Graph Reinforcement Learning in Power Grids: A Survey
- URL: http://arxiv.org/abs/2407.04522v1
- Date: Fri, 5 Jul 2024 14:07:15 GMT
- Title: Graph Reinforcement Learning in Power Grids: A Survey
- Authors: Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev, Josephine Thomas, Bernhard Sick, Christoph Scholz,
- Abstract summary: Deep learning approaches to overcome the lack of flexibility of traditional methods in power grids use cases.
The application of GNNs is particularly promising due to their ability to learn from graph-structured data present in power grids.
This review analyses the ability of GRL to capture the inherent graph structure of power grids to improve representation learning and decision making in different power grid use cases.
- Score: 1.3124421498970822
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The challenges posed by renewable energy and distributed electricity generation motivate the development of deep learning approaches to overcome the lack of flexibility of traditional methods in power grids use cases. The application of GNNs is particularly promising due to their ability to learn from graph-structured data present in power grids. Combined with RL, they can serve as control approaches to determine remedial grid actions. This review analyses the ability of GRL to capture the inherent graph structure of power grids to improve representation learning and decision making in different power grid use cases. It distinguishes between common problems in transmission and distribution grids and explores the synergy between RL and GNNs. In transmission grids, GRL typically addresses automated grid management and topology control, whereas on the distribution side, GRL concentrates more on voltage regulation. We analyzed the selected papers based on their graph structure and GNN model, the applied RL algorithm, and their overall contributions. Although GRL demonstrate adaptability in the face of unpredictable events and noisy or incomplete data, it primarily serves as a proof of concept at this stage. There are multiple open challenges and limitations that need to be addressed when considering the application of RL to real power grid operation.
Related papers
- SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids [55.35059657148395]
We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for Graph Neural Networks (GNNs) in power systems (PS) operations.
SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages.
arXiv Detail & Related papers (2024-07-17T09:01:38Z) - A Perspective on Foundation Models for the Electric Power Grid [53.02072064670517]
Foundation models (FMs) currently dominate news headlines.
We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities.
We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
arXiv Detail & Related papers (2024-07-12T17:09:47Z) - PowerGraph: A power grid benchmark dataset for graph neural networks [7.504044714471332]
We present PowerGraph, which comprises GNN-tailored datasets for power flows, optimal power flows, and cascading failure analyses.
Overall, PowerGraph is a multifaceted GNN dataset for diverse tasks that includes power flow and fault scenarios with real-world explanations.
arXiv Detail & Related papers (2024-02-05T09:24:52Z) - Graph Decision Transformer [83.76329715043205]
Graph Decision Transformer (GDT) is a novel offline reinforcement learning approach.
GDT models the input sequence into a causal graph to capture potential dependencies between fundamentally different concepts.
Our experiments show that GDT matches or surpasses the performance of state-of-the-art offline RL methods on image-based Atari and OpenAI Gym.
arXiv Detail & Related papers (2023-03-07T09:10:34Z) - Reinforcement Learning for Resilient Power Grids [0.23204178451683263]
Traditional power grid systems have become obsolete under more frequent and extreme natural disasters.
Most power grid simulators and RL interfaces do not support simulation of power grid under large-scale blackouts or when the network is divided into sub-networks.
In this study, we proposed an updated power grid simulator built on Grid2Op, an existing simulator and RL interface, and experimented on limiting the action and observation spaces of Grid2Op.
arXiv Detail & Related papers (2022-12-08T04:40:14Z) - Power Flow Balancing with Decentralized Graph Neural Networks [4.812718493682454]
We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in a generic grid.
The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.
arXiv Detail & Related papers (2021-11-03T12:14:56Z) - Power to the Relational Inductive Bias: Graph Neural Networks in
Electrical Power Grids [1.732048244723033]
We argue that there is a gap between GNN research driven by benchmarks which contain graphs that differ from power grids in several important aspects.
We address this gap by means of (i) defining power grid graph datasets in inductive settings, (ii) an exploratory analysis of graph properties, and (iii) an empirical study of the concrete learning task of state estimation on real-world power grids.
arXiv Detail & Related papers (2021-09-08T12:56:00Z) - Reinforcement Learning for Decision-Making and Control in Power Systems:
Tutorial, Review, and Vision [9.363707557258175]
reinforcement learning (RL) has attracted surging attention in recent years.
We focus on RL and aim to provide a tutorial on various RL techniques and how they can be applied to the decision-making and control in power systems.
In particular, we select three key applications, including frequency regulation, voltage control, and energy management, for illustration.
arXiv Detail & Related papers (2021-01-27T03:45:44Z) - Decentralized Control with Graph Neural Networks [147.84766857793247]
We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers.
GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties.
The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
arXiv Detail & Related papers (2020-12-29T18:59:14Z) - Resource Allocation via Graph Neural Networks in Free Space Optical
Fronthaul Networks [119.81868223344173]
This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks.
We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure.
The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
arXiv Detail & Related papers (2020-06-26T14:20:48Z) - Graph Ordering: Towards the Optimal by Learning [69.72656588714155]
Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, prediction, and community detection.
However, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.
In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach.
arXiv Detail & Related papers (2020-01-18T09:14:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.