Real-time Feedback Based Online Aggregate EV Power Flexibility Characterization

Abstract

As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Meanwhile, thanks to the flexibility of EV charging, vehicle-to-grid (V2G) interaction has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity, large number, and private information. Hence, it is the aggregator that interacts with the grid on behalf of EVs by characterizing their aggregate flexibility. In this paper, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We prove that the charging time delays for EVs always meet the requirement even if they are not considered explicitly. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method.

Cost-Optimal Aggregated Electric Vehicle Flexibility for Demand Response Market Participation by Workplace Electric Vehicle Charging Aggregators

“…The aggregated EV flexibility region is the area between V1G opt,100%,imp and the η% service level load time series, V1G opt,η%,imp (Equation ( 8)). All operating points in the flexibility region are feasible [16] and cost optimal. As discussed in Section 3.2, due to forecast errors, the DA market settlement is USD 8.38 (=USD 54.53 − USD 46.15) less than the forecasted market revenue.…”

“…Ref. [16] proposed a time-decoupled aggregated EV flexibility region, which is the area between two energy trajectories: an upper trajectory, which is the sum of the individual EV charging trajectories that meet the maximum EV battery energy, and a lower trajectory, which is the sum of the individual EV charging trajectories that meet the minimum customer-requested energy. Individual EV trajectories differ both by the timing of EV charging and the ending state of the charge of the EV battery.…”

“…In this work, we follow [16] and develop a cost-optimal EV scheduling algorithm which consists of two parts: (1) Offline optimization, in which the EV charging station aggregator considers demand charge management and bids in the day-ahead (DA) demand response market based on the forecasted aggregated EV load and the recent history of EV loads. (2) Online feedback real-time (RT) optimization (note that "real-time" here does not refer to a real-time market, but rather an optimization run during the day to update the DA dispatch), in which the arrival EV plug-in duration and session energy are updated and fed back to the model predictive control scheme every 15 min.…”