Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

We propose herein an extended power forecasting-based coordination dispatch method for PV power generation microgrid with plug-in EVs (PVEVM) to improve the local consumption of renewable energy in the microgrid by guiding electric vehicle (EV) orderly charging. In this method, we use a clustering algorithm and neural network to build a power forecasting model (PFM) based on real data which can effectively characterise the uncertainty of PV power generation and EV charging load. Based on the interaction between the energy control centre (ECC) of the PVEVM and the EV users, a one-leader multiple-follower Stackelberg game is formulated, and the Stackelberg equilibrium is determined by using a power forecasting-based genetic algorithm (GA). As a main contribution of this paper, the PV power generation and EV charging load output from the PFM are used to generate a better quality initial population of the GA to improve its performance. A case study using real data from the Aifeisheng PV power station in China and EV charging stations in the UK verifies the good performance of the proposed extended coordination dispatch algorithm.