Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach

arXiv:2402.15932v1 Announce Type: new
Abstract: In the rapidly evolving domain of electrical power systems, the Volt-VAR optimization (VVO) is increasingly critical, especially with the burgeoning integration of renewable energy sources. Traditional approaches to learning-based VVO in expansive and dynamically changing power systems are often hindered by computational complexities. To address this challenge, our research presents a novel framework that harnesses the potential of Deep Reinforcement Learning (DRL), specifically utilizing the Importance Weighted Actor-Learner Architecture (IMPALA) algorithm, executed on the RAY …

abstract arxiv complexities computational cs.lg cs.sy domain eess.sy energy framework integration optimization power reinforcement reinforcement learning renewable rllib scalable systems type