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Volume 19
Issue 2
10.3390/en19020415
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Source–Grid–Load–Storage Coordinated Optimization and Evolutionarily Stable Strategies for High Renewable Energy

by
Yu Shi
1,
Yiwen Yao
1,
Yiran Li
2,
Jing Wang
1,
Rui Zhou
3,
Xiaomin Lu
1,
Xinhong Wang
1,
Dingheng Wang
1,
Xuefeng Gao
1,
Xin Xu
1,
Zilai Ou
2,
Leilei Jiang
2 and
Zhe Ma
2,*
1
Power Economic Research Institute of Jilin Electric Power Co., Ltd., Changchun 130021, China
2
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
3
State Grid Jilin Electric Power Co., Ltd., Changchun 130021, China
*
Author to whom correspondence should be addressed.
Energies 2026, 19(2), 415; https://doi.org/10.3390/en19020415
Submission received: 16 December 2025 / Revised: 9 January 2026 / Accepted: 12 January 2026 / Published: 14 January 2026
Versions Notes

Abstract

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics.
Keywords: source–grid–load–storage; multi-agent coordination; evolutionary game; demand response source–grid–load–storage; multi-agent coordination; evolutionary game; demand response

Share and Cite

MDPI and ACS Style

Shi, Y.; Yao, Y.; Li, Y.; Wang, J.; Zhou, R.; Lu, X.; Wang, X.; Wang, D.; Gao, X.; Xu, X.; et al. Research on Source–Grid–Load–Storage Coordinated Optimization and Evolutionarily Stable Strategies for High Renewable Energy. Energies 2026, 19, 415. https://doi.org/10.3390/en19020415

AMA Style

Shi Y, Yao Y, Li Y, Wang J, Zhou R, Lu X, Wang X, Wang D, Gao X, Xu X, et al. Research on Source–Grid–Load–Storage Coordinated Optimization and Evolutionarily Stable Strategies for High Renewable Energy. Energies. 2026; 19(2):415. https://doi.org/10.3390/en19020415

Chicago/Turabian Style

Shi, Yu, Yiwen Yao, Yiran Li, Jing Wang, Rui Zhou, Xiaomin Lu, Xinhong Wang, Dingheng Wang, Xuefeng Gao, Xin Xu, and et al. 2026. "Research on Source–Grid–Load–Storage Coordinated Optimization and Evolutionarily Stable Strategies for High Renewable Energy" Energies 19, no. 2: 415. https://doi.org/10.3390/en19020415

APA Style

Shi, Y., Yao, Y., Li, Y., Wang, J., Zhou, R., Lu, X., Wang, X., Wang, D., Gao, X., Xu, X., Ou, Z., Jiang, L., & Ma, Z. (2026). Research on Source–Grid–Load–Storage Coordinated Optimization and Evolutionarily Stable Strategies for High Renewable Energy. Energies, 19(2), 415. https://doi.org/10.3390/en19020415

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