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 reactive power. However, the distributed control EVs under such scenarios is a complex decision-making problem with enormous dynamics uncertainties. Most existing literature employs model-based approaches formulate problems, which require full models are time-consuming. This article proposes multiagent reinforcement learning algorithm featuring deep deterministic policy gradient (DDPG) method parameter sharing framework solve EVs’ coordinated toward both demand-side response regulations. proposed further enhance stability scalability privacy perseverance via location marginal prices. Simulation results based on modified IEEE 15-bus network developed validate its effectiveness providing system charging regulation services. price (LMP) PSDDPG evaluated achieve 38%, 16%, 25% speedup, 1.58, 0.69, 0.27 times higher reward over benchmarks DDPG, TD3, LMP-DDPG, respectively.