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Electric vehicles (EVs), as flexible distributed energy storage units, play an important role in maintaining the balance between supply and demand in modern power systems. However, the stochasticity of their arrival times and the uncertainty of the demand for charging will affect the accuracy of any scheduling. Therefore, an Adaptive Robust Optimization model considering the uncertainty of EV charging demands is proposed. Firstly, an adaptive data-driven uncertainty set is constructed to address the issue of unknown probability distribution associated with the demand for charging, effectively capturing the multimodal characteristics of the uncertain data. Secondly, to eliminate extreme scenarios during real-time scheduling, a scenario reduction strategy based on the Jensen-Shannon divergence is proposed. The strategy effectively reduces the model's conservatism. Thirdly, to prevent grid overload resulting from the large-scale integration of EVs, a hybrid demand response strategy is proposed, which guides EV users to charge in an orderly manner. A new column-and-constraint generation algorithm with a parallel alternating optimization procedure (C&CG-PAOP) is employed to obtain the optimal solution. Experimental results demonstrate the effectiveness of our method in reducing the operating costs of charging stations.
Ren et al. (Mon,) studied this question.