A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies

Abstract

The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, the uncertainty of RGs has also exerted significant challenges to the grid operation and control. Therefore, dynamic power system scheduling to accommodate the intermittent RGs and mass roll-out of PEVs has become extremely important. In this paper, a novel power system rescheduling strategy is proposed to tackle this problem. Considering the uncertainty of the wind energy, a set of indices according to different wind power application scenarios is proposed to initiate a rescheduling scheme for power generations. In addition, a social learning particle swarm optimization algorithm based on real-value and binary parallel is proposed to schedule the output of generator units and the charging and discharging of the PEV. The effectiveness of the proposed active rescheduling framework and solving algorithm has been verified by extensive experiments considering different number of generating units and scenarios, achieving up to over 5.3% cost reduction. The experimental results have also shown that through expropriate management of the charging and discharging of PEVs would be significantly alleviate the negative impact on the grid stability caused by the intermittent wind power generations.