Stochastic Sequential Restoration for Resilient Cyber-Physical Power Distribution Systems

Modern power systems are undergoing a paradigm shift from traditional grids towards smart grids. It fundamentally changes traditional power systems into complex cyber-physical systems. On the other hand, new challenges arise in terms of grid resilience, because natural disasters can cause damages to both cyber and physical systems. In this article, we propose a stochastic sequential restoration scheme for cyber-physical power distribution systems considering resilience. The sequential restoration problem is formulated as an uncertain Markov decision process (UMDP) with hurricanes incorporated as natural disasters. Different wind velocities and directions are considered as hurricane scenarios, which are used to obtain the fragility of distribution lines. The fragility functions are further used for the derivation of uncertain state transition functions of the UMDP. The minimax regret optimization considering the sample weights of UMDP is presented. The robust sequential actions are determined, such that the loads can be restored in a timely manner. To improve computational efficiency, a minimax regret policy iteration algorithm is presented based on the regret Bellman equation. Case studies are conducted based on the IEEE 123-Node Test Feeder and historical data of Hurricane Bonnie to demonstrate the effectiveness of the proposed scheme.