Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO

Tao Yi, Xiaobin Cheng and Peng Peng

Energy, 2022, vol. 239, issue PC

Abstract: This paper studies a two-stage model for the optimization of charging scheduling and charging station construction planning based on the spatial-temporal complementarity of charging demand, in order to improve the overall satisfaction of the whole society. In the first stage, starting from the probability of trip starting point and other characteristics, the simulation of load demand is realized based on Monte Carlo simulation, and the charging plan regulation method is created combined with the principle of Dijkstra algorithm. In the second stage, a solution path based on discrete binary particle swarm optimization is proposed. The experimental results of 14 scenarios show that no matter disorderly charging or orderly charging, the annual social comprehensive cost of the scenario with five charging stations is the lowest. Compared with disordered charging, the total construction capacity of charging station is reduced by 51.61%, and the annual social comprehensive cost is reduced by 29.35%. Therefore, this study provides a framework for the optimal configuration of charging stations, which is applicable when considering or not considering demand response, and provides a solution for the future green development planning of urban charging infrastructure.

Keywords: Spatiotemporal complementarity; Configuration optimization; Monte Carlo simulation; Discrete binary particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221025093

DOI: 10.1016/j.energy.2021.122261

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