Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Bozhen Jiang, Qin Wang (), Shengyu Wu, Yidi Wang and Gang Lu
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Bozhen Jiang: Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Qin Wang: Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Shengyu Wu: State Grid Energy Research Institute, Beijing 102209, China
Yidi Wang: China Electric Power Research Institute, Beijing 100055, China
Gang Lu: State Grid Energy Research Institute, Beijing 102209, China

Energies, 2024, vol. 17, issue 6, 1-17

Abstract: Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

Keywords: optimal power flow; machine learning; artificial neural network; active set; reinforcement learning; optimization method (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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