Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks

The deployment of renewable energy in power systems may raise serious voltage instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services including active and reactive power. However, the distributed control of EVs under such scenarios is a complex decision-making problem with enormous dynamics and uncertainties. Most existing literature employs model-based approaches to formulate active and reactive power control problems, which require full models and are time-consuming. This article proposes a multiagent reinforcement learning algorithm featuring a deep deterministic policy gradient (DDPG) method and a parameter sharing framework to solve the EVs' coordinated active and reactive power control problem toward both demand-side response and voltage regulations. The proposed algorithm can further enhance the learning stability and scalability with privacy perseverance via the location marginal prices. Simulation results based on a modified IEEE 15-bus network are developed to validate its effectiveness in providing system charging and voltage regulation services. The proposed location marginal price (LMP) PSDDPG algorithm is evaluated to achieve 38%, 16%, and 25% speedup, and 1.58, 0.69, and 0.27 times higher reward over the benchmarks DDPG, TD3, and LMP-DDPG, respectively.