Distributed robust optimization model for resiliency analysis of energy communities with shared energy storage systems

The resilient operation of energy communities (ECs) ensures their ability to withstand disruptions, reduce energy supply interruptions, and contribute to overall community sustainability. Shared energy storage (SES), as a flexible resource, enhances the resiliency of ECs by storing excess energy during optimal periods, injecting power during high demand or emergencies, ensuring an uninterrupted supply, and mitigating grid dependencies. This paper presents a decentralized robust model for the resilient operation of ECs in the day-ahead and real-time market. In the proposed model, SES is considered an energy service provider for EC. The model is formulated as a min-max-min problem, where the outer and inner sub-problems represent the optimal operation of the community in both the day ahead and real-time, based on cost minimization. Additionally, the worst-case realizations of uncertain demand and PV are addressed through middle maximization. The optimal strategy of EC is determined to respond economically to the worst-case realizations of uncertain parameters in real-time. The decomposition technique is used to solve the proposed optimization problem. In the day-ahead stage or the upper sub-problem, the alternating direction method of multipliers (ADMM) approach is employed to develop the decentralized model. In the real-time stage or the lower sub-problem, a centralized model is used to verify the resilient operation of EC. The performance and effectiveness of the proposed model are assessed using a case study. Simulation results reveal that implementing the proposed model in various scenarios reduces the solving time by 25-32 %.