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A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources

The traditional optimal power flow problem (OPF) usually centers on thermal generators, which have limited fuel for power generation, while emissions from the network system are commonly overlooked. However, the rising appreciation of renewable energy sources, valued for their sustainability, abunda...

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Main Authors:	Ahmadipour M., Ali Z., Ramachandaramurthy V.K., Ridha H.M.
Other Authors:	57203964708
Format:	Review
Published:	Elsevier Ltd 2025
Subjects:	Acoustic generators Buses Constraint handling Electric load flow Heuristic algorithms Optimal systems Pareto principle Particle swarm optimization (PSO) Probability density function Renewable energy Sustainable development Weibull distribution Bus systems Jaya algorithm Multi-objective optimal power flow Optimal methods Optimal power flow problem Pareto optimal method Pareto-optimal Renewable energy source Thermal generators Multiobjective optimization
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id	my.uniten.dspace-36332
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spelling	my.uniten.dspace-363322025-03-03T15:41:58Z A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources Ahmadipour M. Ali Z. Ramachandaramurthy V.K. Ridha H.M. 57203964708 25824589000 6602912020 59513348300 Acoustic generators Buses Constraint handling Electric load flow Heuristic algorithms Optimal systems Pareto principle Particle swarm optimization (PSO) Probability density function Renewable energy Sustainable development Weibull distribution Bus systems Constraint handling Jaya algorithm Multi-objective optimal power flow Optimal methods Optimal power flow problem Pareto optimal method Pareto-optimal Renewable energy source Thermal generators Multiobjective optimization The traditional optimal power flow problem (OPF) usually centers on thermal generators, which have limited fuel for power generation, while emissions from the network system are commonly overlooked. However, the rising appreciation of renewable energy sources, valued for their sustainability, abundance, and environmental friendliness, has sparked increasing interest in the power systems domain. Consequently, there is a growing trend of integrating renewable energy sources into the electrical grid. This work explores the adaptation of the standard IEEE-30 bus system by incorporating renewable energy sources as a case study. This involves the replacement of traditional thermal generators located on buses 5 and 11 with wind generators, while bus 13 is substituted with solar generators. Addressing the uncertainty and intermittence inherent in renewable energy sources (RES), Weibull and lognormal probability density functions are employed for RES availability estimation. Integrating RES into the optimal power flow problem is framed as a multi-objective optimization task. A novel meta-heuristic optimization approach termed the Memory-Guided Jaya algorithm (MG-Jaya), is specifically tailored to address diverse challenges in multi-objective optimal power flow (MOOPF) incorporating RES. A smart memory-based strategy is incorporated into the algorithm to enhance solution optimality, convergence properties, and exploitation capabilities. Furthermore, a suitable mechanism aimed at efficiently finding Pareto-optimal solutions is proposed to address the multi-objective optimization optimal power flow (MOOPF) problem. Besides, to further evaluate the performance of the proposed approach in addressing complex, larger-scale issues, the study opts to utilize another test system of greater magnitude, namely the IEEE-57 bus system. To assess its effectiveness, the approach is compared against 14 recently introduced metaheuristics, which have garnered significant citations. To ensure fair comparisons, parameter configurations for all algorithms are automated using the parameter tuning tool iterated racing (irace). The experimental outcomes are analyzed using various nonparametric statistical techniques, including the Hyper-volume test, the Wilcoxon signed-rank test, and the critical difference plot. Furthermore, three authenticity criteria - Generational Distance (GD), Spacing Parameter (SP), and Diversity Metric (DM) - are employed to analyze the obtained Pareto-optimal solutions. Simulation results show that the proposed MG-Jaya algorithm demonstrates competitive capabilities. It effectively handles multi-objective and non-convex optimization problems. When compared to other approaches, it outperforms them in terms of solution optimality and feasibility. ? 2024 Elsevier B.V. Final 2025-03-03T07:41:58Z 2025-03-03T07:41:58Z 2024 Review 10.1016/j.asoc.2024.111924 2-s2.0-85198066018 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198066018&doi=10.1016%2fj.asoc.2024.111924&partnerID=40&md5=6868b9f259a087168463d507ee0e5c03 https://irepository.uniten.edu.my/handle/123456789/36332 164 111924 Elsevier Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Acoustic generators Buses Constraint handling Electric load flow Heuristic algorithms Optimal systems Pareto principle Particle swarm optimization (PSO) Probability density function Renewable energy Sustainable development Weibull distribution Bus systems Constraint handling Jaya algorithm Multi-objective optimal power flow Optimal methods Optimal power flow problem Pareto optimal method Pareto-optimal Renewable energy source Thermal generators Multiobjective optimization
spellingShingle	Acoustic generators Buses Constraint handling Electric load flow Heuristic algorithms Optimal systems Pareto principle Particle swarm optimization (PSO) Probability density function Renewable energy Sustainable development Weibull distribution Bus systems Constraint handling Jaya algorithm Multi-objective optimal power flow Optimal methods Optimal power flow problem Pareto optimal method Pareto-optimal Renewable energy source Thermal generators Multiobjective optimization Ahmadipour M. Ali Z. Ramachandaramurthy V.K. Ridha H.M. A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
description	The traditional optimal power flow problem (OPF) usually centers on thermal generators, which have limited fuel for power generation, while emissions from the network system are commonly overlooked. However, the rising appreciation of renewable energy sources, valued for their sustainability, abundance, and environmental friendliness, has sparked increasing interest in the power systems domain. Consequently, there is a growing trend of integrating renewable energy sources into the electrical grid. This work explores the adaptation of the standard IEEE-30 bus system by incorporating renewable energy sources as a case study. This involves the replacement of traditional thermal generators located on buses 5 and 11 with wind generators, while bus 13 is substituted with solar generators. Addressing the uncertainty and intermittence inherent in renewable energy sources (RES), Weibull and lognormal probability density functions are employed for RES availability estimation. Integrating RES into the optimal power flow problem is framed as a multi-objective optimization task. A novel meta-heuristic optimization approach termed the Memory-Guided Jaya algorithm (MG-Jaya), is specifically tailored to address diverse challenges in multi-objective optimal power flow (MOOPF) incorporating RES. A smart memory-based strategy is incorporated into the algorithm to enhance solution optimality, convergence properties, and exploitation capabilities. Furthermore, a suitable mechanism aimed at efficiently finding Pareto-optimal solutions is proposed to address the multi-objective optimization optimal power flow (MOOPF) problem. Besides, to further evaluate the performance of the proposed approach in addressing complex, larger-scale issues, the study opts to utilize another test system of greater magnitude, namely the IEEE-57 bus system. To assess its effectiveness, the approach is compared against 14 recently introduced metaheuristics, which have garnered significant citations. To ensure fair comparisons, parameter configurations for all algorithms are automated using the parameter tuning tool iterated racing (irace). The experimental outcomes are analyzed using various nonparametric statistical techniques, including the Hyper-volume test, the Wilcoxon signed-rank test, and the critical difference plot. Furthermore, three authenticity criteria - Generational Distance (GD), Spacing Parameter (SP), and Diversity Metric (DM) - are employed to analyze the obtained Pareto-optimal solutions. Simulation results show that the proposed MG-Jaya algorithm demonstrates competitive capabilities. It effectively handles multi-objective and non-convex optimization problems. When compared to other approaches, it outperforms them in terms of solution optimality and feasibility. ? 2024 Elsevier B.V.
author2	57203964708
author_facet	57203964708 Ahmadipour M. Ali Z. Ramachandaramurthy V.K. Ridha H.M.
format	Review
author	Ahmadipour M. Ali Z. Ramachandaramurthy V.K. Ridha H.M.
author_sort	Ahmadipour M.
title	A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
title_short	A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
title_full	A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
title_fullStr	A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
title_full_unstemmed	A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
title_sort	memory-guided jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816103052378112
score	13.244109

A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources

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