Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as Whale Optimization Algorithm (WOA) is introduced to perform the optimization. In this paper,...

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Main Authors: Wong, L.A., Ramachandaramurthy, V.K., Walker, S.L., Taylor, P., Sanjari, M.J.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-127652020-07-07T06:34:34Z Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm Wong, L.A. Ramachandaramurthy, V.K. Walker, S.L. Taylor, P. Sanjari, M.J. This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as Whale Optimization Algorithm (WOA) is introduced to perform the optimization. In this paper, two different approaches are presented to achieve the optimal allocation of the BESS. The first approach is to obtain the optimal location and sizing in two steps while the second approach optimizes both location and sizing simultaneously. The performance of the proposed technique has been validated by comparing with two other algorithms namely firefly algorithm and particle swarm optimization. The results show that WOA has outstanding performance in attaining the optimal location and sizing of BESS in the distribution network for power losses reduction. © 2019 Elsevier Ltd 2020-02-03T03:26:36Z 2020-02-03T03:26:36Z 2019 Article 10.1016/j.est.2019.100892 en
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/
language English
description This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as Whale Optimization Algorithm (WOA) is introduced to perform the optimization. In this paper, two different approaches are presented to achieve the optimal allocation of the BESS. The first approach is to obtain the optimal location and sizing in two steps while the second approach optimizes both location and sizing simultaneously. The performance of the proposed technique has been validated by comparing with two other algorithms namely firefly algorithm and particle swarm optimization. The results show that WOA has outstanding performance in attaining the optimal location and sizing of BESS in the distribution network for power losses reduction. © 2019 Elsevier Ltd
format Article
author Wong, L.A.
Ramachandaramurthy, V.K.
Walker, S.L.
Taylor, P.
Sanjari, M.J.
spellingShingle Wong, L.A.
Ramachandaramurthy, V.K.
Walker, S.L.
Taylor, P.
Sanjari, M.J.
Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
author_facet Wong, L.A.
Ramachandaramurthy, V.K.
Walker, S.L.
Taylor, P.
Sanjari, M.J.
author_sort Wong, L.A.
title Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
title_short Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
title_full Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
title_fullStr Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
title_full_unstemmed Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
title_sort optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm
publishDate 2020
_version_ 1672614172948430848
score 13.222552