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Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

Citation

Das, CK and Bass, O and Kothapalli, G and Mahmoud, TS and Habibi, D, Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm, Applied Energy, 232 pp. 212-228. ISSN 0306-2619 (2018) [Refereed Article]

Copyright Statement

2018 Elsevier Ltd. All rights reserved.

DOI: doi:10.1016/j.apenergy.2018.07.100

Abstract

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance.

Item Details

Item Type:Refereed Article
Keywords:Energy storage systems, Energy storage system allocation, Voltage profile improvement, Line loading reduction, Power loss minimization, Particle swarm optimization, Artificial bee colony optimization
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Power and Energy Systems Engineering (excl. Renewable Power)
Objective Division:Energy
Objective Group:Energy Storage, Distribution and Supply
Objective Field:Energy Storage (excl. Hydrogen)
UTAS Author:Mahmoud, TS (Dr Thair Mahmoud)
ID Code:131487
Year Published:2018
Web of Science® Times Cited:18
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2019-03-19
Last Modified:2019-04-11
Downloads:0

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