Research on Long-Term Scheduling Optimization of Water–Wind–Solar Multi-Energy Complementary System Based on DDPG

Zixing Wan (), Wenwu Li, Mu He, Taotao Zhang, Shengzhe Chen, Weiwei Guan, Xiaojun Hua and Shang Zheng
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Zixing Wan: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430000, China
Wenwu Li: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430000, China
Mu He: School of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Taotao Zhang: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430000, China
Shengzhe Chen: Science and Technology Research Institute, China Three Gorges Corporation, Beijing 101117, China
Weiwei Guan: China Yangtze Power Co., Ltd., Yichang 443000, China
Xiaojun Hua: China Yangtze Power Co., Ltd., Yichang 443000, China
Shang Zheng: Three Gorges Renewables Offshore Wind Power Operation and Maintenance Jiangsu Co., Ltd., Yancheng 224000, China

Energies, 2025, vol. 18, issue 15, 1-21

Abstract: To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). First, an improved C-Vine Copula model is used to construct the multi-dimensional joint probability distribution of water, wind, and solar energy, and Latin Hypercube Sampling (LHS) is employed to generate a large number of water–wind–solar coupling scenarios, effectively reducing the model’s complexity. Then, a long-term optimization scheduling model is established with the goal of maximizing the absorption of clean energy, and it is converted into a Markov Decision Process (MDP). Next, the DDPG algorithm is employed with a noise dynamic adjustment mechanism to optimize the policy in continuous action spaces, yielding the optimal long-term scheduling strategy for the water–wind–solar multi-energy complementary system. Finally, using a water–wind–solar integrated energy base as a case study, comparative analysis demonstrates that the proposed method can improve the renewable energy absorption capacity and the system’s power generation efficiency by accurately quantifying the uncertainties of water, wind, and solar energy and precisely controlling the continuous action space during the scheduling process.

Keywords: multi-energy complementarity; clean energy absorption; optimal scheduling; deep reinforcement learning; uncertainty (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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