Lawrence Berkeley National Laboratory

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. Various objectives have been investigated before, such as following a given load profile, reducing load fluctuation and minimizing the total operational cost. However, the uncertainties inside the behaviors of individual drivers have tremendous impact on the cost effectiveness of microgrid operations, which have not been carefully studied. In this paper, we propose a predictive management strategy in a community microgrid, using datasets of system base load, solar generation and EV charging behaviors from real-world cases. Two stage operations are modeled for cost-effective EV management, i.e. wholesale market partition in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.