Aggregation and Scheduling Models for Electric Vehicles in Distribution Networks Considering Power Fluctuations and Load Rebound
Congying Wei, Jian Xu, Siyang Liao, Yuanzhang Sun
Abstract
With electrical performance similar to that of energy storage systems, electric vehicles (EVs) show great potential for demand response to facilitate higher penetration of distributed renewable energy. Therefore, this article presents a look-ahead scheduling model for EVs to offer a load curtailment service in a distribution network, with the aim of smoothing out the power fluctuations caused by distributed renewable energy sources. An equivalent aggregation method that is not sensitive to heterogeneous private parameters is used to extract the overall dynamic power regulation characteristics of the EV population, while a decomposition model ensures the completion of the regulation task. Besides, in the scheduling model, the variable baselines are dynamically refreshed to capture the load rebound effects caused by load curtailment, allowing more accurate regulation plans to be obtained. A two-stage iterative solution strategy is also adopted to obtain the approximate optimal solution. Simulations demonstrate both the accuracy of the aggregation model and the effectiveness of the scheduling model.