand will be, the utilities are facing another question: why is the electricity being consumed? In other words, what are the factors driving the fluctuation of the electric load at a particular time period? Understanding this issue can also be beneficial for the electric load forecasting with the purpose of energy purchase. This paper proposes a modern treatment of a classic technique, multiple linear regression, to model the hourly demand and investigate the causality of the consumption of electric energy. Various interactions are discovered, discussed, tested, and interpreted in this paper. The proposed approach has been used to generate the 3-year hourly energy demand forecast for a US utility.
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paper_title: Power prediction in smart grids with evolutionary local kernel regression

paper_content:

Electric grids are moving from a centralized single supply chain towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and dynamic scenario Soon, the growing smart meter infrastructure will allow the collection of terabytes of detailed data about the grid condition, e.g., the state of renewable electric energy producers or the power consumption of millions of private customers, in very short time steps For reliable prediction strong and fast regression methods are necessary that are able to cope with these challenges In this paper we introduce a novel regression technique, i.e., evolutionary local kernel regression, a kernel regression variant based on local Nadaraya-Watson estimators with independent bandwidths distributed in data space The model is regularized with the CMA-ES, a stochastic non-convex optimization method We experimentally analyze the load forecast behavior on real power consumption data The proposed method is easily parallelizable, and therefore well appropriate for large-scale scenarios in smart grids.
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paper_title: Creating an ambient-intelligence environment using embedded agents

paper_content:

The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.
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Title: A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Section 1: Introduction
Description 1: Write an introduction to the paper, explaining the concept of Demand Side Management and its advantages. Also, introduce the scope and structure of the survey.

Section 2: Main Objectives of DR
Description 2: Summarize the main objectives of applying a DR scheme, including the reduction of total power consumption, total needed power generation, and changing demand to follow the available supply.

Section 3: DR Management
Description 3: Describe the implementation of DR methods, focusing on the control of customer power consumption behavior and the cooperation of main participants in a DR program.

Section 4: DR Applicability
Description 4: Discuss the applicability of DR programs to different types of consumers, such as residential, commercial, and industrial sectors.

Section 5: DR Communication Requirements
Description 5: Detail the communication infrastructure requirements for the effective and reliable operation of DR programs in smart grids.

Section 6: Adversative Conditions in DR Implementation
Description 6: Outline potential issues and adverse conditions that can affect the success of DR programs, such as Cold Load PickUp and voltage violations.

Section 7: Classification of DR Models
Description 7: Classify DR schemes from the literature based on control mechanisms, motivations offered to consumers, and decision variables. Include detailed subsections on each classification.

Section 8: Optimization Methods in DR Programs
Description 8: Review work on optimization methods proposed for DR programs, categorized by the target of the optimization procedure, such as minimization of electricity cost, maximization of social welfare, and minimization of aggregated power consumption.

Section 9: Application of Game Theory to DR Programs
Description 9: Discuss the application of game-theoretic methods to DR programs for optimal decision-making and resource management.

Section 10: Conclusion: Lessons Learned and Future Directions
Description 10: Summarize key lessons learned from existing DR programs and identify future research directions to improve efficiency, scalability, and reliability in smart grid environments.