Capacity model and optimal scheduling strategy of multi-microgrid based on shared energy storage

• Published in: Energy (Oxford) 2024-10, Vol.306, p.132472, Article 132472
• Main Authors: Dai, Bin, Wang, Honglei, Li, Bin, Li, Chengjiang, Tan, Zhukui
• Format: Article
• Language: English
• Subjects: Bilateral uncertainty; Energy storage capacity optimization; Non-dominated sorting equilibrium optimizer; Renewable energy consumption rate; Shared energy storage model
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.132472

The widespread adoption of renewable energy (RE) requires proportional investment in energy storage to address the uncertainty of both the supply and demand sides of the power grid. However, this leads to challenges such as high investment costs and extended payback periods. This paper presents a multi-microgrid energy storage sharing (SES) model. The SES model determines the virtual energy storage capacity during power system operation, reducing the demand for energy storage capacity. A benefit distribution mechanism is developed to ensure fair income distribution among participants in proportion to their investments, facilitating direct benefit interaction. A bi-level optimization method is designed to simultaneously optimize the energy storage capacity and scheduling strategy, ensuring their alignment. A non-dominated sorting equilibrium optimizer algorithm is proposed to avoid the Pareto solution set falling into local optimal and ensure the effective implementation of the proposed benefit distribution mechanism. The results demonstrate that compared with distributed energy storage, the SES model reduces the required storage capacity of the system by 43.27 % and reduces the daily investment and operation and maintenance cost by 25.98 %. Moreover, while maintaining the same operational performance, the SES model requires less storage capacity and achieves 97.30 % self-consumption of RE. •A Shared energy storage model for multi-microgrid joint investment is proposed.•Set up a trading rule for shared energy storage.•Set the trading rules to guide energy interaction reasonably.•A bi-level optimization model is designed to solve the optimal capacity allocation.•A Non-dominated Equilibrium Optimization Algorithm is proposed.