Restoration of a Multi-Energy Distribution System With Joint District Network Reconfiguration via Distributed Stochastic Programming

Extreme events like floods, tsunamis, hurricanes, etc., can threaten the normal operation of district power and thermal networks in multi-energy distribution systems (MDSs). Exploiting the reconfigurable properties of the combined network can provide a cost-effective solution to enhance the MDS resilience level and operation flexibility by redistributing multi-energy loads. In this paper, a resilient restoration approach is proposed for a MDS to coordinate multi-stage dispatch decisions in a distributed manner. First, the MDS operation is modelled with the power and thermal network constraints. As the district thermal network model is nonlinear, massive auxiliary flow variables are introduced to make the reconfiguration problem tractable, which significantly reduces the computational burdens. Second, to handle diverse uncertainties from the outcomes of disasters, loads, and renewable generation, both the pre-restoration and real-time stage measures are coordinated via a two-stage stochastic programming method. To further save the solution time, a distributed algorithm named progress hedging is used to decompose the original large problem into several small sub-ones. At last, case studies are conducted to demonstrate the effectiveness of the proposed method compared to other traditional restoration benchmarks.