Hybrid Modeling Based Co-Optimization of Crew Dispatch and Distribution System Restoration Considering Multiple Uncertainties

Abstract

Natural disasters could lead to large-scale power outages by causing severe damages to distribution networks (DNs). Developing highly efficient outage management schemes is imperative for expediting service restoration. To this end, a novel multistage co-optimization model is proposed to seamlessly integrate the repair crew dispatch with the distribution system restoration. Furthermore, multiple sources of uncertainties are incorporated. To properly handle these uncertainties, a novel hybrid modeling approach is developed. In particular, the first stage is cast as a deterministic optimization problem to determine the crew route option prior to the realization of uncertainties. The second stage is to reconfigure the DN for forming multimicrogrids considering the uncertainties in crew travel time and repair time, which is formulated as a stochastic program. In the third stage, a robust optimization framework is applied to acquire a scheduling plan of various DERs that is immune to the worst-case realizations of electricity demand and photovoltaic generation. Finally, an advanced solution method is devised to achieve the computational tractability based on constraint and column generation and progressive hedging approaches. Numerical tests on the modified IEEE 37-bus DN show that the computation performance of the proposed solution method well fits the real-time application and also demonstrate that the highest demand curtailment is reduced by 3.3% and the variance of the restoration outcome is lowered by 40.5% compared with a recently proposed benchmark.