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

Abstract

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 timeconsuming. This paper proposes a multi-agent reinforcement learning algorithm featuring a deep deterministic policy gradient method and a parameter sharing framework to solve the EVs coordinated active and reactive power control problem towards 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 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.

Distributed Real-Time Feedback Optimization for Renewable Energy Sources and Vehicle-to-Grid Power Compensation of Electric Vehicle Chargers in Distribution Systems

“…The reactive power support capability of EV chargers is often neglected. In fact, their reactive power support capability can be utilized to offer grid ancillary services [18][19][20][21]44].…”

“…Although these high-rating EV chargers can rapidly charge EVs, they will introduce negative impacts such as transformer overloading and voltage instability in distributed systems if not appropriately coordinated [11]. Hence, research into optimal EV charging scheduling has been done to optimally charge EVs or even control EVs to provide vehicle-to-grid (V2G) services [12][13][14][15][16][17][18][19][20][21]. Nevertheless, research into optimal EV charging scheduling usually focuses on optimizing the EV charging rate over a long time window, e.g., 1 h or 30 min.…”

Coordinated Voltage Control of Distribution Network Considering Multiple Types of Electric Vehicles

“…Reference [24] considered the effect of different electric vehicle loads on the voltage control effectiveness and introduced sag controllers for reactive power scheduling. In reference [25], a multiintelligent deep reinforcement learning algorithm and parameter sharing framework was proposed, which can solve the active-reactive coordination control problem of electric vehicles. Reference [26] proposed a model predictive voltage control method that can effectively compensate for the changing values of EV charging load and PV power and reduce the voltage fluctuations at charging stations.…”

Distributed demand response charging control of multiple plug‐in electric vehicle clusters

“…As for the control structure for populations EVs, there are always four control modes: centralised control [23,24], decentralised control [25], and distributed control [26] or some composite hierarchical control modes [27,28]. By formulating the EVs charging problem as a Markov game with an unknown transition function, the authors in [29] proposed a decentralised cooperative charging control strategy based on the multi-agent deep reinforcement learning [30]. The authors in [31] proposed centralised and decentralised valley-filling and rebound peak occurrence for EVs in the grid at the system level.…”