Optimal Dispatch of Electric Vehicles Performing Vehicle-to-Grid

Recently, there has been a great interest in re-thinking the way we produce and consume energy. This is due to concerns of global warming and energy security, among other reasons. Therefore, on the electric power generation side, the utilization of sustainable energy resources has been booming drastically worldwide. On the consumption side, a lot of efforts are geared toward making electric loads more energy-efficient and more responsive so that more optimal utilization of resources is achieved. For the same reasons, the transportation sector is experiencing a wave of electrification. Transportation electrification will definitely have a positive impact on the environment. However, it is seen as an extra burden on the existing, already stressed, energy grids. Therefore, vehicle-to-grid (V2G) services have been proposed as a way to 1- mitigate the negative impact of electric vehicles (EV) on the energy grid, and 2- increase the adoption rate of these vehicles. Unidirectional V2G is especially attractive because it requires little, if any, additional infrastructure other than the communication between the EV and an aggregator. The aggregator, in turn, combines the capacity of many EVs to bid into energy markets and then dispatches the individual EVs to meet the dispatch signal received from the grid operator. This project proposes a new optimal dispatch strategy for electric vehicles using V2G technologies. This strategy will be meant to be used by an aggregator. The optimization will aim to achieve the aggregators obligations in the ancillary service markets by following the system operators reference signals at the minimum dispatch cost. Since the main cost associated with dispatch is the communication bandwidth, the optimization seeks to minimize the number of signals sent to the EVs. This leads to discrete switching of the EVs on or off; charging at either its full capacity or not charging at all. The optimization must keep charging fair across the EV fleet to ensure that all cars are charged equally as well. This optimization must also be done in real-time, i.e. every few seconds. Real-time optimization will be used to accomplish this.