Khalil, Muhammad Ilyas Khan, Rahman, Izaz Ur, Zakarya, Muhammad, Zia, Ashraf, Khan, Ayaz Ali, Chalak Qazani, Mohammad Reza, Al-Bahri, Mahmood, and Haleem, Muhammad (2024) A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems. Scientific Reports, 14. 13418.

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Abstract

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood’s topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce 6.4% of fuel costs and 9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce 9.4% of the emissions measured in tons per hour as compared to the PSO approach.

Item ID:	86709
Item Type:	Article (Research - C1)
ISSN:	2045-2322
Keywords:	Particle swarm optimisation, Markov chain, Evolutionary factor, Large-scale optimisation, Scalability
Copyright Information:	© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Date Deposited:	10 Sep 2025 04:00
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 70% 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400911 Power electronics @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 40% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 60%
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