Book Description
Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs and environmentally friendly operation. However, the anticipated increase of EV usage and increased use of renewable energy sources and smart storage devices for EV charging presents opportunities as well as challenges. Time-varying electricity pricing and day-ahead power commitment adds another dimension to this problem. This thesis, describes development of coordinated EV charging strategies for renewable energy-powered charging stations at homes and parking lots. We develop an optimal control theory-based charging strategy that minimizes power drawn from the electricity grid while utilizing maximum energy from renewable energy sources. Specifically, we derive a centralized iterative control approach in which charging rates of EVs are optimized one at a time. We also propose an algorithm that maximizes profits for parking lot operators by advantageously utilizing time-varying electricity pricing while satisfying system constraints. We propose a linear programming-based strategy for EV charging, and we specifically derive a centralized linear program that minimizes charging costs for parking lot operators while satisfying customer demand in available time. Then we model EV charging behavior of Active Consumers. We develop a real-time pricing scheme that results in favorable load profile for electric utility by influencing EV charging behavior of Active Consumers. We develop this pricing scheme as a game between electric utility and Active Consumers, in which the electric utilities decide optimal electricity prices that minimize peak-to-average load ratio and Active Consumers decide optimal charging strategy that minimizes EV charging costs for Active Consumers.
