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• W2497420502 abstract "The matching between random wind supply and electric vehicle (EV) charging demand can reduce the requirement of traditional power sources and the emission of CO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> . This problem is of great practical interest but involves system dynamics in multiple timescales. We consider this an important problem in this paper. In order to capture the randomness in the wind supply and EV charging demand, we formulate the problem as a bilevel Markov decision process. At the upper level, the charging demand of EVs in different locations is aggregated into multiple aggregators. The system operator dispatches power among the aggregators in a coarse timescale to maximize the wind power utilization. At the lower level, the aggregator schedules the charging process of individual EVs at a finer timescale to minimize the charging cost. In order to solve this large-scale problem, a bilevel simulation-based policy improvement (SBPI) method is developed. It is mathematically proved that the SBPI can improve from base policies in both levels. The performance of this multi-timescale and bilevel coordination approach is demonstrated through case studies in the city of Beijing." @default.
• W2497420502 created "2016-08-23" @default.
• W2497420502 creator A5044965133 @default.
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• W2497420502 creator A5075845093 @default.
• W2497420502 date "2017-04-01" @default.
• W2497420502 modified "2023-10-17" @default.
• W2497420502 title "A Multi-Timescale and Bilevel Coordination Approach for Matching Uncertain Wind Supply With EV Charging Demand" @default.
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• W2497420502 doi "https://doi.org/10.1109/tase.2016.2585180" @default.
• W2497420502 hasPublicationYear "2017" @default.
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