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An accurate electrical load forecast is essential for the effective implementation of vehicle-to-grid (V2G) technology to achieve optimal electric vehicle (EV) charging decisions, consequently, ensuring the security and stability of power grid. While prevailing evaluation metrics prioritize forecast quality, they often overlook the significant influence a forecast exerts when integrated into the V2G scheduling optimization. In this paper, a reliable metric is proposed for forecasts in the context of V2G scheduling from the perspective of forecast value. Firstly, we conducted meticulously designed experiments to expose the limitations of forecast quality metrics in the context of V2G scheduling, as well as reveal three key findings. Subsequently, to address computational challenges and enhance representativeness of scheduling results, statistical features of EV charging are used to construct the aggregate model of EV fleet. Then, a reliable metric called V2G scheduling value error (V2G-SVE) is proposed to evaluate the degradation rate of scheduling performance as the score for forecasting performance. Finally, extensive case studies provide compelling evidence for the effectiveness and broad applicability of V2G-SVE. Beyond proposing an evaluation metric, this paper also aims to provide valuable insights about potential direction of improvement for future load forecasting technology.
Zhong et al. (Wed,) studied this question.