Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm

Mohamed A. M. Shaheen, Hany M. Hasanien (), Said F. Mekhamer, Mohammed H. Qais, Saad Alghuwainem, Zia Ullah, Marcos Tostado-Véliz, Rania A. Turky, Francisco Jurado and Mohamed R. Elkadeem
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Mohamed A. M. Shaheen: Electrical Engineering Department, Future University in Egypt, Cairo 11835, Egypt
Hany M. Hasanien: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Said F. Mekhamer: Electrical Engineering Department, Future University in Egypt, Cairo 11835, Egypt
Mohammed H. Qais: Centre for Advances in Reliability and Safety, Hong Kong, China
Saad Alghuwainem: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Zia Ullah: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Marcos Tostado-Véliz: Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain
Rania A. Turky: Electrical Engineering Department, Future University in Egypt, Cairo 11835, Egypt
Francisco Jurado: Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain
Mohamed R. Elkadeem: Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31511, Egypt

Mathematics, 2022, vol. 10, issue 17, 1-23

Abstract: This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.

Keywords: machine learning; probabilistic optimal power flow; renewable energy sources (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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