A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors

In recent years, global warming impact are becoming increasingly severe due to the dramatic green house emission and severe environmental problem. The large integration of PEV and RGs directly affect the supply and demand balance of power grid, which bring challenges to the secure and economic operation of power system. This study proposes a novel parallel social learning particle swarm optimization method for solving the large scale power system scheduling problem with significant integration of RGs and PEVs. The novel algorithm combines the real value and binary decision variables obtained by social learning particle swarm optimization algorithm, aiming to solve large scale mixed integer unit commitment problem considering charging and discharging management of PEV with large RGs integration. To verify the effectiveness of the proposed algorithm, numerical examples are analyzed for multi scale unit numbers and various cases of RGs and PEVs. The results show that the proposed parallel social learning particle swarm optimization method has superior performance in solving UC problems considering new energy sectors. In addition, the case studies shows that the integration of new energy sources and flexible demand side management of plug-in electric vehicles have great potentials to alleviate power grid load and bring considerable economic benefits. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A novel parallel social learning particle swarm optimization method is proposed in this study to solve the power system scheduling problem with significant integration of renewable energy sources and plug-in electric vehicles. The results show that the method has superior performance in solving unit commitment problems considering the new energy sectors.