Explainable Warm-Start Point Learning for AC Optimal Power Flow Using a Novel Hybrid Stacked Ensemble Method

Kaijie Xu, Xiaochen Zhang and Lin Qiu ()
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Kaijie Xu: Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
Xiaochen Zhang: Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
Lin Qiu: Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China

Sustainability, 2025, vol. 17, issue 2, 1-18

Abstract: With the development of renewable energy, renewable power generation has become an increasingly important component of the power system. However, it also introduces uncertainty into the analysis of the power system. Therefore, to accelerate the solution of the OPF problem, this paper proposes a novel Hybrid Stacked Ensemble Method (HSEM), which incorporates explainable warm-start point learning for AC optimal power flow. The HSEM integrates conventional machine learning techniques, including regression trees and random forests, with gradient boosting trees. This combination leverages the individual strengths of each algorithm, thereby enhancing the overall generalization capabilities of the model in addressing AC-OPF problems and improving its interpretability. Experimental results indicate that the HSEM model achieves superior accuracy in AC-OPF solutions compared to traditional Deep Neural Network (DNN) approaches. Furthermore, the HSEM demonstrates significant improvements in both the feasibility and constraint satisfaction of control variables. The effectiveness of the proposed HSEM is validated through rigorous testing on the IEEE-30 bus system and the IEEE-118 bus system, demonstrating its ability to provide an explainable warm-start point for solving AC-OPF problems.

Keywords: AC-OPF; warm-start; machine learning (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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