Two-Stage Robust Optimization Method for Joint System Considering Uncertainty of Electric Vehicles

To conduct the green and low-carbon development, renewable energy needs to play more vigorous role in power generation. Through the cooperative regulation and participation in the spot market, renewable energy, energy storage and electric vehicles (EVs) jointly participate in the regulation so as to improve energy efficiency and increase the penetration of renewable energy. In this paper, a two-stage robust optimization model is established considering the uncertainty of EVs' number. The first and second stages determine the bidding plans of such joint system in the day-ahead and real-time markets respectively, so as to minimize the cost. The associated optimization can provide the robust and economical results. Importantly, the modeling in perspective of uncertainty is applied to EVs participating in spot markets in an aggregated manner. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is proposed, which is solved by combination of genetic algorithm and solver. The test results verify the effectiveness of the model and method proposed in this paper.