Electric Vehicle Charging Decisions Using Only Market Trends with Persistence

Abstract

Electric vehicle (EV) charging can take advantage of real-time electricity market price volatility. Presuming that an EV must be fully charged at a future target time, the EV should choose to charge using the lowest future electricity prices and thereby minimize electricity cost. Statistical methods must be used if forward prices are unavailable. In this case, historical prices and trends must be mined to anticipate which prices should be used to charge the EV. Price persistence, a tendency for electricity prices to inexplicably become and remain relatively high or low for extended durations, is particularly difficult to forecast and mitigate. This paper formulates and tests a pragmatic strategy for integrating conventional static statistical prices and the Bayesian propagation of price persistence from the current price to prices in the current and future hours. Simulations were conducted to test the cost effectiveness of charging strategy using real-time electricity prices.