Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach

Mingtao Ma, Huijun Huang, Xiaoling Song, Feniosky Peña-Mora, Zhe Zhang and Jie Chen

Applied Energy, 2022, vol. 307, issue C, No S0306261921014410

Abstract: Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments.

Keywords: Shared energy storage; Bi-level optimization model; Sizing and operations; Renewable energy; Interactive genetic algorithm; Energy storage sharing (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921014410
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:307:y:2022:i:c:s0306261921014410

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.118170

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().