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Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables

This paper formulates a mixed integer non-linear probabilistic optimization planning problem to determine the optimal location, power rating and capacity of compressed air energy storage system (CAES) for a hybrid power system that includes wind and photo-voltaic (PV) energy sources. The Quasi-Monte...

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Main Authors:	ALAhmad A.K., Verayiah R., Ramasamy A., Marsadek M., Shareef H.
Other Authors:	58124002200
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Compressed air Compressed air energy storage Electric energy storage Electric loads Electric power system planning Genetic algorithms Integer programming Intelligent systems Investments Multiobjective optimization Nonlinear programming Operating costs Particle swarm optimization (PSO) Power quality Pressure vessels Screening Stochastic systems Compressed air energy storage system Correlated stochastic variable Multi objective particle swarm optimization Multi-objective particle swarm optimization Non-dominated sorting genetic algorithm (NSAGII) Non-dominated sorting genetic algorithms Optimal planning Optimal planning problem Planning problem Probabilistic load flow Quasi-Monte Carlo simulation Stochastic variable Storage systems Monte Carlo methods
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spelling	my.uniten.dspace-367042025-03-03T15:44:03Z Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables ALAhmad A.K. Verayiah R. Ramasamy A. Marsadek M. Shareef H. 58124002200 26431682500 16023154400 26423183000 57189691198 Compressed air Compressed air energy storage Electric energy storage Electric loads Electric power system planning Genetic algorithms Integer programming Intelligent systems Investments Multiobjective optimization Nonlinear programming Operating costs Particle swarm optimization (PSO) Power quality Pressure vessels Screening Stochastic systems Compressed air energy storage system Correlated stochastic variable Multi objective particle swarm optimization Multi-objective particle swarm optimization Non-dominated sorting genetic algorithm (NSAGII) Non-dominated sorting genetic algorithms Optimal planning Optimal planning problem Planning problem Probabilistic load flow Quasi-Monte Carlo simulation Stochastic variable Storage systems Monte Carlo methods This paper formulates a mixed integer non-linear probabilistic optimization planning problem to determine the optimal location, power rating and capacity of compressed air energy storage system (CAES) for a hybrid power system that includes wind and photo-voltaic (PV) energy sources. The Quasi-Monte Carlo simulation (QMCS) method is adopted to generate multiple scenarios for a combination of wind, PV, load and electricity price uncertainties. Also, Cholesky decomposition is adopted to preserve the actual correlation coefficients among the generated input stochastic variables. Moreover, the QMCS method is combined with the probabilistic load flow (PLF) to track the actual output variables. Three constrained incompatible non-linear objective functions are to be minimized simultaneously including, the total expected planning and operation cost of all generation sources, total expected power losses and the total expected voltage deviation. This optimization problem is solved by the hybrid non-dominated sorting genetic algorithm (NSGAII) and the multi-objective particle swarm optimization (MOPSO). The IEEE 118-bus system is adopted as the large-scale testing system to assess the performance of the proposed methodology and the convergence capability of the hybrid algorithm in rejecting the disturbances in the system caused by the existence of 132 different input correlated stochastic variables. The simulation results show that utilizing bulk CAESs can decrease the dependency on the thermal generators by 15.0984 % and decrease the total investment and operation cost by 25.5026 % compared to the case without utilizing any ESS technology. Also, the hybrid NSGAII-MOPSO proved its capability to converge successfully and reject all the input disturbances which could affect its performance. Moreover, the results show that the voltage on each bus in all scenarios remains within the limits in the presence of large input disturbances. ? 2024 Elsevier Ltd Final 2025-03-03T07:44:02Z 2025-03-03T07:44:02Z 2024 Article 10.1016/j.est.2024.110615 2-s2.0-85183204487 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183204487&doi=10.1016%2fj.est.2024.110615&partnerID=40&md5=acadc122547d08ddeabcd0100ce31592 https://irepository.uniten.edu.my/handle/123456789/36704 82 110615 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	Compressed air Compressed air energy storage Electric energy storage Electric loads Electric power system planning Genetic algorithms Integer programming Intelligent systems Investments Multiobjective optimization Nonlinear programming Operating costs Particle swarm optimization (PSO) Power quality Pressure vessels Screening Stochastic systems Compressed air energy storage system Correlated stochastic variable Multi objective particle swarm optimization Multi-objective particle swarm optimization Non-dominated sorting genetic algorithm (NSAGII) Non-dominated sorting genetic algorithms Optimal planning Optimal planning problem Planning problem Probabilistic load flow Quasi-Monte Carlo simulation Stochastic variable Storage systems Monte Carlo methods
spellingShingle	Compressed air Compressed air energy storage Electric energy storage Electric loads Electric power system planning Genetic algorithms Integer programming Intelligent systems Investments Multiobjective optimization Nonlinear programming Operating costs Particle swarm optimization (PSO) Power quality Pressure vessels Screening Stochastic systems Compressed air energy storage system Correlated stochastic variable Multi objective particle swarm optimization Multi-objective particle swarm optimization Non-dominated sorting genetic algorithm (NSAGII) Non-dominated sorting genetic algorithms Optimal planning Optimal planning problem Planning problem Probabilistic load flow Quasi-Monte Carlo simulation Stochastic variable Storage systems Monte Carlo methods ALAhmad A.K. Verayiah R. Ramasamy A. Marsadek M. Shareef H. Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
description	This paper formulates a mixed integer non-linear probabilistic optimization planning problem to determine the optimal location, power rating and capacity of compressed air energy storage system (CAES) for a hybrid power system that includes wind and photo-voltaic (PV) energy sources. The Quasi-Monte Carlo simulation (QMCS) method is adopted to generate multiple scenarios for a combination of wind, PV, load and electricity price uncertainties. Also, Cholesky decomposition is adopted to preserve the actual correlation coefficients among the generated input stochastic variables. Moreover, the QMCS method is combined with the probabilistic load flow (PLF) to track the actual output variables. Three constrained incompatible non-linear objective functions are to be minimized simultaneously including, the total expected planning and operation cost of all generation sources, total expected power losses and the total expected voltage deviation. This optimization problem is solved by the hybrid non-dominated sorting genetic algorithm (NSGAII) and the multi-objective particle swarm optimization (MOPSO). The IEEE 118-bus system is adopted as the large-scale testing system to assess the performance of the proposed methodology and the convergence capability of the hybrid algorithm in rejecting the disturbances in the system caused by the existence of 132 different input correlated stochastic variables. The simulation results show that utilizing bulk CAESs can decrease the dependency on the thermal generators by 15.0984 % and decrease the total investment and operation cost by 25.5026 % compared to the case without utilizing any ESS technology. Also, the hybrid NSGAII-MOPSO proved its capability to converge successfully and reject all the input disturbances which could affect its performance. Moreover, the results show that the voltage on each bus in all scenarios remains within the limits in the presence of large input disturbances. ? 2024 Elsevier Ltd
author2	58124002200
author_facet	58124002200 ALAhmad A.K. Verayiah R. Ramasamy A. Marsadek M. Shareef H.
format	Article
author	ALAhmad A.K. Verayiah R. Ramasamy A. Marsadek M. Shareef H.
author_sort	ALAhmad A.K.
title	Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
title_short	Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
title_full	Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
title_fullStr	Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
title_full_unstemmed	Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
title_sort	optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816192156172288
score	13.244369

Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables

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