| Title: |
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control |
| Authors: |
Monroc, Claire, Bizon; Bušić, Ana; Dubuc, Donatien; Zhu, Jiamin |
| Contributors: |
Apprentissage, graphes et optimisation distribuée (ARGO); Département d'informatique - ENS-PSL (DI-ENS); École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria); IFP Energies nouvelles (IFPEN); Laboratory of Information, Network and Communication Sciences (LINCS); Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom Paris (IMT)-Sorbonne Université (SU); ANR-22-PETA-0004,AI-NRGY,Distributed AI-based architecture of future energy systems integrating very large amounts of distributed sources(2022) |
| Source: |
Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track ; https://hal.science/hal-04864926 ; Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, Dec 2024, Vancouver, Canada ; https://neurips.cc/ |
| Publisher Information: |
CCSD |
| Publication Year: |
2024 |
| Subject Terms: |
[INFO]Computer Science [cs] |
| Subject Geographic: |
Vancouver |
| Time: |
Vancouver, Canada |
| Description: |
International audience ; The wind farm control problem is challenging, since conventional model-based control strategies require tractable models of complex aerodynamical interactions between the turbines and suffer from the curse of dimension when the number of turbines increases. Recently, model-free and multi-agent reinforcement learning approaches have been used to address this challenge. In this article, we introduce WFCRL (Wind Farm Control with Reinforcement Learning), the first open suite of multi-agent reinforcement learning environments for the wind farm control problem. WFCRL frames a cooperative Multi-Agent Reinforcement Learning (MARL) problem: each turbine is an agent and can learn to adjust its yaw, pitch or torque to maximize the common objective (e.g. the total power production of the farm). WFCRL also offers turbine load observations that will allow to optimize the farm performance while limiting turbine structural damages. Interfaces with two state-of-the-art farm simulators are implemented in WFCRL: a static simulator (FLORIS) and a dynamic simulator (FAST.Farm). For each simulator, 10 wind layouts are provided, including 5 real wind farms. Two state-of-the-art online MARL algorithms are implemented to illustrate the scaling challenges. As learning online on FAST.Farm is highly time-consuming, WFCRL offers the possibility of designing transfer learning strategies from FLORIS to FAST.Farm. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-04864926; https://hal.science/hal-04864926v2/document; https://hal.science/hal-04864926v2/file/NeurIPS-2024-wfcrl-a-multi-agent-reinforcement-learning-benchmark-for-wind-farm-control-Paper-Datasets_and_Benchmarks_Track.pdf |
| Rights: |
https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.FF46ECC8 |
| Database: |
BASE |