Document Type

Journal Article

Publication Title

Journal of Energy Storage

Publisher

Elsevier

School

School of Engineering

Comments

This is an Author's Accepted Manuscript of: Das, C. K., Bass, O., Mahmoud, T. S., Kothapalli, G., Masoum, M. A.. S., & Mousavi, N. (2019). An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks. Journal of Energy Storage, 26, Article 100847. Original publication available here

© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Abstract

The allocation of grid-scale energy storage systems (ESSs) can play a significant role in solving distribution network issues and improving overall network performance. This paper presents a strategy for optimal allocation and sizing of distributed ESSs through P and Q injection by the ESSs to a distribution network. The investigation is carried out in a renewable-penetrated (wind and solar) medium voltage IEEE-33 bus distribution network for two different scenarios: (1) using a uniform ESS size and (2) using non-uniform ESS sizes. DIgSILENT PowerFactory is used for system modeling and testing, and simulation events are automated using Python scripting. A hybrid meta-heuristic optimization algorithm such as the fitness-scaled chaotic artificial bee colony algorithm is applied to optimize parameters of the objective function. The artificial bee colony algorithm is also applied to justify the results attained from the fitness-scaled chaotic artificial bee colony algorithm. A performance comparison, in relation to proposed PQ injection approach with previously applied P injection technique, is presented. The obtained results suggest that the proposed PQ injection-based ESS placement strategy performs better than the P injection-based approach, which can significantly improve distribution network performance by minimizing voltage deviation, power losses, and line loading.

DOI

10.1016/j.est.2019.100847

Available for download on Wednesday, December 01, 2021

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