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In order to specifically make the evaluation of the complementarity of multiple power sources, we classify them into renewable power sources and controllable power sources. Six complementarity evaluation indexes are proposed based on the intrinsic complementarity of renewable power sources and the regulation capability of controllable power sources. To improve the traditional Analytic Hierarchy Process (AHP), we combine the expert group decision-making and Monte Carlo’s ideas. To improve the traditional Technique for order Preference by Similarity to an Ideal Solution (TOPSIS), we combine the idea of Euclidean distance and grey correlation from two perspectives of position and shape. Therefore, an evaluation method for the complementarity of multiple power sources is established from these two aspects. Finally, a concrete case is used to verify the validity and rationality of the established evaluation index system and method.

With the fast spreading of electric vehicles (EVs), the demand for EV fast charging station (FCS) is increasing fast. In this paper, a day-ahead scheduling strategy for a microgrid-like FCS with an in-station photovoltaic system (PVS) and a battery energy storage system (BESS) is proposed. The strategy is modeled as a scenario-based stochastic optimization problem whose objective is to minimize the expected total operation cost in the next day. Considering the power exchange between the FCS and the grid, the operation cost contains the cost of buying power in the day-ahead market, the expected adjustment cost and penalty in the real-time market and the life loss cost of the BESS. A piecewise linear life loss calculation model of the BESS is adopted. The uncertainty of the PVS output and the fast charging demand are considered in the model through randomly generated scenarios. In each scenario the PVS output and fast charging demand are generated using a Monte Carlo simulation method. This strategy considers the life loss cost of the BESS effectively and provide reasonable day-ahead schedules. In the case study, a simulation of the proposed day-ahead scheduling method is demonstrated.