Optimisation Study of Investment Decision-Making in Distribution Networks of New Power Systems—Based on a Three-Level Decision-Making Model

Wanru Zhao, Ziteng Liu, Rui Zhang, Mai Lu and Wenhui Zhao ()
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Wanru Zhao: State Grid Shanghai Electric Power Company, Shanghai 200000, China
Ziteng Liu: State Grid Shanghai Electric Power Company, Shanghai 200000, China
Rui Zhang: State Grid Shanghai Electric Power Company, Shanghai 200000, China
Mai Lu: Shanghai Lingli Information Technology Co., Ltd., Shanghai 200000, China
Wenhui Zhao: School of Economics and Management, Shanghai University of Electric Power, Shanghai 200000, China

Energies, 2025, vol. 18, issue 13, 1-31

Abstract: This paper addresses the scientific needs for investment decision-making in distribution networks against the backdrop of new power systems, constructing a three-tier decision-making system that includes investment scale decision-making, investment structure allocation, and investment project prioritization. Initially, it systematically analyzes the new requirements imposed by the new power systems on distribution networks and establishes an investment index system encompassing four dimensions: “capacity, self-healing, interaction, and efficiency”. Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. Furthermore, distribution network projects are categorized into ten classes, and an investment direction decision-making model is constructed to determine the investment scale for each attribute. Then, for the shortcomings of the traditional project comparison method, the investment project decision-making model is established with the attribute investment amount as the constraint and the maximisation of project benefits under the attribute as the goal. Finally, the effectiveness of the decision-making system is verified by taking the Lishui distribution network as a case study. The results show that the system keeps the investment scale prediction error within 5.9%, the city’s total investment deviation within 0.3%, and the projects are synergistically optimized to provide quantitative support for distribution network investment decision-making in the context of a new type of electric power system, and to achieve precise regulation.

Keywords: new power system; distribution network investment decision; three-level decision system; grey prediction (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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