Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&KANO and MVO in PV-Connected Distribution Network

Qingyuan Yan (), Yang Gao, Ling Xing, Binrui Xu, Yanxue Li and Weili Chen
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Qingyuan Yan: College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
Yang Gao: College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
Ling Xing: College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
Binrui Xu: College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
Yanxue Li: State Grid Integrated Energy Planning and D&R Institute Co., Ltd., Beijing 100052, China
Weili Chen: State Grid Hengshui Electric Power Co., Ltd., Hengshui 053401, China

Energies, 2024, vol. 17, issue 14, 1-36

Abstract: The surge in disordered EV charging demand, driven by the rapid growth in the ownership of electric vehicles (EVs), has highlighted the potential for significant disruptions in photovoltaic (PV)-connected distribution networks (DNs). This escalating demand not only presents challenges in meeting charging requirements to satisfy EV owners and grid fast-charging stations (GFCSs) but also jeopardizes the stable operation of the distribution network. To address these challenges, this study introduces a novel model called SOR&KANO for charging decisions, which focuses on addressing the dual-sided demand of GFCSs and EVs. The proposed model utilizes the salp swarm algorithm-convolutional neural network (SSA-CNN) to predict the PV output and employs Monte Carlo simulation to estimate the charging load of EVs, ensuring accurate PV output prediction and efficient EV distribution. To optimize charging decisions for reserved EVs (REVs) and non-reserved EVs (NREVs), this study applies the multi-verse optimizer (MVO) in conjunction with time-of-use (TOU) tariff guidance. By integrating the SOR&KANO model with the MVO algorithm, this approach enhances satisfaction levels for GFCSs by balancing the charging demand, increasing utilization rates, and improving voltage quality within the DN. Simultaneously, for EVs, the optimized scheduling strategy reduces charging time and costs while addressing concerns related to range anxiety and driver fatigue. The efficacy of the proposed approach is validated through a simulation on a modified IEEE-33 system, confirming the effectiveness of the optimal scheduling methods proposed in this study.

Keywords: SOR&KANO; multi-verse optimizer; satisfaction optimization; photovoltaic prediction; grid fast-charging stations; electric vehicles; distribution network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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