A study on prediction of urban electric vehicle ownership and evaluation of demand response capacity

The advancement of the electric vehicle (EV) industry has significantly contributed to achieving global low-carbon goals and has enabled EVs to emerge as new participants in power grid dispatching. This study proposes a comprehensive methodology for forecasting urban EV ownership and evaluating the corresponding demand response capacity. First, a competitive development model for urban EV adoption is constructed based on the Bass-Lotka framework. This model takes into account several key factors, including vehicle scrappage functions, competition with internal combustion engine vehicles, policy incentives, urban traffic saturation, and market saturation effects. The forecasting process distinguishes between different stages of urban EV development to reflect realistic growth dynamics. Second, the study simulates EV charging behaviors using a trip chain analysis framework, which captures the temporal and spatial characteristics of EV mobility patterns. Bayesian inference is employed to estimate the parameters. Building on these results, a demand response capacity evaluation model is developed, quantifying the potential of EVs to provide grid services. The model differentiates between upward and downward demand response capabilities, corresponding to load reduction and load increase potentials, respectively, at various times of the day. Finally, the proposed models and algorithms are empirically validated through a case study of Shanghai.