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Study on Aggregator’s Strategy of Controlling Electric Vehicles to Compensate Imbalances in Power Systems Using Reinforcement Learning

Ryuhei Furuta and Ryuji Matsuhashi
The University of Tokyo, Electrical Engineering Department, Tokyo, Japan
Abstract—In Japan, electricity market became open to any companies in 2016. Due to this legal change, a lot of new power producer and supplier (PPS) could join this market and they aim to maximize their profits. Now, they have to consider the electricity control method because the imbalance penalty would increase in the future. In this paper, we suppose a control strategy of these aggregators considering this imbalance penalty. In this system, these aggregators can control each household’s electric vehicle as a control device by paying some money to the EV owners. Of course, offering a higher reward to EV owners, they can use more electric vehicles to avoid the imbalance penalty than when they offer a lower reward. However, the too high reward is a heavy burden for these companies. Using reinforcement learning, the aggregator can learn the strategy of controlling electric vehicles without defining the households’ precise information. As a result, we could achieve a 30% cost reduction compared to when no electric vehicles are used for reducing imbalance penalty. 

Index Terms—reinforcement learning, EV charging, imbalances, power systems

Cite: Ryuhei Furuta and Ryuji Matsuhashi, "Study on Aggregator’s Strategy of Controlling Electric Vehicles to Compensate Imbalances in Power Systems Using Reinforcement Learning," International Journal of Electrical Energy, Vol. 7, No. 2, pp. 67-71, December 2019. doi: 10.18178/ijoee.7.2.67-71

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