Optimal Power Flow Using Improved Cross-Entropy Method

Hao Su, Qun Niu () and Zhile Yang
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Hao Su: Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Qun Niu: Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Zhile Yang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Energies, 2023, vol. 16, issue 14, 1-33

Abstract: An improved cross-entropy (CE) method assisted with a chaotic operator (CGSCE) is presented for solving the optimal power flow (OPF) problem. The introduction of the chaotic operator helps to enhance the exploration capability of the popular cross-entropy approach while the global best solution is preserved. To handle the constraints in the optimal power flow, an efficient constraint handling technique with no parameter adjustment is also introduced. The approach is tested on both the IEEE-30 bus system and the IEEE-57 bus system with different objective functions to verify its effectiveness in comparison with a few other methods reported in the literature. Simulation results confirm that the proposed method is capable of improving both the exploration ability and the convergence speed of the conventional cross-entropy method. It outperforms the original cross-entropy, its variant GSCE and other methods in most of the OPF study cases.

Keywords: optimal power flow; cross entropy; constraint handling; power system; optimization (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: 2023
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