A Data-Driven Method for Constructing Planning Evaluation Indicators for Emerging Distribution Networks

Yuan Zhang, Wei Xiong (), Jinsen Liu, Xufeng Yuan, Zhiyang Lu and Fei Zheng
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Yuan Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Wei Xiong: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Jinsen Liu: Power Grid Planning & Research Center of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Xufeng Yuan: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Zhiyang Lu: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Fei Zheng: Power Grid Planning & Research Center of Guizhou Power Grid Co., Ltd., Guiyang 550002, China

Energies, 2026, vol. 19, issue 10, 1-20

Abstract: Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning evaluation indicators for emerging distribution networks. First, based on an existing comprehensive indicator system, key factors of county-level distribution networks are identified to classify typical planning scenarios, and a preliminary scenario-oriented indicator system is established with expert knowledge. Second, data-driven techniques are employed for indicator selection. The maximum relevance and minimum redundancy (mRMR) method and the Random Forest (RF) algorithm are introduced to evaluate indicator relevance and importance, respectively, and a game-theoretic combination method with coefficient-of-variation (CV) correction is used for comprehensive screening. Finally, a county-level case study is conducted to validate the proposed method. The results show that the proposed method can adjust the planning evaluation indicator system according to changes in distribution network characteristics under different scenarios and performs well in the studied cases. This method provides a practical framework for constructing adaptive indicator systems for distribution network planning evaluation.

Keywords: distribution network planning evaluation; indicator system; data-driven method; maximum relevance and minimum redundancy; random forest; game theory (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: 2026
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