A decomposed optimization approach for service restoration of renewable-integrated distribution networks considering uncertain traffic conditions

Transportable resources like transportable power sources (TPSs) and repair crews (RCs) hold significant potential in restoring distribution networks, particularly those integrated with renewables. However, due to dynamic traffic conditions and uncertainties of renewable energy sources (RESs), effectively coordinating these resources during restoration remains a challenge. To address this issue, this paper presents a two-stage stochastic service restoration for distribution networks (DNs). In the first stage, we plan the routes of transportable resources. Uncertain traffic conditions are fully addressed when formulating the arrival time of TPSs and RCs, which directly determine the temporal-spatial availability of TPSs and the reactivation of faulted lines. In response to the uncertainty realization, the second stage can effectively coordinate TPS and RCs to restore critical loads, integrate RESs, repair faulty lines, and optimize power scheduling. A decomposition algorithm called adaptive progressive hedging is proposed to efficiently decompose and solve the proposed model. Case studies on two systems with different scales validate the effectiveness of the model and algorithm.