Cloud-Edge Collaboration Control Strategy for Electric Vehicle Aggregators Participating in Frequency and Voltage Regulation

Abstract

With the increasing integration of renewable energy into power grids, ensuring the stability and reliability of power grids has become crucial. The intermittency of renewable energy poses a challenge for the frequency and voltage control of power grids. As an adjustable flexible load, electric vehicles (EVs) have emerged as an important solution for grid frequency and voltage control. A joint control and optimization strategy for electric vehicle aggregators (EVAs) to participate in grid frequency and voltage regulation based on a cloud-edge collaborative hierarchical scheduling architecture is proposed, and a multi-timescale EV charging pile cluster (EVC) scheduling model is established with the goal of maximizing the EVA profit. The strategy and model are grounded in the ancillary service market process. The EVA forecasts and optimizes to declare the active and reactive power capacities of the EVC to the market before the day and hour and controls the EVC to respond quickly and accurately to the frequency and voltage regulation instructions in the real-time stage. The methods of rolling optimization, model predictive control, evaluation of the feasible energy region and real-time capacity correction are adopted to coordinate the active and reactive power of EVC. The feasibility and effectiveness of the strategy are verified by an example, which provides an important reference for EVAs participating in power grid interactions.

Keywords

• Electric vehicle aggregators
• ancillary service market
• frequency and voltage regulation
• cloud-edge collaboration
• multi-timescale