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Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values

This study addresses the critical yet often overlooked aspect of incorporating correlations among input stochastic variables in power system planning and scheduling optimization. While existing literature has extensively focused on uncertainty modelling, there remains a gap in fully assessing the co...

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Main Authors:	ALAhmad A.K., Verayiah R., Ramasamy A., Shareef H.
Other Authors:	58124002200
Format:	Article
Published:	Elsevier B.V. 2025
Subjects:	Decision making Electric network analysis Intelligent systems Monte Carlo methods Solar power generation Stochastic models Stochastic systems Uncertainty analysis Hybrid optimization Hybrid optimization technique Independent and correlated stochastic variable Monte carlo simulation Monte Carlo's simulation Operations Modeling Optimization operation Optimization techniques Power system optimization operation model Power systems optimizations Stochastic variable Uncertainty models Optimization
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spelling	my.uniten.dspace-365422025-03-03T15:42:59Z Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values ALAhmad A.K. Verayiah R. Ramasamy A. Shareef H. 58124002200 26431682500 16023154400 57189691198 Decision making Electric network analysis Intelligent systems Monte Carlo methods Solar power generation Stochastic models Stochastic systems Uncertainty analysis Hybrid optimization Hybrid optimization technique Independent and correlated stochastic variable Monte carlo simulation Monte Carlo's simulation Operations Modeling Optimization operation Optimization techniques Power system optimization operation model Power systems optimizations Stochastic variable Uncertainty models Optimization This study addresses the critical yet often overlooked aspect of incorporating correlations among input stochastic variables in power system planning and scheduling optimization. While existing literature has extensively focused on uncertainty modelling, there remains a gap in fully assessing the consequences of disregarding correlations on objective function values across different power network sizes. To bridge this gap, we utilize Monte Carlo simulation with Cholesky decomposition, alongside Quasi-Monte Carlo sampling and Latin Hypercube Sampling, to effectively model uncertainty and capture correlation coefficients among input variables, including wind, solar photovoltaic, and load power. The most efficient technique is then integrated into our optimization model, which is applied to small, medium, and large power network models. Our proposed optimization model addresses conflicting objectives using a hybrid NSGAII-MOPSO, aiming to simultaneously minimize total operational cost, power loss, and voltage deviation. By implementing this model on selected power networks and comparing outcomes between cases with independent and correlated variables, we rigorously assess discrepancies in objective function values. We visualize and analyze these errors across systems of varying sizes, shedding light on the impact of neglecting variable correlations. Notably, the maximum discrepancies are observed at $3.26/h, $40.66/h, and $2754.04/h for the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, respectively. Crucially, as the system size increases, so does the magnitude of these differences, underlining the escalating impact of neglecting variable correlations on optimization outcomes. We stress the importance of integrating such considerations into future planning and operational strategies to mitigate errors and enhance decision-making processes. ? 2024 Final 2025-03-03T07:42:59Z 2025-03-03T07:42:59Z 2024 Article 10.1016/j.rineng.2024.102351 2-s2.0-85195165722 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195165722&doi=10.1016%2fj.rineng.2024.102351&partnerID=40&md5=034fc43b38d9fdd94565bfd031afbae8 https://irepository.uniten.edu.my/handle/123456789/36542 22 102351 All Open Access; Gold Open Access Elsevier B.V. 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	Decision making Electric network analysis Intelligent systems Monte Carlo methods Solar power generation Stochastic models Stochastic systems Uncertainty analysis Hybrid optimization Hybrid optimization technique Independent and correlated stochastic variable Monte carlo simulation Monte Carlo's simulation Operations Modeling Optimization operation Optimization techniques Power system optimization operation model Power systems optimizations Stochastic variable Uncertainty models Optimization
spellingShingle	Decision making Electric network analysis Intelligent systems Monte Carlo methods Solar power generation Stochastic models Stochastic systems Uncertainty analysis Hybrid optimization Hybrid optimization technique Independent and correlated stochastic variable Monte carlo simulation Monte Carlo's simulation Operations Modeling Optimization operation Optimization techniques Power system optimization operation model Power systems optimizations Stochastic variable Uncertainty models Optimization ALAhmad A.K. Verayiah R. Ramasamy A. Shareef H. Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values
description	This study addresses the critical yet often overlooked aspect of incorporating correlations among input stochastic variables in power system planning and scheduling optimization. While existing literature has extensively focused on uncertainty modelling, there remains a gap in fully assessing the consequences of disregarding correlations on objective function values across different power network sizes. To bridge this gap, we utilize Monte Carlo simulation with Cholesky decomposition, alongside Quasi-Monte Carlo sampling and Latin Hypercube Sampling, to effectively model uncertainty and capture correlation coefficients among input variables, including wind, solar photovoltaic, and load power. The most efficient technique is then integrated into our optimization model, which is applied to small, medium, and large power network models. Our proposed optimization model addresses conflicting objectives using a hybrid NSGAII-MOPSO, aiming to simultaneously minimize total operational cost, power loss, and voltage deviation. By implementing this model on selected power networks and comparing outcomes between cases with independent and correlated variables, we rigorously assess discrepancies in objective function values. We visualize and analyze these errors across systems of varying sizes, shedding light on the impact of neglecting variable correlations. Notably, the maximum discrepancies are observed at $3.26/h, $40.66/h, and $2754.04/h for the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, respectively. Crucially, as the system size increases, so does the magnitude of these differences, underlining the escalating impact of neglecting variable correlations on optimization outcomes. We stress the importance of integrating such considerations into future planning and operational strategies to mitigate errors and enhance decision-making processes. ? 2024
author2	58124002200
author_facet	58124002200 ALAhmad A.K. Verayiah R. Ramasamy A. Shareef H.
format	Article
author	ALAhmad A.K. Verayiah R. Ramasamy A. Shareef H.
author_sort	ALAhmad A.K.
title	Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values
title_short	Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values
title_full	Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values
title_fullStr	Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values
title_full_unstemmed	Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values
title_sort	enhancing optimization accuracy in power systems: investigating correlation effects on objective function values
publisher	Elsevier B.V.
publishDate	2025
_version_	1825816276436516864
score	13.244413

Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values

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