Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control

Abstract

A one-step two-critic deep reinforcement learning (OSTC-DRL) approach for inverter-based volt-var control (IB-VVC) in active distribution networks is proposed in this paper. Firstly, considering IB-VVC can be formulated as a single-period optimization problem, we formulate the IB-VVC as a one-step Markov decision process rather than the standard Markov decision process, which simplifies the DRL learning task. Then we design the one-step actor-critic DRL scheme which is a simplified version of recent DRL algorithms, and it avoids the issue of Q value overestimation successfully. Furthermore, considering two objectives of VVC: minimizing power loss and eliminating voltage violation, we utilize two critics to approximate the rewards of two objectives separately. It simplifies the approximation tasks of each critic, and avoids the interaction effect between two objectives in the learning process of critic. The OSTC-DRL approach integrates the one-step actor-critic DRL scheme and the two-critic technology. Based on the OSTC-DRL, we design two centralized DRL algorithms. Further, we extend the OSTC-DRL to multi-agent OSTC-DRL for decentralized IB-VVC and design two multi-agent DRL algorithms. Simulations demonstrate that the proposed OSTC-DRL has a faster convergence rate and a better control performance, and the multi-agent OSTC-DRL works well for decentralized IB-VVC problems.