Resilience enhancement strategy for multi-energy systems considering multi-stage recovery process and multi-energy coordination. (15th February 2022) - Sun, Qirun; Wu, Zhi; Ma, Zhoujun; Gu, Wei; Zhang, Xiao-Ping; Lu, Yuping; Liu, Pengxiang
Abstract: With the increasing frequency of extreme weather events (EWEs) in recent years, the resilience of multi-energy systems (MESs) has received considerable attention. To improve the ability of MESs to withstand and recover rapidly from an EWE, this paper proposes a comprehensive resilience enhancement operation strategy for MESs considering the multi-stage recovery process and multi-energy coordination. Wherein, multi-stage recovery process throughout the pre-event stage, system disturbance stage, fast fault isolation stage, and remote-controlled service restoration stage are comprehensively considered and modeled in detail. Multiple resilience enhancement measures are adopted, and their coupling relationships at different stages are taken into account. In addition, an integrated energy flow model is developed to minimize the demand curtailment through coordination among different energy subsystems. The overall problem is established as a stochastic mixed-integer linear programming (MILP) model considering uncertainties of fault components. A customized progressive hedging algorithm (PHA) is further developed to reduce the computational burden caused by multiple scenarios. Case studies verify the effectiveness of the proposed model and solution technique. Simulation results reveal that the combination of multiple restoration measures is essential to reduce the impacts of EWEs, and the coordination among different energy carriers can effectively improve the resilience of the overall MESs. Highlights: A novel resilience enhancement model for multi-energy systems is proposed. Multi-stage recovery process is comprehensively modeled as well as their coupling relationships. Coordinated operation among different energy systems is considered. A modified progress hedging algorithm is developed to reduce the computational burden.