Multi-scale event-based optimization for matching uncertain wind supply with EV charging demand

Due to the global environmental pollution and fossil fuel shortage, there is an increasing demand for renewable energy. In this circumstance, the wind power and the electric vehicle (EV) are an important part of the supply side and the demand side, respectively. Because of the multi-scale system dynamics, to match the random wind supply and EV charging demand to reduce the charging cost is challenging and of great practical interest. This is considered as an important problem in this paper. In order to capture the structure of this problem and to use the area information of EVs, we formulate this charging problem as a multi-scale event-based optimization (EBO) model. At the upper level, we define a series of macro events to determine the number of EVs to be charged for each aggregator. At the lower level, we finally decide every EV's charging plan based on a series of micro events and the upper level action. So as to solve this large-scale problem, we develop a multi-scale event-based policy iteration method in this paper. The numerical testing results show the effectiveness of this multi-scale EBO approach on reducing the total charging cost of all EVs.