An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks. (December 2019)

• Record Type: Journal Article
• Authors: Das, Choton K.; Bass, Octavian; Mahmoud, Thair S.; Kothapalli, Ganesh; Masoum, Mohammad A.S.; Mousavi, Navid
• Is Part Of: Journal of energy storage. Volume 26(2019)
• Publication Date: 2019-12
• Subjects: Energy storage system -- Voltage profile -- Optimal sizing -- Power loss -- Line loading -- Optimal placement -- DER -- Asset management -- Network planning -- Artificial bee colony -- DIgSILENT PowerFactory; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2019.100847
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Optimal allocation of distributed energy storage systems is investigated. A uniform and non-uniform energy storage system sizes approaches are employed. Voltage profile is improved; line loading and line losses are minimized. PQ injection approach is applied and %improvement over P injection is evaluated. Performance indices are calculated to assess the system performance. 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.