Hierarchical Optimization Strategy for Integrated Water–Wind–Solar System Considering Load Control of Electric Vehicle Charging Stations

Junyi Yu, Siyang Liao () and Jie Zhang
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Junyi Yu: The School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Siyang Liao: The School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Jie Zhang: Wenshan Electricity Supply Bureau, Yunnan Power Grid Corporation Ltd., Wenshan 663000, China

Energies, 2025, vol. 18, issue 10, 1-23

Abstract: For a high proportion of new energy with access to the grid, the typical random volatility of wind power and photovoltaic output greatly increases the peak load of the grid; in addition, the problem of wind and solar abandonment needs to be solved. This paper proposes the use of electric vehicle charging stations as new peak load resources to participate in grid dispatching. First, according to the actual operation and regulation characteristics of the load of EV charging stations, a refined regulation model enabling charging stations to participate in grid peak load regulation is established; then, combined with the deep peak load regulation model of hydropower units, in order to minimize system abandonment and minimize operating costs, a hierarchical optimization model for the joint peak load regulation of charging stations and hydropower deep regulation is established; finally, taking the actual power grid system as an example, a deep reinforcement learning algorithm is used to solve and analyze the problem, and the effectiveness of the scheme is verified. This study provides valuable insights into the coordinated optimization of electric vehicle charging stations and hydro–wind–solar systems for seamless integration into grid peak-shaving services.

Keywords: EV charging stations load regulation; new energy consumption; integrated segmentation costs; collaborative optimization model (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: 2025
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