A Three-Layer Coordinated Planning Model for Source–Grid–Load–Storage Considering Electricity–Carbon Coupling and Flexibility Supply–Demand Balance

Zequn Wang, Haobin Chen, Haoyang Tang, Lin Zheng, Jianfeng Zheng, Zhilu Liu and Zhijian Hu ()
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Zequn Wang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Haobin Chen: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Haoyang Tang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Lin Zheng: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Jianfeng Zheng: Foshan Power Supply Bureau of Guangdong Power Grid Corporation, Foshan 528000, China
Zhilu Liu: Foshan Power Supply Bureau of Guangdong Power Grid Corporation, Foshan 528000, China
Zhijian Hu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Sustainability, 2025, vol. 17, issue 16, 1-28

Abstract: With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems.

Keywords: electricity–carbon coupling; system flexibility; Source–Grid–Load–Storage; three-layer planning; improved adaptive particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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