Smart Carbon Emission Scheduling for Electric Vehicles via Reinforcement Learning under Carbon Peak Target

Yongsheng Cao () and Yongquan Wang
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Yongsheng Cao: Department of Intelligent Science and Information Law, East China University of Political Science and Law, Shanghai 200042, China
Yongquan Wang: Department of Intelligent Science and Information Law, East China University of Political Science and Law, Shanghai 200042, China

Sustainability, 2022, vol. 14, issue 19, 1-16

Abstract: Electric vehicles (EVs) have become popular in daily life, which influences carbon dioxide emissions and reshapes the curves of community loads. It is crucial to study efficient carbon emission scheduling algorithms to lessen the influence of EVs’ charging demand on carbon dioxide emissions and reduce the carbon emission cost for EVs coming to the community. We study an electric vehicle (EV) carbon emission scheduling problem to shave the peak community load and reduce the carbon emission cost when we do not know future EV data. First, we investigate an offline carbon emission scheduling problem to minimize the carbon emission cost of the community by predicting future data with regard to incoming EVs. Then, we study the online carbon emission problem and propose an online carbon emission algorithm based on a heuristic rolling algorithm. Furthermore, we propose a reinforcement learning smart carbon emission algorithm (RLSCA) to achieve the dispatching plan of the charging carbon emission of EVs. Last but not least, simulation results show that our proposed algorithm can reduce the carbon emission cost by 21.26 % , 16.60 % , and 8.72 % compared with three benchmark algorithms.

Keywords: electric vehicle; load scheduling; carbon emission; online learning; actor-critic method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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