Three-layer resilience-aware scheduling framework in a hydrogen-integrated distribution network considering fuel cell electric vehicles

This paper introduces a three-layer data-driven resilient strategy designed to optimize the coordinated scheduling of hydrogen-integrated distribution networks. The strategy incorporates a proactive scheduling framework for hydrogen-integrated distribution networks by identifying the regions most impacted by hurricanes, which are among the most frequent natural events. In the first layer, a bi-level feature selection model is developed to identify the region most affected by the hurricane. The model uses statistical techniques, such as principal component analysis, the Pearson correlation coefficient, neighborhood component analysis, and the Relief algorithm. Building upon identification of the impacted regions, the planning of the hydrogen-integrated distribution networks is conducted using a two-stage scenario-based stochastic programming with robust concepts. This framework optimizes the siting and sizing of fuel cell electric vehicle charging stations in the second layer of the proposed strategy. The third layer simultaneously minimizes risk and variance by scheduling various resilience resources. The problem is subject to linearized constraints involving regret, the AC optimal power flow equation, and the operational limits of RRs. In the proposed problem, the resilience of the hydrogen-integrated distribution networks for contingency-based scheduling is ensured by the utilization of the proposed proactive readiness index. To evaluate the effectiveness of the proposed scheduling framework, real public datasets are employed in two 33-bus and 69-bus test hydrogen-integrated distribution networks. The numerical results demonstrate the outstanding performance of the proposed optimization model.