Stochastic scheduling of resilient interconnected energy hubs considering peer-to-peer energy trading and energy storages

Abstract

This paper presents a new stochastic optimization framework for resilient operation scheduling of interconnected hubs considering peer-to-peer (P2P) energy trading between hubs and energy storages during severe disturbances such as windstorms. The optimization problem in normal and resilient operating modes is formulated taking into account common uncertainties of renewable energy sources (RESs) and duration of disturbance. In normal operation mode, local resources and trading with the main network are scheduled to maximize the operator's profit, considering the probability of possible disturbance. In resilient operation mode, the power network connection to one of the hubs is disrupted due to disturbance. Therefore, local resources are rescheduled to minimize the load shedding. In this mode, the operator utilizes the P2P energy trading between hubs and also increases the incorporation of energy storages to improve resiliency. The proposed framework is implemented on a test interconnected energy hubs (EHs) system to confirm its effectiveness under normal and resilient operating modes. The results show that P2P energy trading between hubs reduces the load shedding by 64% compared to the case without energy trading. Moreover, the simultaneous use of P2P energy trading and increased use of energy storages reduces load shedding by 76%.