With the increasing penetration of uncontrollable distributed generators such as distributed wind generator and photovoltaic generation

the uncertainty of distributed generator has become a key factor in distribution network reconfiguration. Hence

a stochastic network reconfiguration model was used to minimize the mathematical expectation of overall power loss

considering the constraints of power flow

voltage and topology. The stochastic power flow based on Latin Hypercube sampling-Monte Carlo simulation (LHS-MCS) was used to examine the chance constraints of nodal voltage and branch power flow. To improve computational efficiency

parallel undirected spanning tree-based genetic algorithm (PSTGA) was proposed to solve the network reconfiguration in parallel. The results of test in IEEE 33-bus distribution system have confirmed the rationality of model. The efficiency of PSTGA has been proved by comparing with undirected spanning tree-based genetic algorithm

particle swarm optimization

ant colony searching optimization and improved harmony search algorithm. This work is supported by National Key Technologies R&D Program (No. 2013BAA01B04) and State Grid Corporation of China (No. 520940120036).