A Survey on Energy Trading in Smart Grid
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paper_title: The Outlook for Energy: A View to 2040

paper_content:

Abstract The world's population is expected to rise by 25% to nearly 9 billion people by 2040. The global energy demand is expected to rise by about 35% from 2010 to 2040. Natural gas is the fastest growing major fuel source with demand expected to rise by over 65% from 2010 to 2040. In 2000 unconventional gas was a 'niche market', Asia Pacific has about 73% of the global LNG trade and the business climate and infrastructure access fostered North American and Australian gas development. By 2010 unconventional production reaches almost 10% of global supplies and the global LNG trade has doubled with Asia Pacific accounting for roughly 60%. The global unconventional gas development will continue and the Asia market growth will dominate however there are still a lot of unknowns. Keywords: Use by Sector • Use by Country • Electricity Demand • Electricity Generation Fuel Consumption Presented at: 2013 South East Asia Petroleum Exploration Society (SEAPEX) Conference, Singapore, 2013
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paper_title: Microgrids: Energy management by strategic deployment of DERs—A comprehensive survey

paper_content:

Present paper reports the survey on existing research literatures in connection with various energy management issues/benefits of a microgrid arising due to strategic deployment of its DERs. Survey on regulatory issues includes various barriers, incentives, standards (IEEE 1547, UL-1741, etc.), environmental issues, ancillary services and metering. Economic benefits, like improvement of bus voltages, line loss reduction, deferral of upgrade, waste heat utilization, reduction of customer interruption cost (CIC), ancillary services, emission reduction, fuel cost minimization, etc. have been surveyed from many researchers’ literatures in relation to methods of analyses, algorithms used and their quantification. A brief survey of researchers’ opinion on DER technologies in their respective economic analysis has also been included. This paper also reviews the researches and studies on tariff structure, market strategy of microgrid energy (both electric and thermal).
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paper_title: EV charging stations and modes: International standards

paper_content:

In recent years, Electric vehicles (EVs) are receiving significant attention as an environmental-sustainable and cost-effective substitute of vehicles with internal combustion engine, for the solution of the dependence from fossil fuels and for the saving of Green-House Gasses emission The present paper deals with an overview on different types of EVs charging stations and a comparison between the related European and American Standards. The work includes also a summary on possible types of Energy Storage Systems (ESSs), that are important for the integration of EVs fast charging stations of the last generation in smart grids. Finally a brief analysis on the possible electrical layout for the ESS integration in EVs charging system, proposed in literature, is reported.
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paper_title: Microgrids: Energy management by strategic deployment of DERs—A comprehensive survey

paper_content:

Present paper reports the survey on existing research literatures in connection with various energy management issues/benefits of a microgrid arising due to strategic deployment of its DERs. Survey on regulatory issues includes various barriers, incentives, standards (IEEE 1547, UL-1741, etc.), environmental issues, ancillary services and metering. Economic benefits, like improvement of bus voltages, line loss reduction, deferral of upgrade, waste heat utilization, reduction of customer interruption cost (CIC), ancillary services, emission reduction, fuel cost minimization, etc. have been surveyed from many researchers’ literatures in relation to methods of analyses, algorithms used and their quantification. A brief survey of researchers’ opinion on DER technologies in their respective economic analysis has also been included. This paper also reviews the researches and studies on tariff structure, market strategy of microgrid energy (both electric and thermal).
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paper_title: A Stochastic Power Network Calculus for Integrating Renewable Energy Sources into the Power Grid

paper_content:

Renewable energy such as solar and wind generation will constitute an important part of the future grid. As the availability of renewable sources may not match the load, energy storage is essential for grid stability. In this paper we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid by also accounting for energy storage. To deal with the fluctuation in both the power supply and demand, we extend and apply stochastic network calculus to analyze the power supply reliability with various renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. In particular, we asses the power supply reliability in terms of the average Fraction of Time that energy is Not-Served (FTNS).
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paper_title: Prioritizing Consumers in Smart Grid: A Game Theoretic Approach

paper_content:

This paper proposes an energy management technique for a consumer-to-grid system in smart grid. The benefit to consumers is made the primary concern to encourage consumers to participate voluntarily in energy trading with the central power station (CPS) in situations of energy deficiency. A novel system model motivating energy trading under the goal of social optimality is proposed. A single-leader multiple-follower Stackelberg game is then studied to model the interactions between the CPS and a number of energy consumers (ECs), and to find optimal distributed solutions for the optimization problem based on the system model. The CPS is considered as a leader seeking to minimize its total cost of buying energy from the ECs, and the ECs are the followers who decide on how much energy they will sell to the CPS for maximizing their utilities. It is shown that the game, which can be implemented distributedly, possesses a socially optimal solution, in which the sum of the benefits to all consumers is maximized, as the total cost to the CPS is minimized. Numerical analysis confirms the effectiveness of the game.
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paper_title: Trading agents for the smart electricity grid

paper_content:

The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
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paper_title: Economics of Electric Vehicle Charging: A Game Theoretic Approach

paper_content:

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: Microgrids energy trading in islanding mode

paper_content:

In this paper, we address the case where two microgrids are isolated from the main power grid but can exchange energy with each other in a peer-to-peer (P2P) manner. The goal is to minimize the total cost resulting from energy generation and transportation, while each microgrid satisfies its local power demand. We first propose a centralized solution. In this approach, a central controller must have access to all the information. For those situations where privacy must be protected, we propose a distributed approach, in which each microgrid solves a local optimization problem in an iterative fashion by exchanging some information with the other one.
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paper_title: Stochastic Information Management in Smart Grid

paper_content:

Rising concerns about the efficiency, reliability, economics, and sustainability in electricity production and distribution have been driving an evolution of the traditional electric power grid toward smart grid. A key enabler of the smart grid is the two-way communications throughout the power system, based on which an advanced information system can make optimal decisions on power system operation. Due to the expected deep penetration of renewable energy sources, energy storage devices, demand side management (DSM) tools, and electric vehicles (EVs) in the future smart grid, there exist significant technical challenges on power system planning and operation. Specifically, efficient stochastic information management schemes should be developed to address the randomness in renewable power generation, buffering effect of energy storage devices, consumer behavior patterns in the context of DSM, and high mobility of EVs. In this paper, we provide a comprehensive literature survey on the stochastic information management schemes for the smart grid. We start this survey with an introduction to the smart grid system architecture and the technical challenges in information management. Various component-level modeling techniques are presented to characterize the sources of randomness in the smart grid. Built upon the component-level models, we further explore the system-level stochastic information management schemes for smart grid planning and operation. Future research directions and open research issues are identified.
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paper_title: Prioritizing Consumers in Smart Grid: A Game Theoretic Approach

paper_content:

This paper proposes an energy management technique for a consumer-to-grid system in smart grid. The benefit to consumers is made the primary concern to encourage consumers to participate voluntarily in energy trading with the central power station (CPS) in situations of energy deficiency. A novel system model motivating energy trading under the goal of social optimality is proposed. A single-leader multiple-follower Stackelberg game is then studied to model the interactions between the CPS and a number of energy consumers (ECs), and to find optimal distributed solutions for the optimization problem based on the system model. The CPS is considered as a leader seeking to minimize its total cost of buying energy from the ECs, and the ECs are the followers who decide on how much energy they will sell to the CPS for maximizing their utilities. It is shown that the game, which can be implemented distributedly, possesses a socially optimal solution, in which the sum of the benefits to all consumers is maximized, as the total cost to the CPS is minimized. Numerical analysis confirms the effectiveness of the game.
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paper_title: Smart vehicles in the smart grid: challenges, trends, and application to the design of charging stations

paper_content:

Future “smart electric vehicles,” expected to evolve from emerging electric and plug-in hybrid electric vehicles (EV & PHEV) are becoming increasingly attractive. However, the current electric grid is not considered capable of handling the power demand increase required by a large number of charging stations, especially during peak loads. Furthermore, the envisioned critical infrastructure for such vehicles must include the capability for information exchange involving energy availability, distances, congestion levels and possibly, spot prices or priority incentives. In this chapter, we discuss current trends and challenges in this fascinating and rapidly developing area of research. Our emphasis is on topics related to control, demand-response, infrastructure provisioning, and the communications framework necessary to accomplish all of these “smart” features. As a particular application and a form of case study, we zero in on the design and development of charging stations. We describe a candidate PHEV charging station architecture, and a quantitative stochastic model that allows the analysis of its performance, using queuing theory and economics. The architecture we envision has the capability to store excess power obtained from the grid. Our goal is to promote a general architecture able to sustain grid stability, while providing a required level of quality of service; and to further the development of a general methodology to analyze the performance of such stations with respect to traffic characteristics, energy storage size, pricing and cost parameters.
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paper_title: Application of STATCOM with energy storage for wind farm integration

paper_content:

The application of a STATCOM and Battery Energy Storage System (BESS) can help alleviate some of the problems encountered with wind farm integration to the existing power system. The intent of this paper is to demonstrate the benefits obtained with STATCOM and BESS for this purpose. First, the paper identifies the power quality and some other issues in the Southern California Edison system where there is abundant wind generation. Computer simulations (e.g. load flow and transient stability) show that the application of a STATCOM with BESS can help the system survive under the most severe contingencies occurring in the area. The analysis also demonstrates that the BESS can help dispatch an individual wind farm in the area during steady state. The BESS can help reduce local wind generation curtailment, necessary during high generation and low local system load that causes transmission overloading, by absorbing the excess energy generated by the wind farms.
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paper_title: Trading agents for the smart electricity grid

paper_content:

The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
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paper_title: Adaptive Negotiation Agent for Facilitating Bi-Directional Energy Trading Between Smart Building and Utility Grid

paper_content:

Smart and green buildings have attracted much attention in recent years. Development of an effective negotiation model for facilitating the bi-directional energy trading between the utility grid and the building is important for enhancing the building intelligence. In this paper, a negotiation agent based on adaptive attitude bidding strategy (AABS) is proposed. A comprehensive set of factors for the integrated smart building and utility grid system is taken into account in developing the negotiation model. The AABS based negotiation agent turns out to be able to dynamically adjust its behavior in response to varying attitudes in the negotiation process. In addition, an improved particle swarm optimization-adaptive attitude bidding strategy (PSO-AABS) based negotiation agent is developed for adaptively adjusting the trader's decisions according to the opponent's behaviors. It turns out to be capable of making rational deals in bi-directional energy trading by maximizing the trader's payoffs with reduced negotiation time. The feasibility of the proposed negotiation agents is evaluated by the simulation results.
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paper_title: Vehicle-to-Aggregator Interaction Game

paper_content:

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: The Role of Energy Storage in Development of Smart Grids

paper_content:

The adoption of Smart Grid devices throughout utility networks will effect tremendous change in grid operations and usage of electricity over the next two decades. The changes in ways to control loads, coupled with increased penetration of renewable energy sources, offer a new set of challenges in balancing consumption and generation. Increased deployment of energy storage devices in the distribution grid will help make this process happen more effectively and improve system performance. This paper addresses the new types of storage being utilized for grid support and the ways they are integrated into the grid.
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paper_title: Local energy storage sizing in plug-in hybrid electric vehicle charging stations under blocking probability constraints

paper_content:

Plug-in hybrid electric vehicles (PHEV) are becoming gradually more attractive than internal combustion engine vehicles, even though the current electrical grid is not potentially able to support the required power demand increase to introduce charging stations. Acknowledging that design and development of charging stations has crucial importance, this paper introduces a candidate PHEV charging station architecture, along with a quantitative stochastic model, that allows us to analyze the performance of the system by using arguments from queuing theory and economics. A relevant component of the proposed architecture is the capability of the charging stations to store excess power obtained from the grid. The goal is to design a general architecture which will be able to sustain grid stability, while providing a required level of quality of service; and to describe a general methodology to analyze the performance of such stations with respect to the traffic characteristics, energy storage size, pricing and cost parameters. Our results indicate that significant gains in net cost/profit and useful insights can be made with the right choice of storage size. Such considerations are crucial in this early stage of designing the smart grid and charging stations of the future.
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paper_title: Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid

paper_content:

Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.
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paper_title: A noncooperative game for double auction-based energy trading between PHEVs and distribution grids

paper_content:

Plug-in hybrid electric vehicles (PHEVs) will constitute a key element in the emerging smart grid system. In this paper, the complex decision making processes of a number of PHEV groups seeking to sell part of their stored energy in a power market are studied using noncooperative games and double auctions. In particular, a noncooperative game is formulated between the PHEV groups. In this game, each PHEV group can make a decision on the maximum amount of energy surplus that it is willing to sell so as to maximize a utility function that captures the tradeoff between the economical benefits from energy trading and the associated costs. The trading price governing the energy exchange market between the PHEVs and the smart grid network is determined using a strategy-proof double auction. For solving the game, an algorithm based on best response dynamics is proposed using which the PHEV groups can reach a Nash equilibrium point. Simulation results show how our approach allows the PHEV groups to act strategically while improving the average utility achieved per PHEV group of up to 100.2% relative to a greedy algorithm.
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paper_title: Prioritizing Consumers in Smart Grid: A Game Theoretic Approach

paper_content:

This paper proposes an energy management technique for a consumer-to-grid system in smart grid. The benefit to consumers is made the primary concern to encourage consumers to participate voluntarily in energy trading with the central power station (CPS) in situations of energy deficiency. A novel system model motivating energy trading under the goal of social optimality is proposed. A single-leader multiple-follower Stackelberg game is then studied to model the interactions between the CPS and a number of energy consumers (ECs), and to find optimal distributed solutions for the optimization problem based on the system model. The CPS is considered as a leader seeking to minimize its total cost of buying energy from the ECs, and the ECs are the followers who decide on how much energy they will sell to the CPS for maximizing their utilities. It is shown that the game, which can be implemented distributedly, possesses a socially optimal solution, in which the sum of the benefits to all consumers is maximized, as the total cost to the CPS is minimized. Numerical analysis confirms the effectiveness of the game.
---
paper_title: Trading agents for the smart electricity grid

paper_content:

The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
---
paper_title: Economics of Electric Vehicle Charging: A Game Theoretic Approach

paper_content:

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.
---
paper_title: Microgrids energy trading in islanding mode

paper_content:

In this paper, we address the case where two microgrids are isolated from the main power grid but can exchange energy with each other in a peer-to-peer (P2P) manner. The goal is to minimize the total cost resulting from energy generation and transportation, while each microgrid satisfies its local power demand. We first propose a centralized solution. In this approach, a central controller must have access to all the information. For those situations where privacy must be protected, we propose a distributed approach, in which each microgrid solves a local optimization problem in an iterative fashion by exchanging some information with the other one.
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paper_title: Optimal energy management for cooperative microgrids with renewable energy resources

paper_content:

In this paper, we present an optimal energy management framework for a cooperative network of heterogeneous microgrids (MGs) where energy exchange among connected MGs is allowed to exploit the fluctuations of stochastic energy sources and demands. A multi-objective function is introduced that seeks to achieve an efficient tradeoff between low operation cost and good energy service for customers. The objective function captures the total cost of power exchange with the main grid, the startup and shutdown costs, the operating cost of distributed generators (DGs), the payment for demand response load, the penalty costs for involuntary load curtailment, and renewable energy spillage. We propose to employ the scenario-based two-stage stochastic optimization approach to deal with the uncertainties of renewable energy resources and load demand in the energy scheduling problem. The efficacy of the proposed energy management solution is demonstrated via numerical results.
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paper_title: A Game-Theoretic Approach to Energy Trading in the Smart Grid

paper_content:

Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as plug-in hybrid electric vehicles, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.
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paper_title: An energy market for trading electricity in smart grid neighbourhoods

paper_content:

The smart grid vision relies on active interaction with all of its stakeholders. As consumers are acquiring energy generation capabilities, hence becoming prosumers (producers and consumers), a meaningful way to interact among them would be to trade over a marketplace. Market-driven interactions have been proposed as a promising potential interaction method due to the monetary incentives and other benefits involved for the participants [1]. In the Internet era an on-line marketplace is an thriving concept as it overcomes potential accessibility issues, however it is not clear how they should be structured, operated, what their limits and benefits might be. The design, implementation, modus-operandi as well as the assessment of such an energy market place for smart grid neighbourhoods is presented.
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paper_title: An electricity trade model for microgrid communities in smart grid

paper_content:

Distributed microgrid network is the major trend of future smart grid, which contains various kinds of renewable power generation centers and a small group of energy users. In the distributed power system, each microgrid acts as a “prosumer” (producer and consumer) and maximizes its own social welfare. In addition, different microgrids can interact among each other through trading over a marketplace. In this paper, two models are introduced for microgrids to deal with the welfare maximization problems. In the first model, a microgrid is considered as a closed economy group and decides the optimal power generation distribution in terms of time. In the second model, each microgrid can trade with its neighborhoods and thus achieve a welfare increase from making use of its comparative advantage on power generation during a certain period of time. For each model, an efficient solution is presented. Experimental result shows the accuracy and efficiency of our presented solutions.
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paper_title: Providing QoS guarantees to multiple classes of EVs under deterministic grid power

paper_content:

In order to push Electric Vehicles (EVs) into the mainstream, the wide deployment of charging stations that can serve multiple classes of customers (e.g. fast charge, slow charge etc.) and provide a certain level of Quality of Service (QoS) is required. However, the operation of the power grid becoming more strenuous due to the addition of new large loads represented by EVs. Hence in this paper we propose a control and resource provisioning framework that can alleviate the strain on the power grid. We propose two design problems; first one considers a charging station located in a big metropolitan with a large and highly stochastic EV demand. For this case, we propose a pricing based control mechanism to maximize the total aggregated utility by controlling the arrival rates. Second case provides a capacity planning framework for stations located in small cities where arrival rates can be obtained via profiling studies. At each model, station draws a constant power from the grid and provides QoS guarantees, namely blocking probability, to each class. Hence total stochastic demand is replaced with a deterministic one, by sacrificing to reject a very few percentage of customers. Our results indicate that significant gains can be obtained with the proposed model.
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paper_title: Adaptive Negotiation Agent for Facilitating Bi-Directional Energy Trading Between Smart Building and Utility Grid

paper_content:

Smart and green buildings have attracted much attention in recent years. Development of an effective negotiation model for facilitating the bi-directional energy trading between the utility grid and the building is important for enhancing the building intelligence. In this paper, a negotiation agent based on adaptive attitude bidding strategy (AABS) is proposed. A comprehensive set of factors for the integrated smart building and utility grid system is taken into account in developing the negotiation model. The AABS based negotiation agent turns out to be able to dynamically adjust its behavior in response to varying attitudes in the negotiation process. In addition, an improved particle swarm optimization-adaptive attitude bidding strategy (PSO-AABS) based negotiation agent is developed for adaptively adjusting the trader's decisions according to the opponent's behaviors. It turns out to be capable of making rational deals in bi-directional energy trading by maximizing the trader's payoffs with reduced negotiation time. The feasibility of the proposed negotiation agents is evaluated by the simulation results.
---
paper_title: Vehicle-to-Aggregator Interaction Game

paper_content:

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: An Intelligent Auction Scheme for Smart Grid Market Using a Hybrid Immune Algorithm

paper_content:

Smart Grid technology is recognized as a key component of the solution to challenges such as the increasing electric demand, an aging utility infrastructure and workforce, and the environmental impact of greenhouse gases produced during electric generation. This paper presents the application of a hybrid optimization algorithm for distributed energy resource (DER) management in Smart Grid operation. The approach emphasizes the advantages of using multiagent systems for profitable operation of a Smart Grid in the energy market. The trading strategy adopted for the auction process is a profit-maximizing adaptive bidding strategy based on risk and competitive equilibrium price prediction. The auctioneer manages the usage of DERs by receiving bids from buyers and asks from sellers. A hybrid-immune-system-based particle swarm optimization is used to minimize the fuel cost for generation assuming realistic market prices for power, distributed generator bids reflecting realistic operational costs, and load bids customized according to the consumers' priorities. The simulation results clearly indicate that the agent-based management is effective in coordinating the various DERs economically and profitably.
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paper_title: A multi-layer market for vehicle-to-grid energy trading in the smart grid

paper_content:

In this paper, we propose a novel multi-layer market for analyzing the energy exchange process between electric vehicles and the smart grid. The proposed market consists essentially of two layers: a macro layer and a micro layer. At the macro layer, we propose a double auction mechanism using which the aggregators, acting as sellers, and the smart grid elements, acting as buyers, interact so as to trade energy. We show that this double auction mechanism is strategy-proof and converges asymptotically. At the micro layer, the aggregators, which are the sellers in the macro layer, are given monetary incentives so as to sell the energy of associated plug-in hybrid electric vehicles (PHEVs) and to maximize their revenues. We analyze the interaction between the macro and micro layers and study some representative cases. Depending on the elasticity of the supply and demand, the utility functions are analyzed under different scenarios. Simulation results show that the proposed approach can significantly increase the utility of PHEVs, compared to a classical greedy approach.
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paper_title: Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services

paper_content:

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: Energy trading in the smart grid: From end-user's perspective

paper_content:

The smart grid is expected to be the next generation of electricity grid. It will enable numerous revolutionary features, that allow end-users, such as homes, communities, or businesses, to harvest renewable energy and store the energy in a local battery, which could act as a microgeneration unit. In addition, the customers could be provided with dynamic electricity pricing options under which the electricity price adjusts over a relatively small time scale, e.g., every hour, in response to the changing energy usage of the grid. Consequently, similar to the stock market, it presents an opportunity for an end-user to make profit by trading energy with the grid. More specifically, in this paper, we investigate the profit maximization problem for an end-user that is equipped with renewable energy harvesting devices and a battery, such that the user can buy/sell the energy from/to the grid by leveraging the varying price and the battery storage ability. The resulting algorithm performs arbitrarily close to the optimum without requiring future information of energy demands, electricity prices and the renewable energy arrival process. We validate our results through trace driven simulations.
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paper_title: Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies

paper_content:

Electric vehicles (EVs) are regarded as one of the most effective tools to reduce the oil demands and gas emissions. And they are welcome in the near future for general road transportation. When EVs are connected to the power grid for charging and/or discharging, they become gridable EVs (GEVs). These GEVs will bring a great impact to our society and thus human life. This paper investigates and discusses the opportunities and challenges of GEVs connecting with the grid, namely, the vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G) technologies. The key is to provide the methodologies, approaches, and foresights for the emerging technologies of V2H, V2V, and V2G.
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paper_title: Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services

paper_content:

Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks.
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paper_title: Decentralized control of electric vehicles in a network of fast charging stations

paper_content:

To facilitate the adoption of electric vehicles (EVs) and their plug-in hybrid (PHEVs) counterparts and to avoid straining the capacity of the power grid there is a strong need for developing a network of fast charging facilities and coordinate their service. Incorporation of EVs in the vehicle fleet would decrease green house gas emissions and overall dependency on fossil fuels. A key issue in charging EVs is that the corresponding time is fairly large, which can lead to very long delays. Hence, for the network of charging stations to provide good quality of service to customers, we first propose an admission control mechanism based on pricing for a single charging station. Subsequently, we develop a decentralized routing scheme of EV drivers, employing a game theoretic model. The latter entices drivers through price incentives to require charging from less busy stations, thus leading to a more efficient utilization of power across the network, while it enhances profit for the charging facilities operator. Of note, the proposed scheme does not require advanced monitoring tools for power usage and pricing calculations. The drivers receive and send back the necessary information through the a communications infrastructure and the routing is initiated only when the network has exceeded a critical threshold. The numerical results illustrate the discussed benefits of the proposed scheme.
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paper_title: Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid

paper_content:

Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.
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paper_title: A noncooperative game for double auction-based energy trading between PHEVs and distribution grids

paper_content:

Plug-in hybrid electric vehicles (PHEVs) will constitute a key element in the emerging smart grid system. In this paper, the complex decision making processes of a number of PHEV groups seeking to sell part of their stored energy in a power market are studied using noncooperative games and double auctions. In particular, a noncooperative game is formulated between the PHEV groups. In this game, each PHEV group can make a decision on the maximum amount of energy surplus that it is willing to sell so as to maximize a utility function that captures the tradeoff between the economical benefits from energy trading and the associated costs. The trading price governing the energy exchange market between the PHEVs and the smart grid network is determined using a strategy-proof double auction. For solving the game, an algorithm based on best response dynamics is proposed using which the PHEV groups can reach a Nash equilibrium point. Simulation results show how our approach allows the PHEV groups to act strategically while improving the average utility achieved per PHEV group of up to 100.2% relative to a greedy algorithm.
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paper_title: Prioritizing Consumers in Smart Grid: A Game Theoretic Approach

paper_content:

This paper proposes an energy management technique for a consumer-to-grid system in smart grid. The benefit to consumers is made the primary concern to encourage consumers to participate voluntarily in energy trading with the central power station (CPS) in situations of energy deficiency. A novel system model motivating energy trading under the goal of social optimality is proposed. A single-leader multiple-follower Stackelberg game is then studied to model the interactions between the CPS and a number of energy consumers (ECs), and to find optimal distributed solutions for the optimization problem based on the system model. The CPS is considered as a leader seeking to minimize its total cost of buying energy from the ECs, and the ECs are the followers who decide on how much energy they will sell to the CPS for maximizing their utilities. It is shown that the game, which can be implemented distributedly, possesses a socially optimal solution, in which the sum of the benefits to all consumers is maximized, as the total cost to the CPS is minimized. Numerical analysis confirms the effectiveness of the game.
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paper_title: Trading agents for the smart electricity grid

paper_content:

The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
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paper_title: Economics of Electric Vehicle Charging: A Game Theoretic Approach

paper_content:

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: A Game-Theoretic Approach to Energy Trading in the Smart Grid

paper_content:

Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as plug-in hybrid electric vehicles, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.
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paper_title: An energy market for trading electricity in smart grid neighbourhoods

paper_content:

The smart grid vision relies on active interaction with all of its stakeholders. As consumers are acquiring energy generation capabilities, hence becoming prosumers (producers and consumers), a meaningful way to interact among them would be to trade over a marketplace. Market-driven interactions have been proposed as a promising potential interaction method due to the monetary incentives and other benefits involved for the participants [1]. In the Internet era an on-line marketplace is an thriving concept as it overcomes potential accessibility issues, however it is not clear how they should be structured, operated, what their limits and benefits might be. The design, implementation, modus-operandi as well as the assessment of such an energy market place for smart grid neighbourhoods is presented.
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paper_title: Vehicle-to-Aggregator Interaction Game

paper_content:

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: A multi-layer market for vehicle-to-grid energy trading in the smart grid

paper_content:

In this paper, we propose a novel multi-layer market for analyzing the energy exchange process between electric vehicles and the smart grid. The proposed market consists essentially of two layers: a macro layer and a micro layer. At the macro layer, we propose a double auction mechanism using which the aggregators, acting as sellers, and the smart grid elements, acting as buyers, interact so as to trade energy. We show that this double auction mechanism is strategy-proof and converges asymptotically. At the micro layer, the aggregators, which are the sellers in the macro layer, are given monetary incentives so as to sell the energy of associated plug-in hybrid electric vehicles (PHEVs) and to maximize their revenues. We analyze the interaction between the macro and micro layers and study some representative cases. Depending on the elasticity of the supply and demand, the utility functions are analyzed under different scenarios. Simulation results show that the proposed approach can significantly increase the utility of PHEVs, compared to a classical greedy approach.
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paper_title: Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services

paper_content:

Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks.
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paper_title: Microgrids energy trading in islanding mode

paper_content:

In this paper, we address the case where two microgrids are isolated from the main power grid but can exchange energy with each other in a peer-to-peer (P2P) manner. The goal is to minimize the total cost resulting from energy generation and transportation, while each microgrid satisfies its local power demand. We first propose a centralized solution. In this approach, a central controller must have access to all the information. For those situations where privacy must be protected, we propose a distributed approach, in which each microgrid solves a local optimization problem in an iterative fashion by exchanging some information with the other one.
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paper_title: An Intelligent Auction Scheme for Smart Grid Market Using a Hybrid Immune Algorithm

paper_content:

Smart Grid technology is recognized as a key component of the solution to challenges such as the increasing electric demand, an aging utility infrastructure and workforce, and the environmental impact of greenhouse gases produced during electric generation. This paper presents the application of a hybrid optimization algorithm for distributed energy resource (DER) management in Smart Grid operation. The approach emphasizes the advantages of using multiagent systems for profitable operation of a Smart Grid in the energy market. The trading strategy adopted for the auction process is a profit-maximizing adaptive bidding strategy based on risk and competitive equilibrium price prediction. The auctioneer manages the usage of DERs by receiving bids from buyers and asks from sellers. A hybrid-immune-system-based particle swarm optimization is used to minimize the fuel cost for generation assuming realistic market prices for power, distributed generator bids reflecting realistic operational costs, and load bids customized according to the consumers' priorities. The simulation results clearly indicate that the agent-based management is effective in coordinating the various DERs economically and profitably.
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paper_title: Energy trading in the smart grid: From end-user's perspective

paper_content:

The smart grid is expected to be the next generation of electricity grid. It will enable numerous revolutionary features, that allow end-users, such as homes, communities, or businesses, to harvest renewable energy and store the energy in a local battery, which could act as a microgeneration unit. In addition, the customers could be provided with dynamic electricity pricing options under which the electricity price adjusts over a relatively small time scale, e.g., every hour, in response to the changing energy usage of the grid. Consequently, similar to the stock market, it presents an opportunity for an end-user to make profit by trading energy with the grid. More specifically, in this paper, we investigate the profit maximization problem for an end-user that is equipped with renewable energy harvesting devices and a battery, such that the user can buy/sell the energy from/to the grid by leveraging the varying price and the battery storage ability. The resulting algorithm performs arbitrarily close to the optimum without requiring future information of energy demands, electricity prices and the renewable energy arrival process. We validate our results through trace driven simulations.
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paper_title: Strategy Learning for Autonomous Agents in Smart Grid Markets

paper_content:

Distributed electricity producers, such as small wind farms and solar installations, pose several technical and economic challenges in Smart Grid design. One approach to addressing these challenges is through Broker Agents who buy electricity from distributed producers, and also sell electricity to consumers, via a Tariff Market-a new market mechanism where Broker Agents publish concurrent bid and ask prices. We investigate the learning of pricing strategies for an autonomous Broker Agent to profitably participate in a Tariff Market. We employ Markov Decision Processes (MDPs) and reinforcement learning. An important concern with this method is that even simple representations of the problem domain result in very large numbers of states in the MDP formulation because market prices can take nearly arbitrary real values. In this paper, we present the use of derived state space features, computed using statistics on Tariff Market prices and Broker Agent customer portfolios, to obtain a scalable state representation. We also contribute a set of pricing tactics that form building blocks in the learned Broker Agent strategy. We further present a Tariff Market simulation model based on real-world data and anticipated market dynamics. We use this model to obtain experimental results that show the learned strategy performing vastly better than a random strategy and significantly better than two other non-learning strategies.
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paper_title: Autonomous Data-Driven decision-making in smart electricity markets

paper_content:

For the vision of a Smart Grid to materialize, substantial advances in intelligent decentralized control mechanisms are required. We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies. Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting from these signals. Our design is the first that can accommodate an offline training phase so as to automatically optimize the broker for particular market conditions. We demonstrate the performance of our design in a series of experiments using real-world energy market data, and find that it outperforms previous approaches by a significant margin.
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paper_title: Learned Behaviors of Multiple Autonomous Agents in Smart Grid Markets

paper_content:

One proposed approach to managing a large complex Smart Grid is through Broker Agents who buy electrical power from distributed producers, and also sell power to consumers, via a Tariff Market—a new market mechanism where Broker Agents publish concurrent bid and ask prices. A key challenge is the specification of the market strategy that the Broker Agents should use in order to earn profits while maintaining the market's balance of supply and demand. Interestingly, previous work has shown that a Broker Agent can learn its strategy, using Markov Decision Processes (MDPs) and Q-learning, and outperform other Broker Agents that use predetermined or randomized strategies. In this work, we investigate the more representative scenario in which multiple Broker Agents, instead of a single one, are independently learning their strategies. Using a simulation environment based on real data, we find that Broker Agents who employ periodic increases in exploration achieve higher rewards. We also find that varying levels of market dominance in customer allocation models result in remarkably distinct outcomes in market prices and aggregate Broker Agent rewards. The latter set of results can be explained by established economic principles regarding the emergence of monopolies in market-based competition, further validating our approach.
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paper_title: Agentbased micro-storage management for the smart grid

paper_content:

The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, requires more generation capacity, results in more carbon emissions, and, in the worst case, breaks down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
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paper_title: Evaluation of Community-Based Electric Power Market with Agent-Based Simulation

paper_content:

Electric power trading has been adopted in many countries. Moreover, 33% of the electricity market has been moved to the free competitive market already. In this paper, we focus on community-based electricity markets. Community can be defined as a domestic group of individual homes. We assume a community can have a larger battery that can be shared by individual homes. In the real world, community-based battery sharing has been focused on as a useful electricity buffer in crisis situations and also for stabilizing usage of electricity in a domestic area. In this paper, we build several domestic electric power management agent models by using Japanese power consumption data and meteorological data. We evaluate community-based electricity markets by using agent-based simulation, in which the validity of the large-sized storage battery in a community is confirmed regarding the load mitigation to a power network.
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Title: A Survey on Energy Trading in Smart Grid
Section 1: Motivation
Description 1: This section discusses the need for energy trading in smart grids, driven by the increasing global energy demand and the potential benefits to both utilities and consumers.

Section 2: Benefits
Description 2: This section elaborates on the multiple advantages of energy trading in smart grids, including improved system efficiency, reduced operation costs, reduction in greenhouse gas emissions, and enhanced energy profiling.

Section 3: Enabling Technologies & Models
Description 3: This section reviews the key technologies and models that facilitate energy trading in smart grids, such as distributed renewable generation, energy storage systems, electric vehicles, and two-way communication networks.

Section 4: Required Frameworks
Description 4: This section classifies and describes the various mathematical and simulation-based frameworks used in energy trading, including decentralized solutions, centralized solutions, and simulation-based solutions.

Section 5: Conclusitons
Description 5: This section provides an overview of the growing interest in energy trading within smart grids, summarizing the technologies and frameworks discussed, and highlighting the paper's contributions.