Charging Electric Vehicles in the Smart City: A Survey of Economy-driven Approaches
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paper_title: The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook

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Plug-in hybrid electric vehicles (PHEVs) are the next big thing in the electric transportation market. While much work has been done to detail what economic costs and benefits PHEVs will have on consumers and producers alike, it seems that it is also important to understand what impact PHEVs will have on distribution networks nationwide. This paper finds that the impact of PHEVs on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration. The impacts that these aspects of PHEVs will have on distribution networks have been measured and calculated by multiple authors in different locations using many different tools that range from analytical techniques to simulations and beyond. While much work has already been completed in this area, there is still much to do. Areas left for improvement and future work will include adding more stochasticity into models as well as computing and analyzing reliability indices with respect to distribution networks.
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paper_title: Impacts of Electric Vehicle charging under electricity market operations

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The Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. In this study the effects of 213,561 electric vehicles on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is investigated. A model of Ireland’s electricity market in 2020 is developed using the power systems market model called PLEXOS for power systems. The amount of CO2 emissions associated with charging the EVs and the impacts with respect to Ireland’s target for renewable energy in transport is also quantified. A single generation portfolio and two different charging scenarios, arising from a peak and off-peak charging profile are considered. Results from the study confirm that off-peak charging is more beneficial than peak charging and that charging EVs will contribute 1.45% energy supply to the 10% renewable energy in transport target. The net CO2 reductions are 147 and 210 kt CO2 respectively.
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paper_title: Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems

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Despite the economic and environmental advantages of plug-in hybrid electric vehicles (PHEVs), the increased utilization of PHEVs brings up new concerns for power distribution system decision makers. Impacts of PHEVs on distribution networks, although have been proven to be noticeable, have not been thoroughly investigated for future years. In this paper, a comprehensive model is proposed to study the PHEV impacts on residential distribution systems. In so doing, PHEV fundamental characteristics, i.e., PHEV battery capacity, PHEV state of charge (SOC), and PHEV energy consumption in daily trips, are accurately modeled. As some of these effective characteristics depend on vehicle owner's behavior, their behavior and interests are considered in the proposed model. Also, to get a more practical model of PHEVs, the number of vehicles in a residential distribution network, the PHEV penetration level for upcoming years, distribution of PHEVs in the network, and estimation of household load growth for upcoming years are extracted from related published reports. The proposed model is applied to the IEEE 34-node test feeder, and PHEV impacts on residential distribution network are studied in different time horizons. A sensitivity analysis is also performed to demonstrate the effects of PHEV operation modes on the network load profile.
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paper_title: A novel approach for evaluating the impact of electric vehicles on the power distribution system

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this paper presents a useful and practical approach for assessing the impact of electric vehicles on the electric power distribution system while keeping to a manageable volume of detailed circuit and equipment studies. This paper addresses electric vehicles as an electric load only and does not consider the potential of the vehicle being a generation source. It also only addresses the substation, primary and distribution transformer impacts. It does not consider impacts on the secondary system. The method uses a statistical clustering algorithm to identify a set of representative feeders for the electric utility system, each representing a certain subset of the utility feeder system. This set of representative feeders is analyzed over a series of electric vehicle market penetration scenarios from 0 to 100%, in order to determine the expected impacts on loading, losses, voltage and reliability performance and power quality that each feeder will see. Then, distribution system capacity upgrades that are required to serve uncontrolled EV loads in each area, as well as the benefits that could if EV loads were limited or re-scheduled via demand response are assessed for each. Results from these representative feeder scenario studies are then used to estimate the impacts, control and mitigation needed, and performance expected across the entire utility feeder set. This method has been used to plan for increasing amounts of electric vehicle loads on several utility systems in the USA. Results from one case study are discussed.
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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: 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: Vehicular Networks for a Greener Environment: A Survey

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Researchers are looking for solutions that save the environment and money. Vehicular ad-hoc networks (VANETs) offer promising technology for safety communications. Thus, researchers try to integrate certain applications into existing research. The current survey critically examines the use of vehicular communication networks to provide green solutions. We discuss the current implementations of technology and provide a comparison from the communication perspective. This paper is meant to motivate researchers to investigate a new direction in which a network of vehicles is used to enhance total fuel and power consumption, gas emissions, and-as a result-budgets. Moreover, open issues and research directions that have only been slightly addressed, if at all, are discussed.
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paper_title: Effects of plug-in electric vehicle charge scheduling on the day-ahead electricity market price

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With the advent of plug-in electric vehicles (EV), it becomes increasingly important to study the impact of charge scheduling of a large number of EVs on the day-ahead electricity market price. In this paper, two scheduling models are proposed-joint scheduling model and aggregator scheduling model. They are used to study the effects of scheduling of EV charging on the day-ahead market price of electricity at various penetration levels of the EVs. Results from studies on an IEEE 30-bus test system show that, at lower penetration of EVs, a simple scheduling method using fixed-period charging during low demand periods can be used, without a large increase in market price. But at higher penetration levels, the opposite holds true, requiring advanced scheduling methods such as proposed in this paper. Between the two, joint scheduling model results in a lower increase in electricity price and is further tested on a simplified Nordic day-ahead market model. An overall conclusion is that the Nordic day-ahead market can accommodate large penetration of EVs without a significant increase in market price.
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paper_title: Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles

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This paper reviews the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in electric vehicles and hybrids. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical on-board chargers restrict power because of weight, space, and cost constraints. They can be integrated with the electric drive to avoid these problems. The availability of charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. Future aspects such as roadbed charging are presented. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, and other factors.
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paper_title: Comprehensive Topological Analysis of Conductive and Inductive Charging Solutions for Plug-In Electric Vehicles

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The impending global energy crisis has opened up new opportunities for the automotive industry to meet the ever-increasing demand for cleaner and fuel-efficient vehicles. This has necessitated the development of drivetrains that are either fully or partially electrified in the form of electric and plug-in hybrid electric vehicles (EVs and HEVs), respectively, which are collectively addressed as plug-in EVs (PEVs). PEVs in general are equipped with larger on-board storage and power electronics for charging or discharging the battery, in comparison with HEVs. The extent to which PEVs are adopted significantly depends on the nature of the charging solution utilized. In this paper, a comprehensive topological survey of the currently available PEV charging solutions is presented. PEV chargers based on the nature of charging (conductive or inductive), stages of conversion (integrated single stage or two stages), power level (level 1, 2, or 3), and type of semiconductor devices utilized (silicon, silicon carbide, or gallium nitride) are thoroughly reviewed in this paper.
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paper_title: Toward a wireless charging for battery electric vehicles at traffic intersections

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In near future, Battery Electric Vehicles (BEVs) will become widely accepted and used. One of the main challenges of BEVs is their limited energy capacity. Current battery technologies require BEVs to make frequent trips, in comparison to traditional refuel, to charging stations for recharging. In this paper, we envision a new scheme for charging of BEV based on wireless charging. Our scheme exploits the frequent BEVs stops at traffic intersections to charge their batteries via wireless charging device. We study how to integrate control strategy at traffic intersections for maximizing charging while minimizing waiting delays. Simulation results are provided to show the effectiveness of proposed charging schemes.
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paper_title: A review on inductive charging for electric vehicles

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The scarce supply of fossil fuel in the mere future has driven the development of electric vehicles (EV) worldwide. Plug-in connectors have been commonly proposed for EV charging, however, these systems have disadvantages such as safety, esthete, and operation in snow. Therefore, a new method to inductively charge the vehicle without any physical contact has been proposed. This paper presents a state of the art literature review on the recent advancements of Inductive Power Transfer (IPT) technology used in EV charging. A possible future technology to solve the inherent range anxiety problem is also presented using roadway electrification and in-motion power transfer concepts.
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paper_title: Review of integrated charging methods for plug-in electric and hybrid vehicles

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This paper reviews the current status and implementation of integrated charging methods for plug-in electric vehicles and hybrids. Battery charger systems are categorized into on-board and off-board types. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Typical on-board chargers limit the high power because of weight, space, volume and cost constraints. Integration of the charging function into the electric drive system and electric motor has been developed to minimize battery charger costs, weight and volume. In an integrated charger, motor windings are used for the inductors. The motor drive inverter serves as a bidirectional ac-dc converter. Various integrated charger topologies are presented, compared, and evaluated based on cost, weight, suitability, configurations, and other factors.
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paper_title: Contactless power transfer systems for On-Line Electric Vehicle (OLEV)

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In this paper, we introduced the overall On-Line Electric Vehicle (OLEV) system and two types of contactless power transfer systems in the OLEV system. Simulation and experimental results for the two types of the systems are given and prototypes of the system constructed are tested.
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paper_title: On-Line Electric Vehicle using inductive power transfer system

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In this paper, 3 generations of OLEV (On-Line Electrical Vehicle) are introduced. The 1st generation of OLEV is conventional E-type structure. The air gap of the 1st generation is 1cm and the input to output power efficiency is 80% with 3 kW output power. The ultra slim U-type mono rail structure is applied to the 2nd generation of OLEV. This structure is applied to the OLEV bus which achieves 52 kW output power with 72% efficiency at 17 cm air gap. SUV (Sports Utility Vehicle) equipped with the 3rd generation of OLEV named ultra slim W-type (dual rail structure) accomplished 15 kW/pick-up and 71% efficiency at 17 cm air gap
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paper_title: Cutting the Cord: Static and Dynamic Inductive Wireless Charging of Electric Vehicles

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In this article, we have reviewed the state of the art of IPT systems and have explored the suitability of the technology to wirelessly charge battery powered vehicles. the review shows that the IPT technology has merits for stationary charging (when the vehicle is parked), opportunity charging (when the vehicle is stopped for a short period of time, for example, at a bus stop), and dynamic charging (when the vehicle is moving along a dedicated lane equipped with an IPT system). Dynamic wireless charging holds promise to partially or completely eliminate the overnight charging through a compact network of dynamic chargers installed on the roads that would keep the vehicle batteries charged at all times, consequently reducing the range anxiety and increasing the reliability of EVs. Dynamic charging can help lower the price of EVs by reducing the size of the battery pack. Indeed, if the recharging energy is readily available, the batteries do not have to support the whole driving range but only supply power when the IPT system is not available. Depending on the power capability, the use of dynamic charging may increase driving range and reduce the size of the battery pack.
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paper_title: Impacts of Electric Vehicle charging under electricity market operations

paper_content:

The Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. In this study the effects of 213,561 electric vehicles on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is investigated. A model of Ireland’s electricity market in 2020 is developed using the power systems market model called PLEXOS for power systems. The amount of CO2 emissions associated with charging the EVs and the impacts with respect to Ireland’s target for renewable energy in transport is also quantified. A single generation portfolio and two different charging scenarios, arising from a peak and off-peak charging profile are considered. Results from the study confirm that off-peak charging is more beneficial than peak charging and that charging EVs will contribute 1.45% energy supply to the 10% renewable energy in transport target. The net CO2 reductions are 147 and 210 kt CO2 respectively.
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paper_title: The Role of Aggregators in Smart Grid Demand Response Markets

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The design of efficient Demand Response (DR) mechanisms for the residential sector entails significant challenges, due to the large number of home users and the negligible impact of each of them on the market. In this paper, we introduce a hierarchical market model for the smart grid where a set of competing aggregators act as intermediaries between the utility operator and the home users. The operator seeks to minimize the smart grid operational cost and offers rewards to aggregators toward this goal. Profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users in order to modify their preferable consumption pattern. Finally, end-users seek to optimize the tradeoff between earnings received from the aggregator and discomfort from having to modify their pattern. Based on this market model, we first address the benchmark scenario from the point of view of a cost-minimizing operator that has full information about user demands. Then, we consider a DR market, where all entities are self-interested and non-cooperative. The proposed market scheme captures the diverse objectives of the involved entities and, compared to flat pricing, guarantees significant benefits for each. Using realistic demand traces, we quantify the arising DR benefits. Interestingly, users that are extremely willing to modify their consumption pattern do not derive maximum benefit.
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paper_title: Scalable Real-Time Electric Vehicles Charging With Discrete Charging Rates

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Large penetration of electric vehicles (EVs) can have a negative impact on the power grid, e.g., increased peak load and losses, that can be largely mitigated using coordinated charging strategies. In addition to shifting the charging process to the night valley when the electricity price is lower, this paper explicitly considers the EV owner convenience that can be mainly characterized by a desired state of charge at the departure time. To this end, the EV charging procedure is defined as an uninterruptible process that happens at a given discrete charging rate and the coordinated charging is formulated as a scheduling problem. The scalable real-time greedy (S-RTG) algorithm is proposed to schedule a large population of EVs in a decentralized fashion, explicitly considering the EV owner criteria. Unlike the majority of existing approaches, the S-RTG algorithm does not rely on iterative procedures and does not require heavy computations, broadcast messages, or extensive bi-directional communications. Instead, the proposed algorithm schedules one EV at a time with simple computations, only once (i.e., at the time the EV connects to the grid), and only requires low-speed communication capability making it suitable for real-time implementation. Numerical simulations with significant EVs penetration and comparative analysis with scheduling policies demonstrate the effectiveness of the proposed algorithm.
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paper_title: Real-Time Distributed Control for Smart Electric Vehicle Chargers: From a Static to a Dynamic Study

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At high penetrations, uncontrolled electric vehicle (EV) charging has the potential to cause line and transformer congestion in the distribution network. Instead of upgrading components to higher nameplate ratings, we investigate the use of real-time control to limit EV load to the available capacity in the network. Inspired by rate control algorithms in computer networks such as TCP, we design a measurement-based, real-time, distributed, stable, efficient, and fair charging algorithm using the dual-decomposition approach. We show through extensive numerical simulations and power flow analysis on a test distribution network that this algorithm operates successfully in both static and dynamic settings, despite changes in home loads and the number of connected EVs. We find that our algorithm rapidly converges from large disturbances to a stable operating point. We show that in a test setting, for an acceptable level of overload, only 70 EVs could be fully charged without control, whereas up to around 700 EVs can be fully charged using our control algorithm. This compares well with the maximum supportable population of approximately 900 EVs. Our work also provides engineering guidelines for choosing the control parameters and setpoints in a distribution network.
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paper_title: A contract-based approach for ancillary services in V2G networks: Optimality and learning

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With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.
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paper_title: Rapid-Charging Navigation of Electric Vehicles Based on Real-Time Power Systems and Traffic Data

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Electric vehicles (EVs) have attracted growing attention in recent years. However, daily charging of EVs, in particular rapid charging, may impact power systems, especially during peak hours, and the effects may occur in different places as traffic conditions change. To address these issues, we describe an integrated rapid-charging navigation strategy that considers both the traffic conditions and the status of the power grid. The system is based on an intelligent transport system (ITS), and contains four modules: a power system control center (PSCC), an ITS center, charging stations, and EV terminals. The PSCC calculates the available charging capacity and station charging capacity based on power grid data and transmits the results to the charging stations. The charging stations determine their charging plans and estimate the available charging power for future EVs (CPFE), and transmit these data to the ITS center. After receiving CPFE data and traffic data from the ITS center, the EV terminal estimates the total time for charging (TTC) for different stations, which includes the driving time, waiting time, and charging time. The driver can view these results and choose to be navigated to the charging station corresponding to the minimum TTC. The modular design of the navigation system reduces data transmission, which protects the drivers' privacy since they can choose which charging station to use and are not required to send any data to the ITS system. Simulation results demonstrate the feasibility of the proposed method for different working conditions for power system and traffic conditions.
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paper_title: Charging games in networks of electrical vehicles

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In this paper, a static non-cooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each EV aims at choosing the time at which it starts charging its battery in order to minimize an individual cost which is mainly related to the total power delivered by the transformer, the location of the time interval over which the charging operation is performed, and the charging duration needed for the considered EV to have its battery fully recharged. As individual cost functions are assumed to be memoryless, it is possible to show that the game of interest is always an ordinal potential game. More precisely, both an atomic and nonatomic versions of the charging game are considered. In both cases, equilibrium analysis is conducted. In particular, important issues such as equilibrium uniqueness and efficiency are tackled. Interestingly, both analytical and numerical results show that the efficiency loss due to decentralization (e.g., when cost functions such as distribution network Joule losses or life of residential distribution transformers when no thermal inertia is assumed) induced by charging is small and the corresponding “efficiency”, a notion close to the Price of Anarchy, tends to one when the number of EV increases.
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paper_title: Online mechanism design for electric vehicle charging

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Plug-in hybrid electric vehicles are expected to place a considerable strain on local electricity distribution networks, requiring charging to be coordinated in order to accommodate capacity constraints. We design a novel online auction protocol for this problem, wherein vehicle owners use agents to bid for power and also state time windows in which a vehicle is available for charging. This is a multi-dimensional mechanism design domain, with owners having non-increasing marginal valuations for each subsequent unit of electricity. In our design, we couple a greedy allocation algorithm with the occasional "burning" of allocated power, leaving it unallocated, in order to adjust an allocation and achieve monotonicity and thus truthfulness. We consider two variations: burning at each time step or on-departure. Both mechanisms are evaluated in depth, using data from a real-world trial of electric vehicles in the UK to simulate system dynamics and valuations. The mechanisms provide higher allocative efficiency than a fixed price system, are almost competitive with a standard scheduling heuristic which assumes non-strategic agents, and can sustain a substantially larger number of vehicles at the same per-owner fuel cost saving than a simple random scheme.
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paper_title: Real-Time Demand Response Model

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This paper describes an optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices. The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy-consumption level, maximum and minimum hourly load levels, and ramping limits on such load levels. Price uncertainty is modeled through robust optimization techniques. The model materializes into a simple linear programming algorithm that can be easily integrated in the Energy Management System of a household or a small business. A simple bidirectional communication device between the power supplier and the consumer enables the implementation of the proposed model. Numerical simulations illustrating the interest of the proposed model are provided.
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paper_title: Developing a Test Data Set for Electric Vehicle Applications in Smart Grid Research

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We analyze a detailed set of driving traces for 536 GPS-equipped taxi vehicles and combine them with the features of four different plug-in hybrid electric vehicle (PHEV) brands that currently dominate the North American market in order to develop a test data set for PHEV-related research in the field of smart grid. Our developed data set is made available to public in [1]. It consists of various information, including but not limited to per-PHEV traces of state-of-charges (SoCs), per-PHEV traces of charging loads at different carefully identified charging stations, per-PHEV information on SoC and charging deadline when the PHEV is parked at a charging station, and some information about the potential of PHEVs for vehicle-to-grid applications.
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paper_title: Coordinated Charging of Electric Vehicles for Congestion Prevention in the Distribution Grid

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Distributed energy resources (DERs), like electric vehicles (EVs), can offer valuable services to power systems, such as enabling renewable energy to the electricity producer and providing ancillary services to the system operator. However, these new DERs may challenge the distribution grid due to insufficient capacity in peak hours. This paper aims to coordinate the valuable services and operation constraints of three actors: the EV owner, the Fleet operator (FO) and the Distribution system operator (DSO), considering the individual EV owner's driving requirement, the charging cost of EV and thermal limits of cables and transformers in the proposed market framework. Firstly, a theoretical market framework is described. Within this framework, FOs who represent their customer's (EV owners) interests will centrally guarantee the EV owners' driving requirements and procure the energy for their vehicles with lower cost. The congestion problem will be solved by a coordination between DSO and FOs through a distribution grid capacity market scheme. Then, a mathematical formulation of the market scheme is presented. Further, some case studies are shown to illustrate the effectiveness of the proposed solutions.
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paper_title: Pricing the Internet With Multibid Auctions

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Usage-based or congestion-based charging schemes have been regarded as a relevant way to control congestion and to differentiate services among users in telecommunication networks; auctioning for bandwidth appears as one of several possibilities. In a previous work, the authors designed a multibid auction scheme where users compete for bandwidth at a link by submitting several couples (e.g., amount of bandwidth asked, associated unit price) so that the link allocates the bandwidth and computes the charge according to the second price principle. They showed that incentive compatibility and efficiency among other properties are verified. We propose in the present paper to extend this scheme to the case of a network by using the properties/assumptions that the backbone network is overprovisioned and the access networks have a tree structure
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paper_title: Optimal decentralized protocol for electric vehicle charging

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Motivated by the power-grid-side challenges in the integration of electric vehicles, we propose a decentralized protocol for negotiating day-ahead charging schedules for electric vehicles. The overall goal is to shift the load due to electric vehicles to fill the overnight electricity demand valley. In each iteration of the proposed protocol, electric vehicles choose their own charging profiles for the following day according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. This protocol is guaranteed to converge, irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. At convergence, the l 2 norm of the aggregated demand is minimized, and the aggregated demand profile is as “flat” as it can possibly be. The proposed protocol needs no coordination among the electric vehicles, hence requires low communication and computation capability. Simulation results demonstrate convergence to optimal collections of charging profiles within few iterations.
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paper_title: Optimized Bidding of a EV Aggregation Agent in the Electricity Market

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An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naive forecast for all variables of the problem.
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paper_title: A Mixed-Integer Linear Programming Model for the Electric Vehicle Charging Coordination Problem in Unbalanced Electrical Distribution Systems

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This paper presents a novel mixed-integer linear programming (MILP) model for the electric vehicle charging coordination (EVCC) problem in unbalanced electrical distribution systems (EDSs). Linearization techniques are applied over a mixed-integer nonlinear programming model to obtain the proposed MILP formulation based on current injections. The expressions used to represent the steady-state operation of the EDS take into account a three-phase representation of the circuits, as well as the imbalance of the loads, leading to a more realistic model. Additionally, the proposed formulation considers the presence of distributed generators and operational constraints such as voltage and current magnitude limits. The optimal solution for the mathematical model was found using commercial MILP solvers. The proposed formulation was tested in a distribution system used in the specialized literature. The results show the efficiency and the robustness of the methodology, and also demonstrate that the model can be used in the solution of the EVCC problem in EDSs.
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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: Charging station selection optimization for plug-in electric vehicles: An oligopolistic game-theoretic framework

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In this paper, we describe a framework for the selection of the best charging station when plug-in electric vehicles (PEV) need to recharge their batteries, while at the same time the power utilities, which own the charging stations (CS), optimize their revenue. We use two-way communication to transmit positioning information, which is a key factor so that the plug-in electric vehicles evaluate its required energy. Within this framework, we also describe a procedure that is implemented in a distributed manner and is based on a non-cooperative oligopoly game that makes use of differentiated products theory and conjectural variations to provide a Nash equilibrium in prices and quantities.
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paper_title: Decentralized Charging Control of Large Populations of Plug-in Electric Vehicles

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This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand “valley”. The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation.
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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 Charging Strategy for Plug-In Electric Taxi With Time-Varying Profits

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This paper studies the optimal charging problem for future plug-in electric taxi (PET) with time-varying profits, i.e., time-varying service incomes and charging costs. Aiming at maximizing the average profit of a PET in long term under the constraint of state of charge (SoC) dynamics of PET battery, this problem is formulated as a constrained binary programming problem in infinite time horizon. The main contribution of this paper consists of three parts. First, because the original infinite binary programming problem cannot be directly solved, it is divided into a series of periodic subproblems. Each of the subproblems is in finite time horizon and much easier to solve, which is also proven rigorously to be equivalent to the original one. Second, an efficient and optimal algorithm is proposed to solve the finite time constrained binary programming subproblem, which also yields the optimal initial SoC of PET. Third, a transient control strategy is proposed to transfer the any initial SoC to the optimal one, if they are not identical. The performance of the proposed method is verified by numerical results.
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paper_title: Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

paper_content:

Abstract Electric Vehicle (EV) navigation system normally requires multiple charging in a long origin destination (OD) trip, which makes it different from the traditional vehicle navigation system. Fast charging is an ideal charging solution in multiple charging because of its high charging efficiency, rapid transient response and short charging time. However, EVs fast charging at on-peak hours may lead to overloading of distribution system. To solve the problem above, a real-time pricing (RTP) policy, as one of the main thrusts for load shaving application, should be introduced, which will bring new challenges in EV navigation systems due to the characteristic of time-dependence. In this paper, we address an optimization problem for EV navigation systems under the RTP policy with the consideration of both charging and routing. By using the EV arrival states and the traffic parameters, the original traffic network can be extended to a feasible state graph. An Improved Chrono-SPT (ICS) algorithm is provided to derive the optimal decision sequence, which provides an optimal routing and charging policy. Furthermore, a Simplify-Charge-Control (SCC) algorithm is also presented to reduce the computation complexity of the ICS algorithm. Simulations show the effectiveness of both ICS and SCC algorithms and the computation complexity of SCC algorithm is much simplified within acceptable deviation of optimal cost under approximation pricing (AP) than that in ICS algorithm.
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paper_title: Incentives in Teams

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This paper analyzes the problem of inducing the members of an organization to behave as if they formed a team. Considered is a conglomerate-type organization consisting of a set of semi-autonomous subunits that are coordinated by the organization's head. The head's incentive problem is to choose a set of employee compensation rules that will induce his subunit managers to communicate accurate information and take optimal decisions. The main result exhibits a particular set of compensation rules, an optimal incentive structure, that leads to team behavior. Particular attention is directed to the informational aspects of the problem. An extended example of a resource allocation model is discussed and the optimal incentive structure is interpreted in terms of prices charged by the head for resources allocated to the subunits.
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paper_title: Algorithmic game theory

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A new era of theoretical computer science addresses fundamental problems about auctions, networks, and human behavior.
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paper_title: Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles

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Plug-in hybrid electric vehicles (PHEVs) have received considerable recent attention for their potential to reduce petroleum consumption significantly and quickly in the transportation sector. Analysis to aid the design of such vehicles and predict their real-world performance and fuel displacement must consider the driving patterns the vehicles will typically encounter. This paper goes beyond consideration of standardized certification cycless by leveraging state-of-the-art travel survey techniques that use Global Positioning System (GPS) technology to obtain a large set of real-world drive cycles from the surveyed vehicle fleet. This study specifically extracts 24-h, second-by-second driving profiles from a set of 227 GPS-instrumented vehicles in the St. Louis, Missouri, metropolitan area. The performance of midsize conventional, hybrid electric, and PHEV models is then simulated over the 227 full-day driving profiles to assess fuel consumption and operating characteristics of these vehicle technologies...
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paper_title: Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV)

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Deployment of PHEV will initiate an integration of transportation and power systems. Intuitively, the PHEVs will constitute an additional demand to the electricity grid, potentially violating converter or line capacities when recharging. Smart management schemes can alleviate possible congestions in power systems, intelligently distributing available energy. As PHEV are inherently independent entities, an agent based approach is expedient. Nonlinear pricing will be adapted to model and manage recharging behavior of large numbers of autonomous PHEV agents connecting in one urban area modelled as an energy hub. The scheme will incorporate price dependability. An aggregation entity, with no private information about its customers, will manage the PHEV agents whose individual parameters will be based on technical constraints and individual objectives. Analysis of the management scheme will give implications for PHEV modelling and integration schemes as well as tentative ideas of possible repercussions on power systems.
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paper_title: Integration of Plug-In Hybrid Electric Vehicles into energy networks

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Electrification of substantial percentages of individual transportation through Plug-In Hybrid Electric Vehicles (PHEVs) will lead to an integration of power and transport systems. This, in turn, will impose an additional demand on today's power system, potentially stressing hazardous for some pieces of equipment. Smart management schemes, investigated in this paper, can alleviate possible congestion issues in power systems by intelligently distributing available energy among connected PHEVs, modelled as agents with individual parameters and goals. The management scheme is integrated in aggregation entities, clustering PHEVs in various urban areas. Network impacts resulting from the smart management scheme will be studied based on power flow calculations. The analysis will give implications for PHEV integration schemes as well as tentative ideas of possible repercussions on hybrid power systems and how to counteract.
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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: 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: Network constrained transactive control for electric vehicles integration

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Electric vehicles (EVs) are commonly recognized as smart grid assets in addition to their environmental benefits. However, uncoordinated charging or sole cost minimization based charging of electric vehicles may bring undesirable peak demands and voltage violations in the distribution system. This paper applies the transactive control concept to integrate electric vehicles into the power distribution system with the purpose of minimizing the charging cost of electric vehicles as well as preventing grid congestions and voltage violations. A hierarchical EV management system is proposed where three actors are considered: distribution system operator (DSO), fleet operators and EV owners. In the lower level of the hierarchy, the fleet operator centrally manages the charging schedule of electric vehicles; in the upper level of the hierarchy, the DSO uses transactive control technique to coordinate the aggregated charging behavior of fleet operators. Detailed models are presented to illustrate the operation of the hierarchical EV management system. In the end, simulations are presented to show the effectiveness of the proposed solutions.
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paper_title: A Flexible Stochastic Optimization Method for Wind Power Balancing With PHEVs

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This paper proposes a flexible optimization method, based on state of the art algorithms, for the smart control of plug-in hybrid electric vehicles (PHEVs) to balance wind power production. The problem is approached from the perspective of a balance responsible party (BRP) with a large share of wind power in its portfolio. The BRP uses controllable PHEVs to minimize the imbalance of its portfolio resulting from wind power forecast errors. A Markov Decision Process (MDP) formulation in combination with dynamic programming is used to solve the multistage stochastic problem. The main difficulty for applying MDPs to this problem is to efficiently include time interdependence of the wind power forecast error. In the presented approach, the probability distribution and time interdependence of the forecast error are represented by a scenario tree. Because of the MDP formulation, the algorithm is adaptable to deal with different transition models and constraints. This feature enables to use the algorithm in a dynamic environment such as the future smart grid. To demonstrate this, a generic charging model for PHEVs is used in the BRP wind balancing case. The flexibility of the algorithm is shown by investigating the solution for different degrees of complexity in the charging model.
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paper_title: Optimal Charging of Electric Vehicles With Uncertain Departure Times: A Closed-Form Solution

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In this paper, we show that an uncertain departure time significantly changes the analysis in optimizing the charging schedule of electric vehicles (EVs). We also obtain a closed-form solution for the stochastic optimization problem that is formulated to schedule charging of EVs with uncertain departure times in presence of hourly time-of-use pricing tariffs.
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paper_title: Charging scheduling with minimal waiting in a network of electric vehicles and charging stations

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Environment preservation has become a prominent issue around the world. As traditional internal combustion engine (ICE) vehicles have been major contributors of air pollution, electric vehicles (EVs) are gaining popularity. However, due to the limited electricity supply of battery pack, EVs need to be charged frequently and each charge takes long time. This may degrade travel efficiency and driver comfort. To address this issue, this paper aims to minimize charging waiting time through intelligently scheduling charging activities spatially and temporally. A theoretical study has been conducted to formulate the waiting time minimized charging scheduling problem and derive a performance upper bound (i.e., the theoretical lower bound of charging waiting time). Based on the insights discovered from the theoretical analysis, a practical distributed scheme has been proposed. Extensive simulation results verify that the proposed design can achieve a waiting time near the theoretical lower bound.
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paper_title: The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook

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Plug-in hybrid electric vehicles (PHEVs) are the next big thing in the electric transportation market. While much work has been done to detail what economic costs and benefits PHEVs will have on consumers and producers alike, it seems that it is also important to understand what impact PHEVs will have on distribution networks nationwide. This paper finds that the impact of PHEVs on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration. The impacts that these aspects of PHEVs will have on distribution networks have been measured and calculated by multiple authors in different locations using many different tools that range from analytical techniques to simulations and beyond. While much work has already been completed in this area, there is still much to do. Areas left for improvement and future work will include adding more stochasticity into models as well as computing and analyzing reliability indices with respect to distribution networks.
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paper_title: Optimizing the Energy Delivery via V2G Systems Based on Stochastic Inventory Theory

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In this paper, we study the optimal energy delivery problem from viewpoints of both the vehicle owner and aggregator, in load shaving services of a vehicle-to-grid (V2G) system. We formulate the optimization problem based on a general plug-in hybrid electric vehicle (PHEV) model, taking into account the randomness in vehicle mobility, time-of-use electricity pricing, and realistic battery modeling. Stochastic inventory theory is applied to analyze the problem. We mathematically prove that a state-dependent (S,S') policy is optimal for the daily energy cost minimization of each vehicle, and develop an estimation algorithm to calculate the parameters of the optimal policy for practical applications. Furthermore, we investigate the multi-vehicle aggregator design problem by considering the power system constraints. A policy adjustment scheme is proposed to adjust the values of S and S' with respect to the optimal policy adopted by each PHEV, such that the aggregated recharging and discharging power constraints of the power system can be satisfied, while minimizing the incremental cost (or revenue loss) of PHEV owners. Based on characteristics of the state-dependent (S,S') policy and our proposed policy adjustment scheme, the optimal aggregator operation problem is transformed into a convex optimization one which can be readily solved by existing algorithms. The performance of our proposed schemes is evaluated via simulations based on real data collected from Canadian utilities, households, and commuters.
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paper_title: Towards optimal energy store-carry-and-deliver for PHEVs via V2G system

paper_content:

As an important component of smart grid, the vehicle-to-grid (V2G) system is recently introduced to enable bidirectional energy delivery between the power grid and plug-in electric vehicles. Communication technology is incorporated to facilitate the energy delivery by providing electricity pricing and energy demand information. However, different from the stationary energy storage systems, the energy store-carry-and-deliver mechanism for a V2G system poses new challenges for performance optimization, such as bi-directional energy flow and non-stationary energy demand. How to utilize the statistical information provided by the communication system to achieve efficient energy delivery is critical for a V2G system and is still an open issue. In this paper, we address a specific problem in this new research area, i.e., daily energy cost minimization of vehicle owners under time-of-use (TOU) electricity pricing. We investigate a plug-in hybrid electric vehicle (PHEV) with a realistic battery model, which is general for both battery electric cars and plug-in hybrids. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and TOU electricity price. We prove the optimality of a state-dependent double-threshold (or (S, S′)) policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average (EWMA) algorithm is used to estimate the statistics of PHEV mobility and energy demand. The performance of the proposed scheme is demonstrated by simulations based on survey and real data collected from Canadian households. Numerical results indicate that our proposed scheme performs closely to a scheme with a priori knowledge of the PHEV mobility and energy demand information. Compared with the existing approaches, the proposed scheme can achieve energy cost reduction, which increases with the battery capacity.
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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: Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems

paper_content:

This paper presents an analysis of the cost of utilizing battery electric vehicle (BEV) batteries as energy storage in power grids [also known as vehicle-to-grid (V2G)] associated with lessening battery cycle life due to more frequent charging and discharging activities and utilization in elevated ambient temperature. Comparison is made between V2G in the U.K., where annual electricity peak demand is reached in winter, and in China, where peak demand is reached in summer due to the air conditioning load. This paper presents mathematical correlations between charging-discharging, ambient temperature, depth of discharge (DoD), and the degradation of electric vehicle batteries based on manufacturer's data. Simulation studies were carried out for V2G in both the U.K. and China. Numerical results show that ambient temperature and DoD of a BEV battery play a crucial role in the cost of battery wear. Lead-acid and NiMH battery powered BEVs are not cost effective in V2G use due to the present electricity tariff. Under the present electricity tariff structure, no vehicles would be cost effective for the peak power sources in China. However, lithium-ion battery powered BEVs are cost effective in the U.K. due to a much longer cycle life.
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paper_title: A contract-based approach for ancillary services in V2G networks: Optimality and learning

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With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.
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paper_title: Real-time welfare-maximizing regulation allocation in aggregator-EVs systems

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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: Elements of Information Theory

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Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entropy, and Mutual Information. 2.1 Entropy. 2.2 Joint Entropy and Conditional Entropy. 2.3 Relative Entropy and Mutual Information. 2.4 Relationship Between Entropy and Mutual Information. 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information. 2.6 Jensen's Inequality and Its Consequences. 2.7 Log Sum Inequality and Its Applications. 2.8 Data-Processing Inequality. 2.9 Sufficient Statistics. 2.10 Fano's Inequality. Summary. Problems. Historical Notes. 3. Asymptotic Equipartition Property. 3.1 Asymptotic Equipartition Property Theorem. 3.2 Consequences of the AEP: Data Compression. 3.3 High-Probability Sets and the Typical Set. Summary. Problems. Historical Notes. 4. Entropy Rates of a Stochastic Process. 4.1 Markov Chains. 4.2 Entropy Rate. 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph. 4.4 Second Law of Thermodynamics. 4.5 Functions of Markov Chains. Summary. Problems. Historical Notes. 5. Data Compression. 5.1 Examples of Codes. 5.2 Kraft Inequality. 5.3 Optimal Codes. 5.4 Bounds on the Optimal Code Length. 5.5 Kraft Inequality for Uniquely Decodable Codes. 5.6 Huffman Codes. 5.7 Some Comments on Huffman Codes. 5.8 Optimality of Huffman Codes. 5.9 Shannon-Fano-Elias Coding. 5.10 Competitive Optimality of the Shannon Code. 5.11 Generation of Discrete Distributions from Fair Coins. Summary. Problems. Historical Notes. 6. Gambling and Data Compression. 6.1 The Horse Race. 6.2 Gambling and Side Information. 6.3 Dependent Horse Races and Entropy Rate. 6.4 The Entropy of English. 6.5 Data Compression and Gambling. 6.6 Gambling Estimate of the Entropy of English. Summary. Problems. Historical Notes. 7. Channel Capacity. 7.1 Examples of Channel Capacity. 7.2 Symmetric Channels. 7.3 Properties of Channel Capacity. 7.4 Preview of the Channel Coding Theorem. 7.5 Definitions. 7.6 Jointly Typical Sequences. 7.7 Channel Coding Theorem. 7.8 Zero-Error Codes. 7.9 Fano's Inequality and the Converse to the Coding Theorem. 7.10 Equality in the Converse to the Channel Coding Theorem. 7.11 Hamming Codes. 7.12 Feedback Capacity. 7.13 Source-Channel Separation Theorem. Summary. Problems. Historical Notes. 8. Differential Entropy. 8.1 Definitions. 8.2 AEP for Continuous Random Variables. 8.3 Relation of Differential Entropy to Discrete Entropy. 8.4 Joint and Conditional Differential Entropy. 8.5 Relative Entropy and Mutual Information. 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information. Summary. Problems. Historical Notes. 9. Gaussian Channel. 9.1 Gaussian Channel: Definitions. 9.2 Converse to the Coding Theorem for Gaussian Channels. 9.3 Bandlimited Channels. 9.4 Parallel Gaussian Channels. 9.5 Channels with Colored Gaussian Noise. 9.6 Gaussian Channels with Feedback. Summary. Problems. Historical Notes. 10. Rate Distortion Theory. 10.1 Quantization. 10.2 Definitions. 10.3 Calculation of the Rate Distortion Function. 10.4 Converse to the Rate Distortion Theorem. 10.5 Achievability of the Rate Distortion Function. 10.6 Strongly Typical Sequences and Rate Distortion. 10.7 Characterization of the Rate Distortion Function. 10.8 Computation of Channel Capacity and the Rate Distortion Function. Summary. Problems. Historical Notes. 11. Information Theory and Statistics. 11.1 Method of Types. 11.2 Law of Large Numbers. 11.3 Universal Source Coding. 11.4 Large Deviation Theory. 11.5 Examples of Sanov's Theorem. 11.6 Conditional Limit Theorem. 11.7 Hypothesis Testing. 11.8 Chernoff-Stein Lemma. 11.9 Chernoff Information. 11.10 Fisher Information and the Cram-er-Rao Inequality. Summary. Problems. Historical Notes. 12. Maximum Entropy. 12.1 Maximum Entropy Distributions. 12.2 Examples. 12.3 Anomalous Maximum Entropy Problem. 12.4 Spectrum Estimation. 12.5 Entropy Rates of a Gaussian Process. 12.6 Burg's Maximum Entropy Theorem. Summary. Problems. Historical Notes. 13. Universal Source Coding. 13.1 Universal Codes and Channel Capacity. 13.2 Universal Coding for Binary Sequences. 13.3 Arithmetic Coding. 13.4 Lempel-Ziv Coding. 13.5 Optimality of Lempel-Ziv Algorithms. Compression. Summary. Problems. Historical Notes. 14. Kolmogorov Complexity. 14.1 Models of Computation. 14.2 Kolmogorov Complexity: Definitions and Examples. 14.3 Kolmogorov Complexity and Entropy. 14.4 Kolmogorov Complexity of Integers. 14.5 Algorithmically Random and Incompressible Sequences. 14.6 Universal Probability. 14.7 Kolmogorov complexity. 14.9 Universal Gambling. 14.10 Occam's Razor. 14.11 Kolmogorov Complexity and Universal Probability. 14.12 Kolmogorov Sufficient Statistic. 14.13 Minimum Description Length Principle. Summary. Problems. Historical Notes. 15. Network Information Theory. 15.1 Gaussian Multiple-User Channels. 15.2 Jointly Typical Sequences. 15.3 Multiple-Access Channel. 15.4 Encoding of Correlated Sources. 15.5 Duality Between Slepian-Wolf Encoding and Multiple-Access Channels. 15.6 Broadcast Channel. 15.7 Relay Channel. 15.8 Source Coding with Side Information. 15.9 Rate Distortion with Side Information. 15.10 General Multiterminal Networks. Summary. Problems. Historical Notes. 16. Information Theory and Portfolio Theory. 16.1 The Stock Market: Some Definitions. 16.2 Kuhn-Tucker Characterization of the Log-Optimal Portfolio. 16.3 Asymptotic Optimality of the Log-Optimal Portfolio. 16.4 Side Information and the Growth Rate. 16.5 Investment in Stationary Markets. 16.6 Competitive Optimality of the Log-Optimal Portfolio. 16.7 Universal Portfolios. 16.8 Shannon-McMillan-Breiman Theorem (General AEP). Summary. Problems. Historical Notes. 17. Inequalities in Information Theory. 17.1 Basic Inequalities of Information Theory. 17.2 Differential Entropy. 17.3 Bounds on Entropy and Relative Entropy. 17.4 Inequalities for Types. 17.5 Combinatorial Bounds on Entropy. 17.6 Entropy Rates of Subsets. 17.7 Entropy and Fisher Information. 17.8 Entropy Power Inequality and Brunn-Minkowski Inequality. 17.9 Inequalities for Determinants. 17.10 Inequalities for Ratios of Determinants. Summary. Problems. Historical Notes. Bibliography. List of Symbols. Index.
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paper_title: The effect of communication architecture on the availability, reliability, and economics of plug-in hybrid electric vehicle-to-grid ancillary services

paper_content:

Researchers have proposed that fleets of plug-in hybrid vehicles could be used to perform ancillary services for the electric grid. In many of these studies, the vehicles are able to accrue revenue for performing these grid stabilization services, which would offset the increased purchase cost of plug-in hybrid vehicles. To date, all such studies have assumed a vehicle command architecture that allows for a direct and deterministic communication between the grid system operator and the vehicle. This work compares this direct, deterministic vehicle command architecture to an aggregative vehicle command architecture on the bases of the availability, reliability and value of vehicle-provided ancillary services. This research incorporates a new level of detail into the modeling of vehicle-to-grid ancillary services by incorporating probabilistic vehicle travel models, time series ancillary services pricing, and a consideration of ancillary services reliability. Results show that including an aggregating entity in the command and contracting architecture can improve the scale and reliability of vehicle-to-grid ancillary services, thereby making vehicle-to-grid ancillary services more compatible with the current ancillary services market. However, the aggregative architecture has the deleterious effect of reducing the revenue accrued by plug-in vehicle owners relative to the default architectures.
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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: Fair Design of Plug-in Electric Vehicles Aggregator for V2G Regulation

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Plug-in electric vehicles (PEVs) have recently attracted much attention due to their potential to reduce CO2 emissions and transportation costs and can be grouped into entities (aggregators) to provide ancillary services such as frequency regulation. In this paper, the application of aggregators to frequency regulation by making fair use of their energy storage capacity is addressed. When the power grid requires frequency regulation service to the aggregator to adjust the grid frequency, the PEVs participating in providing the service can either draw energy (as it is usually done to charge the vehicle) or deliver energy to the grid by means of the vehicle-to-grid (V2G) interface. Under the general framework of optimizing the aggregator profit, different methods, such as state-dependent allocation and the water-filling approach, are proposed to achieve a final state of charge (SOC) of the PEVs that satisfy the desired fairness criteria once the regulation service has been carried out.
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paper_title: Using vehicle-to-grid technology for frequency regulation and peak-load reduction

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Abstract This paper explores the potential financial return for using plug-in hybrid electric vehicles as a grid resource. While there is little financial incentive for individuals when the vehicle-to-grid (V2G) service is used exclusively for peak reduction, there is a significant potential for financial return when the V2G service is used for frequency regulation. We propose that these two uses for V2G technology are not mutually exclusive, and that there could exist a “dual-use” program that utilizes V2G for multiple uses simultaneously. In our proposition, V2G could be used for regulation on a daily basis to ensure profits, and be used for peak reduction on days with high electricity demand and poor ambient air quality in order to reap the greatest environmental benefits. The profits for the individual in this type of dual-use program are close to or even higher than the profits experienced in either of the single-use programs. More importantly, we argue that the external benefits of this type of program are much greater as well. At higher V2G participation rates, our analysis shows that the market for regulation capacity could become saturated by V2G-based regulation providers. At the same time, there is plenty of potential for widespread use of V2G technology, especially if the demand for regulation, reserves, and storage grows as more intermittent renewable resources are being incorporated into the power systems.
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paper_title: ELECTRIC VEHICLES AS A NEW POWER SOURCE FOR ELECTRIC UTILITIES

paper_content:

Abstract Electric-drive vehicles, whether fueled by batteries or by liquid or gaseous fuels generating electricity on-board, will have value to electric utilities as power resources. The power capacity of the current internal combustion passenger vehicle fleet is enormous and under-utilized. In the United States, for example, the vehicle fleet has over 10 times the mechanical power of all current U.S. electrical generating plants and is idle over 95% of the day. Electric utilities could use battery vehicles as storage, or fuel cell and hybrid vehicles as generation. This paper analyzes vehicle battery storage in greatest detail, comparing three electric vehicle configurations over a range of driving requirements and electric utility demand conditions. Even when making unfavorable assumptions about the cost and lifetime of batteries, over a wide range of conditions the value to the utility of tapping vehicle electrical storage exceeds the cost of the two-way hook-up and reduced vehicle battery life. For example, even a currently-available electric vehicle, in a utility with medium value of peak power, could provide power at a net present cost to the vehicle owner of $955 and net present value to the utility of $2370. As an incentive to the vehicle owner, the utility might offer a vehicle purchase subsidy, lower electric rates, or purchase and maintenance of successive vehicle batteries. For a utility tapping vehicle power, the increased storage would provide system benefits such as reliability and lower costs, and would later facilitate large-scale integration of intermittent-renewable energy resources.
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paper_title: Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany

paper_content:

This study investigates plug-in hybrid electric vehicles (PHEVs) as providers of regulating power in the form of primary, secondary and tertiary frequency control. Previous studies have shown that PHEVs could generate substantial profits while providing ancillary services. This study investigates under what conditions PHEVs can generate revenues using actual market data, i.e. prices and activations of regulating power, from Sweden and Germany from four months in 2008. PHEV market participation is modelled for individual vehicles in a fleet subject to a simulated movement pattern. Costs for infrastructure and vehicle-to-grid equipment are not included in the analysis. The simulation results indicate that maximum average profits generated on the German markets are in the range 30-80Â [euro] per vehicle and month whereas the Swedish regulating power markets give no profit. In addition, an analysis is performed to identify strengths, weaknesses, opportunities, and threats (SWOT) of PHEVs as regulating power providers. Based on the simulation results and the SWOT analysis, characteristics for an ideal regulating power market for PHEVs are presented.
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paper_title: Deploying power grid-integrated electric vehicles as a multi-agent system

paper_content:

Grid-Integrated Vehicles (GIVs) are plug-in Electric Drive Vehicles (EDVs) with power-management and other controls that allow them to respond to external commands sent by power-grid operators, or their affiliates, when parked and plugged-in to the grid. At a bare minimum, such GIVs should respond to demand-management commands or pricing signals to delay, reduce or switch-off the rate of charging when the demand for electricity is high. In more advanced cases, these GIVs might sell both power and storage capacity back to the grid in any of the several electric power markets --- a concept known as Vehicle-to-Grid power or V2G power. ::: ::: Although individual EDVs control too little power to sell in the market at an individual level, a large group of EDVs may form an aggregate or coalition that controls enough power to meaningfully sell, at a profit, in these markets. The profits made by such a coalition can then be used by the coalition members to offset the costs of the electric vehicles and batteries themselves. In this paper we describe an implemented and deployed multi-agent system that is used to integrate EDVs into the electricity grid managed by PJM, the largest transmission service operator in the world. We provide a brief introduction to GIVs and the various power markets and discuss why multi-agent systems are a good match for this application.
---
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).
---
paper_title: Real-Time Welfare-Maximizing Regulation Allocation in Dynamic Aggregator-EVs System

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 dynamic EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among its EVs. Compared to previous works, WMRA accommodates a wide spectrum of vital system characteristics, including dynamics of EV, limited EV battery size, EV battery degradation cost, and the cost of using external energy sources for the aggregator. The algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate that WMRA is away from the optimum by O(1/V), where V is a controlling parameter depending on EV's battery size. In addition, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.
---
paper_title: PEV-based reactive power compensation for wind DG units: A stackelberg game approach

paper_content:

There has been a growing interest recently towards integrating more renewable energy resources, in particular wind power, in form of distributed generation (DG) units. However, one important challenge with wind DG units is to provide low-cost and fast-responding reactive power compensation of the wind turbine's inductive load to ensure a stable voltage profile in the system. Since reactive power can only be compensated locally, we consider a scenario where a wind DG unit is co-located with a plug-in electric vehicle (PEV) charging station or a parking lot, and we investigate how to align incentives to encourage PEV owners to participate in reactive power compensation for wind DG units. For this purpose, in this paper, we introduce a two-stage Stackelberg game between the wind DG unit and the PEV owners. We use backward induction to analyze the formulated game and derive the optimal pricing scheme. We assess the performance of our proposed scheme using field data and make suggestions for the size of the charging stations.
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paper_title: Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time

paper_content:

We model many combinations of renewable electricity sources (inland wind, offshore wind, and photovoltaics) with electrochemical storage (batteries and fuel cells), incorporated into a large grid system (72 GW). The purpose is twofold: 1) although a single renewable generator at one site produces intermittent power, we seek combinations of diverse renewables at diverse sites, with storage, that are not intermittent and satisfy need a given fraction of hours. And 2) we seek minimal cost, calculating true cost of electricity without subsidies and with inclusion of external costs. Our model evaluated over 28 billion combinations of renewables and storage, each tested over 35,040 h (four years) of load and weather data. We find that the least cost solutions yield seemingly-excessive generation capacitydat times, almost three times the electricity needed to meet electrical load. This is because diverse renewable generation and the excess capacity together meet electric load with less storage, lowering total system cost. At 2030 technology costs and with excess electricity displacing natural gas, we find that the electric system can be powered 90%e99.9% of hours entirely on renewable electricity, at costs comparable to today’sdbut only if we optimize the mix of generation and storage technologies.
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paper_title: Fast MPC-Based Coordination of Wind Power and Battery Energy Storage Systems

paper_content:

In this paper, a model predictive control (MPC)-based coordinated scheduling framework for variable wind generation and battery energy storage systems (BESSs) is presented. On the basis of the short-term forecast of available wind generation and price information, a joint look-ahead optimization is performed by the wind farm and storage system to determine their net power injection to the electric power grid. In conjunction with moderate battery capacity, the excess unpredictable wind generation can be used to charge the battery storage and vice versa. The benefits of the proposed scheduling approach are that (1) the combined profit of wind generation and BESS is increased; (2) the net power injection from the wind farm into the power grid is smoothed out; and (3) the look-ahead optimization updates the price prediction in a moving horizon, which leads to more robust profit for wind farm and BESS against price uncertainties. By formulating the MPC-based coordinated scheduling as a quadratic programming problem, several numerically efficient algorithms to compute the optimal control strategy for wind generation and BESS are proposed. The effectiveness of the proposed algorithm in a modified IEEE 24-bus reli- ability test system with aggregated plug-in hybrid electric vehicles is demonstrated. It is shown that the proposed algorithm can increase the joint profit of wind farm and BESS while smoothing out the net power injection to the electricity grid. The proposed MPC-based scheduling problem can be solved in approximately 400 ms, which makes the framework implementable in realtime electricity market operations. DOI: 10.1061/(ASCE)EY.1943-7897.0000071. © 2012 American Society of Civil Engineers. CE Database subject headings: Predictions; Wind power; Energy storage; Vehicles. Author keywords: Model predictive control; Wind power integration; Battery energy storage systems; Plug-in hybrid electric vehicle; Power system economic dispatch; Electricity market.
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paper_title: Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy

paper_content:

Abstract Vehicle-to-grid power (V2G) uses electric-drive vehicles (battery, fuel cell, or hybrid) to provide power for specific electric markets. This article examines the systems and processes needed to tap energy in vehicles and implement V2G. It quantitatively compares today's light vehicle fleet with the electric power system. The vehicle fleet has 20 times the power capacity, less than one-tenth the utilization, and one-tenth the capital cost per prime mover kW. Conversely, utility generators have 10–50 times longer operating life and lower operating costs per kWh. To tap V2G is to synergistically use these complementary strengths and to reconcile the complementary needs of the driver and grid manager. This article suggests strategies and business models for doing so, and the steps necessary for the implementation of V2G. After the initial high-value, V2G markets saturate and production costs drop, V2G can provide storage for renewable energy generation. Our calculations suggest that V2G could stabilize large-scale (one-half of US electricity) wind power with 3% of the fleet dedicated to regulation for wind, plus 8–38% of the fleet providing operating reserves or storage for wind. Jurisdictions more likely to take the lead in adopting V2G are identified.
---
paper_title: An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

paper_content:

Wind power is gaining in significance as an important renewable source of clean energy. However, due to their inherent uncertainty, wind generators are often unable to participate in the forward electricity markets like the more predictable and controllable conventional generators. Given this, virtual power plants (VPPs) are being advocated as a solution for increasing the reliability of such intermittent renewable sources. In this paper, we take this idea further by considering VPPs as coalitions of wind generators and electric vehicles, where wind generators seek to use electric vehicles (EVs) as a storage medium to overcome the vagaries of generation. Using electric vehicles in this manner has the advantage that, since the number of EVs is increasing rapidly, no initial investment in dedicated storage is needed. In more detail, we first formally model the VPP and then, through an operational model based on linear programming, we show how the supply to the Grid and storage in the EV batteries can be scheduled to increase the profit of the VPP, while also paying for the storage using a novel scheme. The feasibility of our approach is examined through a realistic case-study, using real wind power generation data, corresponding electricity market prices and electric vehicles' characteristics.
---
paper_title: Charging station selection optimization for plug-in electric vehicles: An oligopolistic game-theoretic framework

paper_content:

In this paper, we describe a framework for the selection of the best charging station when plug-in electric vehicles (PEV) need to recharge their batteries, while at the same time the power utilities, which own the charging stations (CS), optimize their revenue. We use two-way communication to transmit positioning information, which is a key factor so that the plug-in electric vehicles evaluate its required energy. Within this framework, we also describe a procedure that is implemented in a distributed manner and is based on a non-cooperative oligopoly game that makes use of differentiated products theory and conjectural variations to provide a Nash equilibrium in prices and quantities.
---
paper_title: Real-Time Distributed Control for Smart Electric Vehicle Chargers: From a Static to a Dynamic Study

paper_content:

At high penetrations, uncontrolled electric vehicle (EV) charging has the potential to cause line and transformer congestion in the distribution network. Instead of upgrading components to higher nameplate ratings, we investigate the use of real-time control to limit EV load to the available capacity in the network. Inspired by rate control algorithms in computer networks such as TCP, we design a measurement-based, real-time, distributed, stable, efficient, and fair charging algorithm using the dual-decomposition approach. We show through extensive numerical simulations and power flow analysis on a test distribution network that this algorithm operates successfully in both static and dynamic settings, despite changes in home loads and the number of connected EVs. We find that our algorithm rapidly converges from large disturbances to a stable operating point. We show that in a test setting, for an acceptable level of overload, only 70 EVs could be fully charged without control, whereas up to around 700 EVs can be fully charged using our control algorithm. This compares well with the maximum supportable population of approximately 900 EVs. Our work also provides engineering guidelines for choosing the control parameters and setpoints in a distribution network.
---
paper_title: A contract-based approach for ancillary services in V2G networks: Optimality and learning

paper_content:

With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.
---
paper_title: Rapid-Charging Navigation of Electric Vehicles Based on Real-Time Power Systems and Traffic Data

paper_content:

Electric vehicles (EVs) have attracted growing attention in recent years. However, daily charging of EVs, in particular rapid charging, may impact power systems, especially during peak hours, and the effects may occur in different places as traffic conditions change. To address these issues, we describe an integrated rapid-charging navigation strategy that considers both the traffic conditions and the status of the power grid. The system is based on an intelligent transport system (ITS), and contains four modules: a power system control center (PSCC), an ITS center, charging stations, and EV terminals. The PSCC calculates the available charging capacity and station charging capacity based on power grid data and transmits the results to the charging stations. The charging stations determine their charging plans and estimate the available charging power for future EVs (CPFE), and transmit these data to the ITS center. After receiving CPFE data and traffic data from the ITS center, the EV terminal estimates the total time for charging (TTC) for different stations, which includes the driving time, waiting time, and charging time. The driver can view these results and choose to be navigated to the charging station corresponding to the minimum TTC. The modular design of the navigation system reduces data transmission, which protects the drivers' privacy since they can choose which charging station to use and are not required to send any data to the ITS system. Simulation results demonstrate the feasibility of the proposed method for different working conditions for power system and traffic conditions.
---
paper_title: Charging games in networks of electrical vehicles

paper_content:

In this paper, a static non-cooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each EV aims at choosing the time at which it starts charging its battery in order to minimize an individual cost which is mainly related to the total power delivered by the transformer, the location of the time interval over which the charging operation is performed, and the charging duration needed for the considered EV to have its battery fully recharged. As individual cost functions are assumed to be memoryless, it is possible to show that the game of interest is always an ordinal potential game. More precisely, both an atomic and nonatomic versions of the charging game are considered. In both cases, equilibrium analysis is conducted. In particular, important issues such as equilibrium uniqueness and efficiency are tackled. Interestingly, both analytical and numerical results show that the efficiency loss due to decentralization (e.g., when cost functions such as distribution network Joule losses or life of residential distribution transformers when no thermal inertia is assumed) induced by charging is small and the corresponding “efficiency”, a notion close to the Price of Anarchy, tends to one when the number of EV increases.
---
paper_title: Online mechanism design for electric vehicle charging

paper_content:

Plug-in hybrid electric vehicles are expected to place a considerable strain on local electricity distribution networks, requiring charging to be coordinated in order to accommodate capacity constraints. We design a novel online auction protocol for this problem, wherein vehicle owners use agents to bid for power and also state time windows in which a vehicle is available for charging. This is a multi-dimensional mechanism design domain, with owners having non-increasing marginal valuations for each subsequent unit of electricity. In our design, we couple a greedy allocation algorithm with the occasional "burning" of allocated power, leaving it unallocated, in order to adjust an allocation and achieve monotonicity and thus truthfulness. We consider two variations: burning at each time step or on-departure. Both mechanisms are evaluated in depth, using data from a real-world trial of electric vehicles in the UK to simulate system dynamics and valuations. The mechanisms provide higher allocative efficiency than a fixed price system, are almost competitive with a standard scheduling heuristic which assumes non-strategic agents, and can sustain a substantially larger number of vehicles at the same per-owner fuel cost saving than a simple random scheme.
---
paper_title: Pricing the Internet With Multibid Auctions

paper_content:

Usage-based or congestion-based charging schemes have been regarded as a relevant way to control congestion and to differentiate services among users in telecommunication networks; auctioning for bandwidth appears as one of several possibilities. In a previous work, the authors designed a multibid auction scheme where users compete for bandwidth at a link by submitting several couples (e.g., amount of bandwidth asked, associated unit price) so that the link allocates the bandwidth and computes the charge according to the second price principle. They showed that incentive compatibility and efficiency among other properties are verified. We propose in the present paper to extend this scheme to the case of a network by using the properties/assumptions that the backbone network is overprovisioned and the access networks have a tree structure
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paper_title: Optimal decentralized protocol for electric vehicle charging

paper_content:

Motivated by the power-grid-side challenges in the integration of electric vehicles, we propose a decentralized protocol for negotiating day-ahead charging schedules for electric vehicles. The overall goal is to shift the load due to electric vehicles to fill the overnight electricity demand valley. In each iteration of the proposed protocol, electric vehicles choose their own charging profiles for the following day according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. This protocol is guaranteed to converge, irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. At convergence, the l 2 norm of the aggregated demand is minimized, and the aggregated demand profile is as “flat” as it can possibly be. The proposed protocol needs no coordination among the electric vehicles, hence requires low communication and computation capability. Simulation results demonstrate convergence to optimal collections of charging profiles within few iterations.
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paper_title: Optimized Bidding of a EV Aggregation Agent in the Electricity Market

paper_content:

An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naive forecast for all variables of the problem.
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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.
---
paper_title: Charging station selection optimization for plug-in electric vehicles: An oligopolistic game-theoretic framework

paper_content:

In this paper, we describe a framework for the selection of the best charging station when plug-in electric vehicles (PEV) need to recharge their batteries, while at the same time the power utilities, which own the charging stations (CS), optimize their revenue. We use two-way communication to transmit positioning information, which is a key factor so that the plug-in electric vehicles evaluate its required energy. Within this framework, we also describe a procedure that is implemented in a distributed manner and is based on a non-cooperative oligopoly game that makes use of differentiated products theory and conjectural variations to provide a Nash equilibrium in prices and quantities.
---
paper_title: Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

paper_content:

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: Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

paper_content:

Abstract Electric Vehicle (EV) navigation system normally requires multiple charging in a long origin destination (OD) trip, which makes it different from the traditional vehicle navigation system. Fast charging is an ideal charging solution in multiple charging because of its high charging efficiency, rapid transient response and short charging time. However, EVs fast charging at on-peak hours may lead to overloading of distribution system. To solve the problem above, a real-time pricing (RTP) policy, as one of the main thrusts for load shaving application, should be introduced, which will bring new challenges in EV navigation systems due to the characteristic of time-dependence. In this paper, we address an optimization problem for EV navigation systems under the RTP policy with the consideration of both charging and routing. By using the EV arrival states and the traffic parameters, the original traffic network can be extended to a feasible state graph. An Improved Chrono-SPT (ICS) algorithm is provided to derive the optimal decision sequence, which provides an optimal routing and charging policy. Furthermore, a Simplify-Charge-Control (SCC) algorithm is also presented to reduce the computation complexity of the ICS algorithm. Simulations show the effectiveness of both ICS and SCC algorithms and the computation complexity of SCC algorithm is much simplified within acceptable deviation of optimal cost under approximation pricing (AP) than that in ICS algorithm.
---
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).
---
paper_title: Real-Time Welfare-Maximizing Regulation Allocation in Dynamic Aggregator-EVs System

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 dynamic EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among its EVs. Compared to previous works, WMRA accommodates a wide spectrum of vital system characteristics, including dynamics of EV, limited EV battery size, EV battery degradation cost, and the cost of using external energy sources for the aggregator. The algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate that WMRA is away from the optimum by O(1/V), where V is a controlling parameter depending on EV's battery size. In addition, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.
---
paper_title: Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV)

paper_content:

Deployment of PHEV will initiate an integration of transportation and power systems. Intuitively, the PHEVs will constitute an additional demand to the electricity grid, potentially violating converter or line capacities when recharging. Smart management schemes can alleviate possible congestions in power systems, intelligently distributing available energy. As PHEV are inherently independent entities, an agent based approach is expedient. Nonlinear pricing will be adapted to model and manage recharging behavior of large numbers of autonomous PHEV agents connecting in one urban area modelled as an energy hub. The scheme will incorporate price dependability. An aggregation entity, with no private information about its customers, will manage the PHEV agents whose individual parameters will be based on technical constraints and individual objectives. Analysis of the management scheme will give implications for PHEV modelling and integration schemes as well as tentative ideas of possible repercussions on power systems.
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paper_title: Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

paper_content:

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: Charging scheduling with minimal waiting in a network of electric vehicles and charging stations

paper_content:

Environment preservation has become a prominent issue around the world. As traditional internal combustion engine (ICE) vehicles have been major contributors of air pollution, electric vehicles (EVs) are gaining popularity. However, due to the limited electricity supply of battery pack, EVs need to be charged frequently and each charge takes long time. This may degrade travel efficiency and driver comfort. To address this issue, this paper aims to minimize charging waiting time through intelligently scheduling charging activities spatially and temporally. A theoretical study has been conducted to formulate the waiting time minimized charging scheduling problem and derive a performance upper bound (i.e., the theoretical lower bound of charging waiting time). Based on the insights discovered from the theoretical analysis, a practical distributed scheme has been proposed. Extensive simulation results verify that the proposed design can achieve a waiting time near the theoretical lower bound.
---
paper_title: Real-Time Distributed Control for Smart Electric Vehicle Chargers: From a Static to a Dynamic Study

paper_content:

At high penetrations, uncontrolled electric vehicle (EV) charging has the potential to cause line and transformer congestion in the distribution network. Instead of upgrading components to higher nameplate ratings, we investigate the use of real-time control to limit EV load to the available capacity in the network. Inspired by rate control algorithms in computer networks such as TCP, we design a measurement-based, real-time, distributed, stable, efficient, and fair charging algorithm using the dual-decomposition approach. We show through extensive numerical simulations and power flow analysis on a test distribution network that this algorithm operates successfully in both static and dynamic settings, despite changes in home loads and the number of connected EVs. We find that our algorithm rapidly converges from large disturbances to a stable operating point. We show that in a test setting, for an acceptable level of overload, only 70 EVs could be fully charged without control, whereas up to around 700 EVs can be fully charged using our control algorithm. This compares well with the maximum supportable population of approximately 900 EVs. Our work also provides engineering guidelines for choosing the control parameters and setpoints in a distribution network.
---
paper_title: A contract-based approach for ancillary services in V2G networks: Optimality and learning

paper_content:

With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.
---
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.
---
paper_title: Charging games in networks of electrical vehicles

paper_content:

In this paper, a static non-cooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each EV aims at choosing the time at which it starts charging its battery in order to minimize an individual cost which is mainly related to the total power delivered by the transformer, the location of the time interval over which the charging operation is performed, and the charging duration needed for the considered EV to have its battery fully recharged. As individual cost functions are assumed to be memoryless, it is possible to show that the game of interest is always an ordinal potential game. More precisely, both an atomic and nonatomic versions of the charging game are considered. In both cases, equilibrium analysis is conducted. In particular, important issues such as equilibrium uniqueness and efficiency are tackled. Interestingly, both analytical and numerical results show that the efficiency loss due to decentralization (e.g., when cost functions such as distribution network Joule losses or life of residential distribution transformers when no thermal inertia is assumed) induced by charging is small and the corresponding “efficiency”, a notion close to the Price of Anarchy, tends to one when the number of EV increases.
---
paper_title: Online mechanism design for electric vehicle charging

paper_content:

Plug-in hybrid electric vehicles are expected to place a considerable strain on local electricity distribution networks, requiring charging to be coordinated in order to accommodate capacity constraints. We design a novel online auction protocol for this problem, wherein vehicle owners use agents to bid for power and also state time windows in which a vehicle is available for charging. This is a multi-dimensional mechanism design domain, with owners having non-increasing marginal valuations for each subsequent unit of electricity. In our design, we couple a greedy allocation algorithm with the occasional "burning" of allocated power, leaving it unallocated, in order to adjust an allocation and achieve monotonicity and thus truthfulness. We consider two variations: burning at each time step or on-departure. Both mechanisms are evaluated in depth, using data from a real-world trial of electric vehicles in the UK to simulate system dynamics and valuations. The mechanisms provide higher allocative efficiency than a fixed price system, are almost competitive with a standard scheduling heuristic which assumes non-strategic agents, and can sustain a substantially larger number of vehicles at the same per-owner fuel cost saving than a simple random scheme.
---
paper_title: Optimal decentralized protocol for electric vehicle charging

paper_content:

Motivated by the power-grid-side challenges in the integration of electric vehicles, we propose a decentralized protocol for negotiating day-ahead charging schedules for electric vehicles. The overall goal is to shift the load due to electric vehicles to fill the overnight electricity demand valley. In each iteration of the proposed protocol, electric vehicles choose their own charging profiles for the following day according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. This protocol is guaranteed to converge, irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. At convergence, the l 2 norm of the aggregated demand is minimized, and the aggregated demand profile is as “flat” as it can possibly be. The proposed protocol needs no coordination among the electric vehicles, hence requires low communication and computation capability. Simulation results demonstrate convergence to optimal collections of charging profiles within few iterations.
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paper_title: Optimized Bidding of a EV Aggregation Agent in the Electricity Market

paper_content:

An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naive forecast for all variables of the problem.
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paper_title: Fair Design of Plug-in Electric Vehicles Aggregator for V2G Regulation

paper_content:

Plug-in electric vehicles (PEVs) have recently attracted much attention due to their potential to reduce CO2 emissions and transportation costs and can be grouped into entities (aggregators) to provide ancillary services such as frequency regulation. In this paper, the application of aggregators to frequency regulation by making fair use of their energy storage capacity is addressed. When the power grid requires frequency regulation service to the aggregator to adjust the grid frequency, the PEVs participating in providing the service can either draw energy (as it is usually done to charge the vehicle) or deliver energy to the grid by means of the vehicle-to-grid (V2G) interface. Under the general framework of optimizing the aggregator profit, different methods, such as state-dependent allocation and the water-filling approach, are proposed to achieve a final state of charge (SOC) of the PEVs that satisfy the desired fairness criteria once the regulation service has been carried out.
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paper_title: Charging station selection optimization for plug-in electric vehicles: An oligopolistic game-theoretic framework

paper_content:

In this paper, we describe a framework for the selection of the best charging station when plug-in electric vehicles (PEV) need to recharge their batteries, while at the same time the power utilities, which own the charging stations (CS), optimize their revenue. We use two-way communication to transmit positioning information, which is a key factor so that the plug-in electric vehicles evaluate its required energy. Within this framework, we also describe a procedure that is implemented in a distributed manner and is based on a non-cooperative oligopoly game that makes use of differentiated products theory and conjectural variations to provide a Nash equilibrium in prices and quantities.
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paper_title: Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

paper_content:

Abstract Electric Vehicle (EV) navigation system normally requires multiple charging in a long origin destination (OD) trip, which makes it different from the traditional vehicle navigation system. Fast charging is an ideal charging solution in multiple charging because of its high charging efficiency, rapid transient response and short charging time. However, EVs fast charging at on-peak hours may lead to overloading of distribution system. To solve the problem above, a real-time pricing (RTP) policy, as one of the main thrusts for load shaving application, should be introduced, which will bring new challenges in EV navigation systems due to the characteristic of time-dependence. In this paper, we address an optimization problem for EV navigation systems under the RTP policy with the consideration of both charging and routing. By using the EV arrival states and the traffic parameters, the original traffic network can be extended to a feasible state graph. An Improved Chrono-SPT (ICS) algorithm is provided to derive the optimal decision sequence, which provides an optimal routing and charging policy. Furthermore, a Simplify-Charge-Control (SCC) algorithm is also presented to reduce the computation complexity of the ICS algorithm. Simulations show the effectiveness of both ICS and SCC algorithms and the computation complexity of SCC algorithm is much simplified within acceptable deviation of optimal cost under approximation pricing (AP) than that in ICS algorithm.
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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: Real-Time Welfare-Maximizing Regulation Allocation in Dynamic Aggregator-EVs System

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 dynamic EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among its EVs. Compared to previous works, WMRA accommodates a wide spectrum of vital system characteristics, including dynamics of EV, limited EV battery size, EV battery degradation cost, and the cost of using external energy sources for the aggregator. The algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate that WMRA is away from the optimum by O(1/V), where V is a controlling parameter depending on EV's battery size. In addition, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.
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paper_title: An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

paper_content:

Wind power is gaining in significance as an important renewable source of clean energy. However, due to their inherent uncertainty, wind generators are often unable to participate in the forward electricity markets like the more predictable and controllable conventional generators. Given this, virtual power plants (VPPs) are being advocated as a solution for increasing the reliability of such intermittent renewable sources. In this paper, we take this idea further by considering VPPs as coalitions of wind generators and electric vehicles, where wind generators seek to use electric vehicles (EVs) as a storage medium to overcome the vagaries of generation. Using electric vehicles in this manner has the advantage that, since the number of EVs is increasing rapidly, no initial investment in dedicated storage is needed. In more detail, we first formally model the VPP and then, through an operational model based on linear programming, we show how the supply to the Grid and storage in the EV batteries can be scheduled to increase the profit of the VPP, while also paying for the storage using a novel scheme. The feasibility of our approach is examined through a realistic case-study, using real wind power generation data, corresponding electricity market prices and electric vehicles' characteristics.
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paper_title: Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV)

paper_content:

Deployment of PHEV will initiate an integration of transportation and power systems. Intuitively, the PHEVs will constitute an additional demand to the electricity grid, potentially violating converter or line capacities when recharging. Smart management schemes can alleviate possible congestions in power systems, intelligently distributing available energy. As PHEV are inherently independent entities, an agent based approach is expedient. Nonlinear pricing will be adapted to model and manage recharging behavior of large numbers of autonomous PHEV agents connecting in one urban area modelled as an energy hub. The scheme will incorporate price dependability. An aggregation entity, with no private information about its customers, will manage the PHEV agents whose individual parameters will be based on technical constraints and individual objectives. Analysis of the management scheme will give implications for PHEV modelling and integration schemes as well as tentative ideas of possible repercussions on power systems.
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paper_title: Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

paper_content:

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: A Flexible Stochastic Optimization Method for Wind Power Balancing With PHEVs

paper_content:

This paper proposes a flexible optimization method, based on state of the art algorithms, for the smart control of plug-in hybrid electric vehicles (PHEVs) to balance wind power production. The problem is approached from the perspective of a balance responsible party (BRP) with a large share of wind power in its portfolio. The BRP uses controllable PHEVs to minimize the imbalance of its portfolio resulting from wind power forecast errors. A Markov Decision Process (MDP) formulation in combination with dynamic programming is used to solve the multistage stochastic problem. The main difficulty for applying MDPs to this problem is to efficiently include time interdependence of the wind power forecast error. In the presented approach, the probability distribution and time interdependence of the forecast error are represented by a scenario tree. Because of the MDP formulation, the algorithm is adaptable to deal with different transition models and constraints. This feature enables to use the algorithm in a dynamic environment such as the future smart grid. To demonstrate this, a generic charging model for PHEVs is used in the BRP wind balancing case. The flexibility of the algorithm is shown by investigating the solution for different degrees of complexity in the charging model.
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paper_title: Charging scheduling with minimal waiting in a network of electric vehicles and charging stations

paper_content:

Environment preservation has become a prominent issue around the world. As traditional internal combustion engine (ICE) vehicles have been major contributors of air pollution, electric vehicles (EVs) are gaining popularity. However, due to the limited electricity supply of battery pack, EVs need to be charged frequently and each charge takes long time. This may degrade travel efficiency and driver comfort. To address this issue, this paper aims to minimize charging waiting time through intelligently scheduling charging activities spatially and temporally. A theoretical study has been conducted to formulate the waiting time minimized charging scheduling problem and derive a performance upper bound (i.e., the theoretical lower bound of charging waiting time). Based on the insights discovered from the theoretical analysis, a practical distributed scheme has been proposed. Extensive simulation results verify that the proposed design can achieve a waiting time near the theoretical lower bound.
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paper_title: Scalable Real-Time Electric Vehicles Charging With Discrete Charging Rates

paper_content:

Large penetration of electric vehicles (EVs) can have a negative impact on the power grid, e.g., increased peak load and losses, that can be largely mitigated using coordinated charging strategies. In addition to shifting the charging process to the night valley when the electricity price is lower, this paper explicitly considers the EV owner convenience that can be mainly characterized by a desired state of charge at the departure time. To this end, the EV charging procedure is defined as an uninterruptible process that happens at a given discrete charging rate and the coordinated charging is formulated as a scheduling problem. The scalable real-time greedy (S-RTG) algorithm is proposed to schedule a large population of EVs in a decentralized fashion, explicitly considering the EV owner criteria. Unlike the majority of existing approaches, the S-RTG algorithm does not rely on iterative procedures and does not require heavy computations, broadcast messages, or extensive bi-directional communications. Instead, the proposed algorithm schedules one EV at a time with simple computations, only once (i.e., at the time the EV connects to the grid), and only requires low-speed communication capability making it suitable for real-time implementation. Numerical simulations with significant EVs penetration and comparative analysis with scheduling policies demonstrate the effectiveness of the proposed algorithm.
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paper_title: Real-Time Distributed Control for Smart Electric Vehicle Chargers: From a Static to a Dynamic Study

paper_content:

At high penetrations, uncontrolled electric vehicle (EV) charging has the potential to cause line and transformer congestion in the distribution network. Instead of upgrading components to higher nameplate ratings, we investigate the use of real-time control to limit EV load to the available capacity in the network. Inspired by rate control algorithms in computer networks such as TCP, we design a measurement-based, real-time, distributed, stable, efficient, and fair charging algorithm using the dual-decomposition approach. We show through extensive numerical simulations and power flow analysis on a test distribution network that this algorithm operates successfully in both static and dynamic settings, despite changes in home loads and the number of connected EVs. We find that our algorithm rapidly converges from large disturbances to a stable operating point. We show that in a test setting, for an acceptable level of overload, only 70 EVs could be fully charged without control, whereas up to around 700 EVs can be fully charged using our control algorithm. This compares well with the maximum supportable population of approximately 900 EVs. Our work also provides engineering guidelines for choosing the control parameters and setpoints in a distribution network.
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paper_title: A contract-based approach for ancillary services in V2G networks: Optimality and learning

paper_content:

With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.
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paper_title: Rapid-Charging Navigation of Electric Vehicles Based on Real-Time Power Systems and Traffic Data

paper_content:

Electric vehicles (EVs) have attracted growing attention in recent years. However, daily charging of EVs, in particular rapid charging, may impact power systems, especially during peak hours, and the effects may occur in different places as traffic conditions change. To address these issues, we describe an integrated rapid-charging navigation strategy that considers both the traffic conditions and the status of the power grid. The system is based on an intelligent transport system (ITS), and contains four modules: a power system control center (PSCC), an ITS center, charging stations, and EV terminals. The PSCC calculates the available charging capacity and station charging capacity based on power grid data and transmits the results to the charging stations. The charging stations determine their charging plans and estimate the available charging power for future EVs (CPFE), and transmit these data to the ITS center. After receiving CPFE data and traffic data from the ITS center, the EV terminal estimates the total time for charging (TTC) for different stations, which includes the driving time, waiting time, and charging time. The driver can view these results and choose to be navigated to the charging station corresponding to the minimum TTC. The modular design of the navigation system reduces data transmission, which protects the drivers' privacy since they can choose which charging station to use and are not required to send any data to the ITS system. Simulation results demonstrate the feasibility of the proposed method for different working conditions for power system and traffic conditions.
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paper_title: Fast MPC-Based Coordination of Wind Power and Battery Energy Storage Systems

paper_content:

In this paper, a model predictive control (MPC)-based coordinated scheduling framework for variable wind generation and battery energy storage systems (BESSs) is presented. On the basis of the short-term forecast of available wind generation and price information, a joint look-ahead optimization is performed by the wind farm and storage system to determine their net power injection to the electric power grid. In conjunction with moderate battery capacity, the excess unpredictable wind generation can be used to charge the battery storage and vice versa. The benefits of the proposed scheduling approach are that (1) the combined profit of wind generation and BESS is increased; (2) the net power injection from the wind farm into the power grid is smoothed out; and (3) the look-ahead optimization updates the price prediction in a moving horizon, which leads to more robust profit for wind farm and BESS against price uncertainties. By formulating the MPC-based coordinated scheduling as a quadratic programming problem, several numerically efficient algorithms to compute the optimal control strategy for wind generation and BESS are proposed. The effectiveness of the proposed algorithm in a modified IEEE 24-bus reli- ability test system with aggregated plug-in hybrid electric vehicles is demonstrated. It is shown that the proposed algorithm can increase the joint profit of wind farm and BESS while smoothing out the net power injection to the electricity grid. The proposed MPC-based scheduling problem can be solved in approximately 400 ms, which makes the framework implementable in realtime electricity market operations. DOI: 10.1061/(ASCE)EY.1943-7897.0000071. © 2012 American Society of Civil Engineers. CE Database subject headings: Predictions; Wind power; Energy storage; Vehicles. Author keywords: Model predictive control; Wind power integration; Battery energy storage systems; Plug-in hybrid electric vehicle; Power system economic dispatch; Electricity market.
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paper_title: Charging games in networks of electrical vehicles

paper_content:

In this paper, a static non-cooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each EV aims at choosing the time at which it starts charging its battery in order to minimize an individual cost which is mainly related to the total power delivered by the transformer, the location of the time interval over which the charging operation is performed, and the charging duration needed for the considered EV to have its battery fully recharged. As individual cost functions are assumed to be memoryless, it is possible to show that the game of interest is always an ordinal potential game. More precisely, both an atomic and nonatomic versions of the charging game are considered. In both cases, equilibrium analysis is conducted. In particular, important issues such as equilibrium uniqueness and efficiency are tackled. Interestingly, both analytical and numerical results show that the efficiency loss due to decentralization (e.g., when cost functions such as distribution network Joule losses or life of residential distribution transformers when no thermal inertia is assumed) induced by charging is small and the corresponding “efficiency”, a notion close to the Price of Anarchy, tends to one when the number of EV increases.
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paper_title: Online mechanism design for electric vehicle charging

paper_content:

Plug-in hybrid electric vehicles are expected to place a considerable strain on local electricity distribution networks, requiring charging to be coordinated in order to accommodate capacity constraints. We design a novel online auction protocol for this problem, wherein vehicle owners use agents to bid for power and also state time windows in which a vehicle is available for charging. This is a multi-dimensional mechanism design domain, with owners having non-increasing marginal valuations for each subsequent unit of electricity. In our design, we couple a greedy allocation algorithm with the occasional "burning" of allocated power, leaving it unallocated, in order to adjust an allocation and achieve monotonicity and thus truthfulness. We consider two variations: burning at each time step or on-departure. Both mechanisms are evaluated in depth, using data from a real-world trial of electric vehicles in the UK to simulate system dynamics and valuations. The mechanisms provide higher allocative efficiency than a fixed price system, are almost competitive with a standard scheduling heuristic which assumes non-strategic agents, and can sustain a substantially larger number of vehicles at the same per-owner fuel cost saving than a simple random scheme.
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paper_title: Optimizing the Energy Delivery via V2G Systems Based on Stochastic Inventory Theory

paper_content:

In this paper, we study the optimal energy delivery problem from viewpoints of both the vehicle owner and aggregator, in load shaving services of a vehicle-to-grid (V2G) system. We formulate the optimization problem based on a general plug-in hybrid electric vehicle (PHEV) model, taking into account the randomness in vehicle mobility, time-of-use electricity pricing, and realistic battery modeling. Stochastic inventory theory is applied to analyze the problem. We mathematically prove that a state-dependent (S,S') policy is optimal for the daily energy cost minimization of each vehicle, and develop an estimation algorithm to calculate the parameters of the optimal policy for practical applications. Furthermore, we investigate the multi-vehicle aggregator design problem by considering the power system constraints. A policy adjustment scheme is proposed to adjust the values of S and S' with respect to the optimal policy adopted by each PHEV, such that the aggregated recharging and discharging power constraints of the power system can be satisfied, while minimizing the incremental cost (or revenue loss) of PHEV owners. Based on characteristics of the state-dependent (S,S') policy and our proposed policy adjustment scheme, the optimal aggregator operation problem is transformed into a convex optimization one which can be readily solved by existing algorithms. The performance of our proposed schemes is evaluated via simulations based on real data collected from Canadian utilities, households, and commuters.
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paper_title: Coordinated Charging of Electric Vehicles for Congestion Prevention in the Distribution Grid

paper_content:

Distributed energy resources (DERs), like electric vehicles (EVs), can offer valuable services to power systems, such as enabling renewable energy to the electricity producer and providing ancillary services to the system operator. However, these new DERs may challenge the distribution grid due to insufficient capacity in peak hours. This paper aims to coordinate the valuable services and operation constraints of three actors: the EV owner, the Fleet operator (FO) and the Distribution system operator (DSO), considering the individual EV owner's driving requirement, the charging cost of EV and thermal limits of cables and transformers in the proposed market framework. Firstly, a theoretical market framework is described. Within this framework, FOs who represent their customer's (EV owners) interests will centrally guarantee the EV owners' driving requirements and procure the energy for their vehicles with lower cost. The congestion problem will be solved by a coordination between DSO and FOs through a distribution grid capacity market scheme. Then, a mathematical formulation of the market scheme is presented. Further, some case studies are shown to illustrate the effectiveness of the proposed solutions.
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paper_title: Pricing the Internet With Multibid Auctions

paper_content:

Usage-based or congestion-based charging schemes have been regarded as a relevant way to control congestion and to differentiate services among users in telecommunication networks; auctioning for bandwidth appears as one of several possibilities. In a previous work, the authors designed a multibid auction scheme where users compete for bandwidth at a link by submitting several couples (e.g., amount of bandwidth asked, associated unit price) so that the link allocates the bandwidth and computes the charge according to the second price principle. They showed that incentive compatibility and efficiency among other properties are verified. We propose in the present paper to extend this scheme to the case of a network by using the properties/assumptions that the backbone network is overprovisioned and the access networks have a tree structure
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paper_title: Optimal decentralized protocol for electric vehicle charging

paper_content:

Motivated by the power-grid-side challenges in the integration of electric vehicles, we propose a decentralized protocol for negotiating day-ahead charging schedules for electric vehicles. The overall goal is to shift the load due to electric vehicles to fill the overnight electricity demand valley. In each iteration of the proposed protocol, electric vehicles choose their own charging profiles for the following day according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. This protocol is guaranteed to converge, irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. At convergence, the l 2 norm of the aggregated demand is minimized, and the aggregated demand profile is as “flat” as it can possibly be. The proposed protocol needs no coordination among the electric vehicles, hence requires low communication and computation capability. Simulation results demonstrate convergence to optimal collections of charging profiles within few iterations.
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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: Experiences, barriers and success factors

paper_content:

Although microgrids have been researched for over a decade and recognized for their multitude of benefits to improve power reliability, security, sustainability, and decrease power costs for the consumer, they have still not reached rapid commercial growth. The main aim of this research is to identify the common barriers and ultimate success factors to implementing a microgrid in the real world. We found that microgrids vary significantly depending on location, components, and optimization goals, which cause them to experience different types of challenges and barriers. However, the most common barriers were identified and grouped into four categories: technical, regulatory, financial, and stakeholder, based on the literature and overlying patterns recognized amongst the thirteen case studies. The most common technical barriers include problems with technology components, dual-mode switching from grid-connected to island mode, power quality and control, and protection issues. There is extensive research on how to overcome these issues, so technical solutions are becoming available yet case specific. Regulatory barriers exist due to interconnection rules with the main grid and the prohibition of bi-directional power flow and local power trading between microgrid and the main network. The latter issue is the barrier experienced most often and has only recently been addressed, so solutions need further research. The main financial barrier is still the burden of high investment and replacement costs of the microgrid. This can be resolved with proper market support in the short term and might naturally resolve itself through learning over the long run. Lastly, stakeholder barriers include issues with conflicting self-interest and trust, and having the expertise to manage operations. These stakeholder barriers are not yet addressed in the literature and need to be further researched.
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paper_title: Charging station selection optimization for plug-in electric vehicles: An oligopolistic game-theoretic framework

paper_content:

In this paper, we describe a framework for the selection of the best charging station when plug-in electric vehicles (PEV) need to recharge their batteries, while at the same time the power utilities, which own the charging stations (CS), optimize their revenue. We use two-way communication to transmit positioning information, which is a key factor so that the plug-in electric vehicles evaluate its required energy. Within this framework, we also describe a procedure that is implemented in a distributed manner and is based on a non-cooperative oligopoly game that makes use of differentiated products theory and conjectural variations to provide a Nash equilibrium in prices and quantities.
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paper_title: Decentralized Charging Control of Large Populations of Plug-in Electric Vehicles

paper_content:

This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand “valley”. The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation.
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paper_title: Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

paper_content:

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: Towards optimal energy store-carry-and-deliver for PHEVs via V2G system

paper_content:

As an important component of smart grid, the vehicle-to-grid (V2G) system is recently introduced to enable bidirectional energy delivery between the power grid and plug-in electric vehicles. Communication technology is incorporated to facilitate the energy delivery by providing electricity pricing and energy demand information. However, different from the stationary energy storage systems, the energy store-carry-and-deliver mechanism for a V2G system poses new challenges for performance optimization, such as bi-directional energy flow and non-stationary energy demand. How to utilize the statistical information provided by the communication system to achieve efficient energy delivery is critical for a V2G system and is still an open issue. In this paper, we address a specific problem in this new research area, i.e., daily energy cost minimization of vehicle owners under time-of-use (TOU) electricity pricing. We investigate a plug-in hybrid electric vehicle (PHEV) with a realistic battery model, which is general for both battery electric cars and plug-in hybrids. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and TOU electricity price. We prove the optimality of a state-dependent double-threshold (or (S, S′)) policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average (EWMA) algorithm is used to estimate the statistics of PHEV mobility and energy demand. The performance of the proposed scheme is demonstrated by simulations based on survey and real data collected from Canadian households. Numerical results indicate that our proposed scheme performs closely to a scheme with a priori knowledge of the PHEV mobility and energy demand information. Compared with the existing approaches, the proposed scheme can achieve energy cost reduction, which increases with the battery capacity.
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paper_title: An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

paper_content:

Wind power is gaining in significance as an important renewable source of clean energy. However, due to their inherent uncertainty, wind generators are often unable to participate in the forward electricity markets like the more predictable and controllable conventional generators. Given this, virtual power plants (VPPs) are being advocated as a solution for increasing the reliability of such intermittent renewable sources. In this paper, we take this idea further by considering VPPs as coalitions of wind generators and electric vehicles, where wind generators seek to use electric vehicles (EVs) as a storage medium to overcome the vagaries of generation. Using electric vehicles in this manner has the advantage that, since the number of EVs is increasing rapidly, no initial investment in dedicated storage is needed. In more detail, we first formally model the VPP and then, through an operational model based on linear programming, we show how the supply to the Grid and storage in the EV batteries can be scheduled to increase the profit of the VPP, while also paying for the storage using a novel scheme. The feasibility of our approach is examined through a realistic case-study, using real wind power generation data, corresponding electricity market prices and electric vehicles' characteristics.
---
paper_title: Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV)

paper_content:

Deployment of PHEV will initiate an integration of transportation and power systems. Intuitively, the PHEVs will constitute an additional demand to the electricity grid, potentially violating converter or line capacities when recharging. Smart management schemes can alleviate possible congestions in power systems, intelligently distributing available energy. As PHEV are inherently independent entities, an agent based approach is expedient. Nonlinear pricing will be adapted to model and manage recharging behavior of large numbers of autonomous PHEV agents connecting in one urban area modelled as an energy hub. The scheme will incorporate price dependability. An aggregation entity, with no private information about its customers, will manage the PHEV agents whose individual parameters will be based on technical constraints and individual objectives. Analysis of the management scheme will give implications for PHEV modelling and integration schemes as well as tentative ideas of possible repercussions on power systems.
---
paper_title: Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

paper_content:

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: 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: Network constrained transactive control for electric vehicles integration

paper_content:

Electric vehicles (EVs) are commonly recognized as smart grid assets in addition to their environmental benefits. However, uncoordinated charging or sole cost minimization based charging of electric vehicles may bring undesirable peak demands and voltage violations in the distribution system. This paper applies the transactive control concept to integrate electric vehicles into the power distribution system with the purpose of minimizing the charging cost of electric vehicles as well as preventing grid congestions and voltage violations. A hierarchical EV management system is proposed where three actors are considered: distribution system operator (DSO), fleet operators and EV owners. In the lower level of the hierarchy, the fleet operator centrally manages the charging schedule of electric vehicles; in the upper level of the hierarchy, the DSO uses transactive control technique to coordinate the aggregated charging behavior of fleet operators. Detailed models are presented to illustrate the operation of the hierarchical EV management system. In the end, simulations are presented to show the effectiveness of the proposed solutions.
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paper_title: A Flexible Stochastic Optimization Method for Wind Power Balancing With PHEVs

paper_content:

This paper proposes a flexible optimization method, based on state of the art algorithms, for the smart control of plug-in hybrid electric vehicles (PHEVs) to balance wind power production. The problem is approached from the perspective of a balance responsible party (BRP) with a large share of wind power in its portfolio. The BRP uses controllable PHEVs to minimize the imbalance of its portfolio resulting from wind power forecast errors. A Markov Decision Process (MDP) formulation in combination with dynamic programming is used to solve the multistage stochastic problem. The main difficulty for applying MDPs to this problem is to efficiently include time interdependence of the wind power forecast error. In the presented approach, the probability distribution and time interdependence of the forecast error are represented by a scenario tree. Because of the MDP formulation, the algorithm is adaptable to deal with different transition models and constraints. This feature enables to use the algorithm in a dynamic environment such as the future smart grid. To demonstrate this, a generic charging model for PHEVs is used in the BRP wind balancing case. The flexibility of the algorithm is shown by investigating the solution for different degrees of complexity in the charging model.
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paper_title: Charging scheduling with minimal waiting in a network of electric vehicles and charging stations

paper_content:

Environment preservation has become a prominent issue around the world. As traditional internal combustion engine (ICE) vehicles have been major contributors of air pollution, electric vehicles (EVs) are gaining popularity. However, due to the limited electricity supply of battery pack, EVs need to be charged frequently and each charge takes long time. This may degrade travel efficiency and driver comfort. To address this issue, this paper aims to minimize charging waiting time through intelligently scheduling charging activities spatially and temporally. A theoretical study has been conducted to formulate the waiting time minimized charging scheduling problem and derive a performance upper bound (i.e., the theoretical lower bound of charging waiting time). Based on the insights discovered from the theoretical analysis, a practical distributed scheme has been proposed. Extensive simulation results verify that the proposed design can achieve a waiting time near the theoretical lower bound.
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Title: Charging Electric Vehicles in the Smart City: A Survey of Economy-driven Approaches
Section 1: Introduction
Description 1: Briefly introduce the context, motivation, and objectives of the paper.

Section 2: Facilities for Electric Vehicle Charging
Description 2: Describe various EV charging technologies, including BEVs and PHEVs, and charging methods and standards.

Section 3: Electric Vehicles - An Enabler of the Smart Grid and a Participant in Electricity Markets
Description 3: Discuss how EVs interact with the smart grid and participate in electricity markets, focusing on Demand Response (DR) and Energy Storage.

Section 4: Dealing with Self-interested Actors
Description 4: Explain how EV charging involves multiple actors with differing objectives and introduce game theory as a framework for managing these interactions.

Section 5: Unidirectional Charging Mechanisms
Description 5: Present the economic approaches to manage unidirectional EV charging, considering static and dynamic models, and mobility aspects.

Section 6: Bidirectional Energy Trading
Description 6: Review control mechanisms for scenarios where EVs can buy and sell electricity, including individual arbitrage and V2G for regulation services and renewable energy storage.

Section 7: Communication Aspects
Description 7: Highlight the importance of communication in implementing EV charging schemes and detail the necessary information exchanges and their frequency.

Section 8: Challenges
Description 8: Summarize the economic approaches and identify challenges and open research questions in EV charging management.

Section 9: Conclusion
Description 9: Conclude the paper by summarizing the key findings and discussing potential future research directions in EV charging management.