Monte Carlo Simulation and a Clustering Technique for Solving the Probabilistic Optimal Power Flow Problem for Hybrid Renewable Energy Systems

Mohamed S. Hashish, Hany M. Hasanien (), Haoran Ji, Abdulaziz Alkuhayli, Mohammed Alharbi, Tlenshiyeva Akmaral, Rania A. Turky, Francisco Jurado and Ahmed O. Badr
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Mohamed S. Hashish: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Hany M. Hasanien: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Haoran Ji: The Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Abdulaziz Alkuhayli: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Mohammed Alharbi: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Tlenshiyeva Akmaral: Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain
Rania A. Turky: Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
Francisco Jurado: Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain
Ahmed O. Badr: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt

Sustainability, 2023, vol. 15, issue 1, 1-25

Abstract: This paper proposes a new, metaheuristic optimization technique, Artificial Gorilla Troops Optimization (GTO), for a hybrid power system with photovoltaic (PV) and wind energy (WE) sources, solving the probabilistic optimum power flow (POPF) issue. First, the selected algorithm is developed and evaluated such that it applies to solve the classical optimum power flow (OPF) approach with the total fuel cost as the objective function. Second, the proposed algorithm is used for solving the POPF, including the PV and WE sources, considering the uncertainty of these renewable energy sources (RESs). The performance of the suggested algorithm was confirmed using the standard test systems IEEE 30-bus and 118-bus. Different scenarios involving different sets of the PV and WE sources and fixed and variable loads were considered in this study. The comparison of the obtained results from the suggested algorithm with other algorithms mentioned in this literature has confirmed the efficiency and performance of the proposed algorithm for providing optimal solutions for a hybrid power system. Furthermore, the results showed that the penetration of the PV and WE sources in the system significantly reduces the total cost of the system.

Keywords: probabilistic optimal power flow; renewable energy sources; uncertainties; Monte Carlo Simulation; K-means clustering; Elbow method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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