Staff View: Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

In this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-yea...

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Main Authors:	ALAhmad A.K., Verayiah R., Shareef H.
Other Authors:	58124002200
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Battery storage Benchmarking Charging (batteries) Distributed power generation Electric loads Genetic algorithms Intelligent systems Investments Monte Carlo methods Multiobjective optimization Operating costs Particle swarm optimization (PSO) Screening Secondary batteries Stochastic systems Wind Battery energy storage systems Charging and discharging control strategy Charging and discharging controls Control strategies Distribution systems Green energy Hybrid optimization algorithm Long term planning Renewable energy source Uncertainty Renewable energy
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spelling	my.uniten.dspace-365082025-03-03T15:42:47Z Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration ALAhmad A.K. Verayiah R. Shareef H. 58124002200 26431682500 57189691198 Battery storage Benchmarking Charging (batteries) Distributed power generation Electric loads Genetic algorithms Intelligent systems Investments Monte Carlo methods Multiobjective optimization Operating costs Particle swarm optimization (PSO) Screening Secondary batteries Stochastic systems Wind Battery energy storage systems Charging and discharging control strategy Charging and discharging controls Control strategies Distribution systems Green energy Hybrid optimization algorithm Long term planning Renewable energy source Uncertainty Renewable energy In this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-years. The aim is to enhance the integrated capacity of green energy in the electric power distribution system (DS) while adhering to topological, technical, and economic constraints and considering the annual load growth. Moreover, to account for uncertainties related to various input random variables such as wind speed, solar irradiation, load power, and energy prices, Monte Carlo Simulation (MCS) is employed to generate multiple scenarios. The backward reduction method (BRM) is then applied to streamline the number of generated scenarios, reducing computational efforts. To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). This hybrid approach aims to simultaneously minimize three long term objective functions from the economic, environmental, and technical point of view: total expected investment, operational, and carbon emission cost, power loss, and voltage deviation. The effectiveness of the planning model and the performance of the solver method are validated using the 69-bus benchmark test system. The adopted system is configured into three cases, including basic DS, DS with RESs, and DS with a combination of RESs and BESSs. Simulation results demonstrate the capability of the proposed planning model in achieving the following improvements: RESs without ESS achieved 3.35 MVA penetration while reducing DS dependency by 31.44 %. Moreover, the technical objectives improved: power loss by 39.14 % and voltage deviation by 45.45 %. Post-BESS deployment, green energy capacity reached 3.65 MVA, enhancing technical objectives by 3.74 % and 9.00 %, with a marginal 0.82 % expense increase compared to the case with RESs alone. ? 2024 Elsevier Ltd Final 2025-03-03T07:42:47Z 2025-03-03T07:42:47Z 2024 Article 10.1016/j.est.2024.111868 2-s2.0-85192048658 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192048658&doi=10.1016%2fj.est.2024.111868&partnerID=40&md5=27c4e0375c38a3d82d018a9d870c77f6 https://irepository.uniten.edu.my/handle/123456789/36508 90 111868 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	Battery storage Benchmarking Charging (batteries) Distributed power generation Electric loads Genetic algorithms Intelligent systems Investments Monte Carlo methods Multiobjective optimization Operating costs Particle swarm optimization (PSO) Screening Secondary batteries Stochastic systems Wind Battery energy storage systems Charging and discharging control strategy Charging and discharging controls Control strategies Distribution systems Green energy Hybrid optimization algorithm Long term planning Renewable energy source Uncertainty Renewable energy
spellingShingle	Battery storage Benchmarking Charging (batteries) Distributed power generation Electric loads Genetic algorithms Intelligent systems Investments Monte Carlo methods Multiobjective optimization Operating costs Particle swarm optimization (PSO) Screening Secondary batteries Stochastic systems Wind Battery energy storage systems Charging and discharging control strategy Charging and discharging controls Control strategies Distribution systems Green energy Hybrid optimization algorithm Long term planning Renewable energy source Uncertainty Renewable energy ALAhmad A.K. Verayiah R. Shareef H. Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
description	In this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-years. The aim is to enhance the integrated capacity of green energy in the electric power distribution system (DS) while adhering to topological, technical, and economic constraints and considering the annual load growth. Moreover, to account for uncertainties related to various input random variables such as wind speed, solar irradiation, load power, and energy prices, Monte Carlo Simulation (MCS) is employed to generate multiple scenarios. The backward reduction method (BRM) is then applied to streamline the number of generated scenarios, reducing computational efforts. To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). This hybrid approach aims to simultaneously minimize three long term objective functions from the economic, environmental, and technical point of view: total expected investment, operational, and carbon emission cost, power loss, and voltage deviation. The effectiveness of the planning model and the performance of the solver method are validated using the 69-bus benchmark test system. The adopted system is configured into three cases, including basic DS, DS with RESs, and DS with a combination of RESs and BESSs. Simulation results demonstrate the capability of the proposed planning model in achieving the following improvements: RESs without ESS achieved 3.35 MVA penetration while reducing DS dependency by 31.44 %. Moreover, the technical objectives improved: power loss by 39.14 % and voltage deviation by 45.45 %. Post-BESS deployment, green energy capacity reached 3.65 MVA, enhancing technical objectives by 3.74 % and 9.00 %, with a marginal 0.82 % expense increase compared to the case with RESs alone. ? 2024 Elsevier Ltd
author2	58124002200
author_facet	58124002200 ALAhmad A.K. Verayiah R. Shareef H.
format	Article
author	ALAhmad A.K. Verayiah R. Shareef H.
author_sort	ALAhmad A.K.
title	Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
title_short	Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
title_full	Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
title_fullStr	Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
title_full_unstemmed	Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
title_sort	long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816234426368000
score	13.244109

Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

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