Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/659648
Title: A Game Theoretic Approach to Risk-Based Optimal Bidding Strategies for Electric Vehicle Aggregators in Electricity Markets With Variable Wind Energy Resources
Authors: Hongyu Wu;Mohammad Shahidehpour;Ahmed Alabdulwahab;Abdullah Abusorrah
subject: Nash equilibrium|Electric vehicle aggregators|incomplete information|strategic bidding|conditional value at risk (CVaR)|wind energy
Year: 2016
Publisher: IEEE
Abstract: This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators risks caused by the uncertainties. The EV aggregators optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level mixed integer linear programming (MILP) problem using the prime-dual formulation with linearized constraints. The progressive hedging algorithm (PHA) is utilized to solve the resulting single-level MILP problem. A game theoretic approach is developed for analyzing the competition among the EV aggregators. Numerical cases are studied for a modified 6-bus system and the IEEE 118-bus system. The results show the validity of the proposed approach and the impact of the aggregators bidding strategies on the stochastic electricity market operation.
URI: http://localhost/handle/Hannan/162648
http://localhost/handle/Hannan/659648
ISSN: 1949-3029
1949-3037
volume: 7
issue: 1
Appears in Collections:2016

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Title: A Game Theoretic Approach to Risk-Based Optimal Bidding Strategies for Electric Vehicle Aggregators in Electricity Markets With Variable Wind Energy Resources
Authors: Hongyu Wu;Mohammad Shahidehpour;Ahmed Alabdulwahab;Abdullah Abusorrah
subject: Nash equilibrium|Electric vehicle aggregators|incomplete information|strategic bidding|conditional value at risk (CVaR)|wind energy
Year: 2016
Publisher: IEEE
Abstract: This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators risks caused by the uncertainties. The EV aggregators optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level mixed integer linear programming (MILP) problem using the prime-dual formulation with linearized constraints. The progressive hedging algorithm (PHA) is utilized to solve the resulting single-level MILP problem. A game theoretic approach is developed for analyzing the competition among the EV aggregators. Numerical cases are studied for a modified 6-bus system and the IEEE 118-bus system. The results show the validity of the proposed approach and the impact of the aggregators bidding strategies on the stochastic electricity market operation.
URI: http://localhost/handle/Hannan/162648
http://localhost/handle/Hannan/659648
ISSN: 1949-3029
1949-3037
volume: 7
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7349233.pdf1.1 MBAdobe PDFThumbnail
Preview File
Title: A Game Theoretic Approach to Risk-Based Optimal Bidding Strategies for Electric Vehicle Aggregators in Electricity Markets With Variable Wind Energy Resources
Authors: Hongyu Wu;Mohammad Shahidehpour;Ahmed Alabdulwahab;Abdullah Abusorrah
subject: Nash equilibrium|Electric vehicle aggregators|incomplete information|strategic bidding|conditional value at risk (CVaR)|wind energy
Year: 2016
Publisher: IEEE
Abstract: This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators risks caused by the uncertainties. The EV aggregators optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level mixed integer linear programming (MILP) problem using the prime-dual formulation with linearized constraints. The progressive hedging algorithm (PHA) is utilized to solve the resulting single-level MILP problem. A game theoretic approach is developed for analyzing the competition among the EV aggregators. Numerical cases are studied for a modified 6-bus system and the IEEE 118-bus system. The results show the validity of the proposed approach and the impact of the aggregators bidding strategies on the stochastic electricity market operation.
URI: http://localhost/handle/Hannan/162648
http://localhost/handle/Hannan/659648
ISSN: 1949-3029
1949-3037
volume: 7
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7349233.pdf1.1 MBAdobe PDFThumbnail
Preview File