Emergency reserve constrained optimal allocation of energy storage in a novel honeycomb‐like microgrid cluster with volatile renewable energy resources

With the increasing penetration of large‐scale distributed renewable energies, the electricity distribution network is growing more complex with challenges brought by the intermittency and uncertainty of renewable energies. Microgrid (MG), one of feasible solutions to integrating renewable energies,...

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Veröffentlicht in:IET Generation, Transmission & Distribution Transmission & Distribution, 2022-01, Vol.16 (2), p.305-318
Hauptverfasser: Zhu, Naixuan, Hu, Pengfei, Liu, Siqi, Jiang, Daozhuo, Liang, Yiqiao, Li, Zhiyi
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Sprache:eng
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Zusammenfassung:With the increasing penetration of large‐scale distributed renewable energies, the electricity distribution network is growing more complex with challenges brought by the intermittency and uncertainty of renewable energies. Microgrid (MG), one of feasible solutions to integrating renewable energies, has shortages because of limited capacity. Therefore, MG cluster (MGC) was proposed as an improved solution. To efficiently and friendly integrate renewable energies, a novel honeycomb‐like MG cluster (H‐MGC) is proposed in this paper. Considering the uncertainty of renewable energies, a robust optimisation method of the siting and sizing of energy storage system (ESS) constrained by emergency reserve is proposed. Combining the column‐and‐constraint generation algorithm and big‐M method, the proposed optimisation model is decomposed into mixed integer second‐order cone programming master problem and sub‐problems, which are alternately solved. Based on the optimal results of study case, the siting and sizing results of ESS are analysed, as well as the operating characteristics of H‐MGC. Compared to existing MGC system, the proposed H‐MGC has a more flexible operation mode. The related costs of ESS are reduced, and the operational life is relatively extended, under the condition that the emergency reserve of system is satisfied.
ISSN:1751-8687
1751-8695
DOI:10.1049/gtd2.12306