Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm

Mohammed Hamouda Ali (), Ali M. El-Rifaie (), Ahmed A. F. Youssef, Vladimir N. Tulsky and Mohamed A. Tolba ()
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Mohammed Hamouda Ali: Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt
Ali M. El-Rifaie: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Ahmed A. F. Youssef: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Vladimir N. Tulsky: Electrical Power Systems Department, National Research University “MPEI”, Moscow 111250, Russia
Mohamed A. Tolba: Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Cairo 11787, Egypt

Energies, 2023, vol. 16, issue 2, 1-29

Abstract: The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition, the results indicate that POA is capable of identifying the most appropriate worldwide solutions. It is also successfully investigating preferred search locations, ensuring a fast convergence speed and enhancing the search engine’s capabilities.

Keywords: optimal power flow; peafowl optimization algorithm; control variables; multi-objective function; load dispatch (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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