Energy storage systems: a multi-objective optimization with spatio-temporal correlation modeling and extreme scenario screening for distribution networks

Information from the abstract

This study proposes an optimization approach for energy storage systems (ESS) allocation, integrating spatio-temporal correlation modeling and two-layer extreme scenario screening for distribution networks. In ESS allocation, the volatility and uncertainty of renewable energy outputs pose significant challenges to distribution network operation. Traditional ESS allocation based on typical scenarios struggles to address voltage violations, renewable energy curtailment under extreme weather, and sudden load changes, especially with the growing penetration of wind and photovoltaic power. (1) A source-load joint scenario generation framework is established in which the spatio-temporal correlation characteristics of wind power, solar power, and load are modeled through the Spatio-Temporal Coupled Markov-Vine Synergistic Modeling Method (STC-MVSM) to generate high-fidelity input scenarios; (2) a two-layer screening mechanism based on Fuzzy Spectral Clustering (FSC) and the magnitude of net load change is developed to accurately extract extreme scenarios; and (3) A multi-objective ESS optimization framework is established for distribution networks under extreme scenarios. Simulation results on the IEEE 33-bus distribution system show that compared with traditional methods, the proposed method reduces total system cost by 5.3%, wind and solar curtailment penalties by 57.9%, load shedding(LS) penalties by 40.2%, and voltage deviation by 1.5%. These results verify the economic and operational advantages of the proposed method.

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Related topics: Optimal Power Flow Distribution · Microgrid Control and Optimization · Electric Power System Optimization