Coordinated Electric Vehicle Charging Control with Aggregator Power Trading and Indirect Load Control
For power system operators and EV aggregators, this work provides a scalable distributed method for coordinated charging with energy trading, though it is an incremental improvement over existing optimization techniques.
This paper formulates a large-scale EV charging problem with energy trading to maximize aggregator profit, and develops a distributed optimization-based heuristic to solve the non-convex problem. Simulations on a modified IEEE 118 bus system with 10 aggregators and 30,000 EVs show that the proposed approach effectively increases total profit and achieves near-optimal performance.
Due to the increasing concern for greenhouse gas emissions and fossil fuel security, electric vehicles (EVs) have attracted much attention in recent years. EVs can aggregate together constituting the vehicle-to-grid system. Coordination of EVs is beneficial to the power system in many ways. In this paper, we formulate a novel large-scale EV charging problem with energy trading in order to maximize the aggregator profit. This problem is non-convex and can be solved with a centralized iterative approach. To overcome the computation complexity issue brought by the non-convexity, we develop a distributed optimization-based heuristic. To evaluate our proposed approach, a modified IEEE 118 bus testing system is employed with 10 aggregators serving 30 000 EVs. The simulation results indicate that our proposed distributed heuristic with energy trading can effectively increase the total profit of aggregators. In addition, the proposed distributed optimization-based heuristic strategy can achieve near-optimal performance.