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18
Development of an optimal vehicle-togrid aggregator for frequency regulation
- IEEE Trans. Smart Grid
, 2010
"... Abstract—For vehicle-to-grid (V2G) frequency regulation ser-vices, we propose an aggregator that makes efficient use of the dis-tributed power of electric vehicles to produce the desired grid-scale power. The cost arising from the battery charging and the revenue obtained by providing the regulation ..."
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Abstract—For vehicle-to-grid (V2G) frequency regulation ser-vices, we propose an aggregator that makes efficient use of the dis-tributed power of electric vehicles to produce the desired grid-scale power. The cost arising from the battery charging and the revenue obtained by providing the regulation are investigated and repre-sented mathematically. Some design considerations of the aggre-gator are also discussed together with practical constraints such as the energy restriction of the batteries. The cost function with con-straints enables us to construct an optimization problem. Based on the developed optimization problem, we apply the dynamic pro-gramming algorithm to compute the optimal charging control for each vehicle. Finally, simulations are provided to illustrate the op-timality of the proposed charging control strategy with variations of parameters. Index Terms—Aggregator, battery, dynamic programming, elec-tric vehicle, plug-in hybrid electric vehicle (PHEV), regulation, ve-hicle-to-grid (V2G). I.
Smart grid -- the new and improved power grid: A survey
- IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
, 2011
"... The Smart Grid . . . power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart i ..."
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Cited by 58 (2 self)
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The Smart Grid . . . power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem. For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.
Economics of electric vehicle charging: A game theoretic approach
- IEEE Transactions on Smart Grid
, 2012
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Real-Time Vehicle-to-Grid Control Algorithm under Price Uncertainty
- IEEE SmartGridComm
, 2011
"... Abstract—The vehicle-to-grid (V2G) system enables energy flow from the electric vehicles (EVs) to the grid. The distributed power of the EVs can either be sold to the grid or be used to provide frequency regulation service when V2G is implemented. A V2G control algorithm is necessary to decide wheth ..."
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Abstract—The vehicle-to-grid (V2G) system enables energy flow from the electric vehicles (EVs) to the grid. The distributed power of the EVs can either be sold to the grid or be used to provide frequency regulation service when V2G is implemented. A V2G control algorithm is necessary to decide whether the EV should be charged, discharged, or provide frequency regulation service in each hour. The V2G control problem is further complicated by the price uncertainty, where the electricity price is determined dynamically every hour. In this paper, we study the real-time V2G control problem under price uncertainty. We model the electricity price as a Markov chain with unknown transition probabilities and formulate the problem as a Markov decision process (MDP). This model features implicit estimation of the impact of future electricity prices and current control operation on long-term profits. The Q-learning algorithm is then used to adapt the control operation to the hourly available price in order to maximize the profit for the EV owner during the whole parking time. We evaluate our proposed V2G control algorithm using both the simulated price and the actual price from PJM in 2010. Simulation results show that our proposed algorithm can work effectively in the real electricity market and it is able to increase the profit significantly compared with the conventional EV charging scheme. I.
Plug-in Electric Vehicle Infrastructure: A Foundation for Electrified Transportation
, 2010
"... Plug-in electric vehicles (PEVs)—which include all-electric vehicles and plug-in hybrid electric vehicles—provide a new opportunity for reducing oil consumption by drawing power from the electric grid. To maximize the benefits of PEVs, the emerging PEV infrastructure—from battery manufacturing to co ..."
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Cited by 5 (0 self)
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Plug-in electric vehicles (PEVs)—which include all-electric vehicles and plug-in hybrid electric vehicles—provide a new opportunity for reducing oil consumption by drawing power from the electric grid. To maximize the benefits of PEVs, the emerging PEV infrastructure—from battery manufacturing to communication and control between the vehicle and the grid—must provide access to clean electricity, satisfy stakeholder expectations, and ensure safety. Currently, codes and standards organizations are collaborating on a PEV infrastructure plan. Establishing a PEV infrastructure framework will create new opportunities for business and job development initiating the move toward electrified transportation. This paper summarizes the components of the PEV infrastructure, challenges and opportunities related to the design and deployment of the infrastructure, and the potential benefits.
Realtime modeling of distributed plug-in vehicles for V2G transactions
- in Proceedings of the IEEE Energy Conversion Congress and Exposition
, 2009
"... Abstract-- A real-time model of a fleet of plug-in vehicles performing vehicle-to-grid (V2G) power transactions is presented in this paper. Two sets of four vehicles are connected to both the grid and to each other though a short transmission line. The real-time modeling is carried out on a Real-Tim ..."
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Abstract-- A real-time model of a fleet of plug-in vehicles performing vehicle-to-grid (V2G) power transactions is presented in this paper. Two sets of four vehicles are connected to both the grid and to each other though a short transmission line. The real-time modeling is carried out on a Real-Time Digital Simulator (RTDS). This setup allows for two different case studies with either two distributed sets of four individual vehicles or two smart parking lots with several vehicles. Output power levels and charge/discharge times are scheduled intelligently in order to maximize profits from grid transactions based on one-day ahead electricity pricing. The effects of grid faults on V2G operations are presented. Index Terms — computational intelligence, particle swarm optimization, plug-in vehicles, power transactions, real-time simulation, real-time digital simulator, smart parking lots, vehicle-to-grid (V2G) I.
Optimal V2G scheduling of electric vehicles and unit commitment using chemical reaction optimization
- in Proc. IEEE Congr. Evol. Comput., Cancun
, 2013
"... Abstract—An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle’s battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. I ..."
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Abstract—An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle’s battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a new formulation of the joint scheduling of EV and Unit Commitment (UC), called EVUC. Our formulation considers the characteristics of EVs while optimizing the system total running cost. We employ Chemical Reaction Optimization (CRO), a general-purpose optimization algorithm to solve this problem and the simulation results on a widely used set of instances indicate that CRO can effectively optimize this problem. Index Terms—Electric vehicle, unit commitment, chemical reaction optimization, metaheuristic, power system, smart grid, vehicle-to-grid. I.
w,t EV demand during trip periods [kW] Nw Battery capacity of the EV [kWh]
"... Optimal energy management for a residential microgrid including a vehicle-to-grid system Lucı́a Igualada, Cristina Corchero, Miguel Cruz-Zambrano, and F.-Javier Heredia Abstract—An optimization model is proposed to manage a residential microgrid including a charging spot with a vehicle-to-grid syste ..."
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Optimal energy management for a residential microgrid including a vehicle-to-grid system Lucı́a Igualada, Cristina Corchero, Miguel Cruz-Zambrano, and F.-Javier Heredia Abstract—An optimization model is proposed to manage a residential microgrid including a charging spot with a vehicle-to-grid system and renewable energy sources. In order to achieve a realistic and convenient management, we take into account: (1) the household load split into three different profiles depending on the characteristics of the elements considered; (2) a realistic approach to owner behavior by introducing the novel concept of range anxiety; (3) the vehicle battery management considering the mobility profile of the owner and (4) different domestic renewable energy sources. We consider the microgrid operated in grid-connected mode. The model is executed one-day-ahead and generates a schedule for all components of the microgrid. The results obtained show daily costs in the range of 2.82eto 3.33e; the proximity of these values to the actual energy costs for Spanish households validate the modeling. The experimental results of applying the designed managing strategies show daily costs savings of nearly 10%. Index Terms—Optimal management, smart grids, vehicle-to-grid (V2G), range anxiety, renewable generation, residential microgrids I. NOTATION Sets R Set of devices with shiftable load, r ∈ R T Set of time intervals, t ∈ T Uw Intervals where the EV is plugged (Uw ⊆ T) Lr Intervals of shiftable load profile (Lr ⊆ T), l ∈ Lr W Set of electrical vehicles (EV fleet), w ∈ W
CIRED Workshop -Lisbon A AN N E EC CO ON NO OM MI IC C M MO OD DE EL L F FO OR R P PO OW WE ER R E EX XC CH HA AN NG GE E O OF F V V2 2G GS S I IN N P PA AR RK KI IN NG G L LO OT TS S
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Special Issue on Wide Area Monitoring, Protection and Control
, 2010
"... Wide area control for improving stability of a power system with plug-in electric vehicles ..."
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Wide area control for improving stability of a power system with plug-in electric vehicles
