Power Grid Control with Graph-Based Distributed Reinforcement Learning
The necessary integration of renewable energy sources, combined with the expanding scale of power networks, presents significant challenges in controlling modern power grids. Traditional control systems, which are human and optimization-based, struggle to adapt and to scale in such an evolving context, motivating the exploration of more dynamic and distributed control strategies. This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management. The proposed architecture consists of a network of distributed low-level agents acting on individual power lines and coordinated by a high-level manager agent. A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation. To accelerate convergence and enhance learning stability, the framework integrates imitation learning and potential-based reward shaping. In contrast to conventional decentralized approaches that decompose only the action space while relying on global observations, this method also decomposes the observation space. Each low-level agent acts based on a structured and informative local view of the environment constructed through the GNN. Experiments on the Grid2Op simulation environment show the effectiveness of the approach, which consistently outperforms the standard baseline commonly adopted in the field. Additionally, the proposed model proves to be much more computationally efficient than the simulation-based Expert method.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningGraph Neural NetworkSimilar Papers 제목 키워드 기반
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
The rise of renewable energy and distributed generation requires new approaches to overcome the limitations of traditional methods. In this context, Graph Neural Networks are promising due to their ability to learn from …
Decision Makingreinforcement-learningReinforcement LearningRepresentation Learning+1CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities
As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographi…
BenchmarkingManagementreinforcement-learningReinforcement LearningDistributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework join…
Deep Reinforcement Learningenergy managementManagementreinforcement-learning+2PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in d…
Deep Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
Grid-interactive building control has emerged as a promising approach for improving demand-side flexibility in modern power systems. Realistic studies of such systems, however, require tightly coupled co-simulation acros…
Reinforcement Learning