V2G for Frequency Regulation Service: a Stackelberg Game Approach Considering Endogenous Uncertainties

With the proliferation of electric vehicles (EVs), Vehicle-to-Grid (V2G) technology is promising in providing frequency regulation services for power grids. The electric vehicle aggregator (EVA) usually acts as a coordinator between EVs and the grid, which declares prices to EVs, and then EVs determine their charging/discharging power accordingly. However, the major challenge for V2G is how to manage frequency regulation while dealing with the uncertain charging demand of EVs. Especially in V2G systems with dynamic electricity prices, this uncertain charging demand is impacted by pricing decisions, i.e., endogenous uncertainty. To this end, this paper formulates a novel framework for V2G, where the interaction of EVA and EVs is modeled as a Stackelberg game with the consideration of the pricing impact on the probability distribution of the EV charging demand. Then, a decision-dependent distributionally robust chance constraint method is proposed to characterize the endogenous uncertainty. Finally, the Stackelberg game model is transformed into a mixed-integer second-order cone programming, which is tractable. Numerical experiments demonstrate the effectiveness of the model and methodology in guiding EVs to participate in ancillary services. Compared to the existing method considering only exogenous uncertainties, the proposed approach performs better in terms of the economic efficiency in the frequency regulation. In addition, compared with orderly charging, V2G can reduce the peak-to-average ratio for the grid.