A Bi-Level Optimization and Scheduling Strategy for Charging Stations Considering Battery Degradation

Qiwei Yang, Yantai Huang, Qiangqiang Zhang and Jinjiang Zhang ()
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Qiwei Yang: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Yantai Huang: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Qiangqiang Zhang: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Jinjiang Zhang: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China

Energies, 2023, vol. 16, issue 13, 1-15

Abstract: This paper proposes a bi-level optimization scheduling strategy for integrated photovoltaic (PV) and energy storage systems (ESS) to meet electric vehicle (EV) charging demands while reducing charging costs. First, a battery degradation cost model is developed in order to convert the long-term costs into short-term costs for real-time operation. The upper layer of ESS and power grid operation strategies are obtained by minimizing costs associated with battery degradation and distribution grid costs. The lower layer considers the PV uncertainty and the error caused by the upper layer operation strategy, and obtains the lower layer operation strategy by adding a penalty function to minimize fluctuations in power. Second, the author proposes a global optimization algorithm that combines Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) in order to solve the above-mentioned models, effectively combining the global search feature of PSO with the local search capability of SQP. Finally, the bi-level optimization scheduling strategy is obtained by solving the model through the algorithm. Simulation results verify the practicality of the scheduling strategy and the effectiveness of the proposed algorithm.

Keywords: charging stations; battery degradation; PSO-SQP algorithm; optimized scheduling (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: 2023
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