A Two-Stage Stochastic Programming Model for Resilience Enhancement of Active Distribution Networks With Mobile Energy Storage Systems
H. Chen, Xiaofu Xiong, Jizhong Zhu, Jian Wang, Wei Wang, Yufei He
Abstract
Existing energy storage systems (ESSs) are mostly deployed at locations that generate the maximum economic benefits of active distribution networks (ADNs). However, mobile energy storage systems (MESSs) hold significant potential in improving the active response capability of ADNs following disruptions due to their flexibility, controllability, and rapid response. To fully tap into the potential of MESSs, a two-stage stochastic mixed-integer programming (SMIP) model is proposed in this paper. The first stage involves formulating the planning decisions for the locations and capacities of MESSs, while the second stage evaluates the operating costs of the ADN by considering various operating scenarios such as normal, severe, and extreme scenarios. To demonstrate the feasibility and practicality of the proposed method, we conducted a case study using various test systems. The computational results indicate that by adopting appropriate planning and scheduling strategies, MESSs can not only ensure economic operation under normal weather conditions but also enhance the resilience of the ADN against catastrophic weather events.