(49f) Stochastic Statistical Models of Vehicle-to-Grid Economics for Predicting Impact of Policy and Renewables Portfolio

The continued growth of renewable sources of energy lead to grid-level intermittent supply and demand mismatch. There are a number of research and engineering solutions that address this problem including grid-level energy storage and demand response via a smart grid infrastructure. Vehicle-to-grid (V2G) is a promising approach because it uses the existing resource of electric vehicle batteries as the energy storage medium. Electric vehicles charge at night while power is cheap, commute to work, and discharge when demand is high yielding the electric vehicle owner a profit. This simple sounding process has a number of complicating economic issues and in this talk we discuss them. For example, charge/discharge efficiency, battery degradation, and location-based marginal pricing of electricity. Our preliminary conclusion is V2G is not beneficial to an electric vehicle owner with current technology and electricity pricing, even with public policy tools. In our model, a stochastic economic analysis of V2G which takes into account randomness in driving patterns and work patterns of EV users is presented. We use battery degradation models to study the impact of vehicle battery degradation on this process. This analysis is done for multiple cities of the United States and location based results are presented. We also apply an economic analysis at the utility level to see how V2G affects load and what issues can arise. For example, as solar makes up more of the electricity generation mix, the highest price of electricity may be at night when generation is minimal. V2G can improve the peak and demand mismatch in such cases.