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

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 pa...

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Veröffentlicht in:Applied energy Jg. 307; S. 118170
Hauptverfasser: Ma, Mingtao, Huang, Huijun, Song, Xiaoling, Peña-Mora, Feniosky, Zhang, Zhe, Chen, Jie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.02.2022
Schlagworte:
algorithms
Bi-level optimization model
coal
energy
Energy storage sharing
hybrids
Interactive genetic algorithm
power generation
power plants
Renewable energy
Shared energy storage
Sizing and operations
solar energy
stakeholders
wind
Shared energy storage
Sizing and operations
Renewable energy
Energy storage sharing
Interactive genetic algorithm
Bi-level optimization model
ISSN:0306-2619, 1872-9118
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Zusammenfassung: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. •Shared energy storage is planned and modelled in distribution networks.•A bi-level model is established for shared energy storage sizing and operation.•An interactive bi-level nested genetic algorithm is designed.•A comparative analysis is conducted to validate the shared energy storage feasibility. [Display omitted]
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2021.118170