Optimal Allocation of Community Distributed Energy and Storage Based on Regional Autonomous Balance and Sharing Mechanism

Jiangping Liu, Jing Wang, Xue Cui (), Peng Liu, Pingzheng Tong and Xuehan Dang
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Jiangping Liu: Hubei Power Exchange Center, Wuhan 430077, China
Jing Wang: Hubei Power Exchange Center, Wuhan 430077, China
Xue Cui: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Peng Liu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Pingzheng Tong: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Xuehan Dang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2024, vol. 18, issue 1, 1-19

Abstract: In the context of new power systems, the rapid development of distributed renewable energy and the drive of dual carbon targets have prompted community-level clean energy and energy storage configuration to become the key to improving energy efficiency and reducing carbon emissions. Based on the regional autonomy balance and sharing mechanism, this paper establishes a community distributed energy and energy storage optimization configuration model. With the goal of minimizing the total operating cost of the community, the established model is linearized by using the Big-M method and the McCormick method and transformed into a mixed integer linear programming model that is easy to solve. In order to comprehensively evaluate the comprehensive benefits of the established optimization scheme, this paper introduces the indicators of clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO 2 investment payback period from the aspects of energy utilization, the economy, and the environment. Finally, a calculation example analysis is conducted, and the results show that, compared with the scenario where energy storage is configured separately and distributed energy resources are not shared, the configuration strategy proposed in the article can reduce the energy storage configuration capacity by 46.6% and the distributed energy configuration capacity by 21.1%. Investment costs can be reduced by 15.6%. At the same time, 91.75% of distributed energy self-consumption and 96.80% of load self-supply are achieved, reducing grid interaction and promoting regional autonomy and balance. The static CO 2 investment payback period is also significantly shortened, and the carbon emission reduction effect is significant, providing an important reference for community energy system optimization planning and green and low-carbon development.

Keywords: distributed energy; community energy storage; optimal configuration; McCormick method; environmental benefits; regional autonomy balance; low carbon (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: 2024
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