Scalable Optimization for Wind Farm Control using Coordination Graphs

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Abstract

Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict constraints on power output to ensure stability of the electricity grid. Specifically, a wind farm controller is required to match the farm's power production with a power demand imposed by the grid operator. This is a non-trivial optimization problem, as complex dependencies exist between the wind turbines. State-of-the-art wind farm control typically relies on physics-based heuristics that fail to capture the full load spectrum that defines a turbine's health status. When this is not taken into account, the long-term viability of the farm's turbines is put at risk. Given the complex dependencies that determine a turbine's lifetime, learning a flexible and optimal control strategy requires a data-driven approach. However, as wind farms are largescale multi-agent systems, optimizing control strategies over the full joint action space is intractable.We propose a new learning method for wind farm control that leverages the sparse wind farm structure to factorize the optimization problem. Using a Bayesian approach, based on multi-agent Thompson sampling, we explore the factored joint action space for configurations that match the demand, while considering the lifetime of turbines. We apply our method to a grid-like wind farm layout, and evaluate configurations using a state-of-the-art wind flow simulator. Our results are competitive with a physics-based heuristic approach in terms of demand error, while, contrary to the heuristic, our method prolongs the lifetime of high-risk turbines.

Original languageEnglish
Title of host publicationProc. of the 20th International Conference on Autonomous Agents and Multiagent Systems
PublisherIFAAMAS
Pages1362-1370
Number of pages9
ISBN (Electronic)9781450375184
DOIs
Publication statusPublished - 3 May 2021
EventThe 20th International Conference on Autonomous Agents and Multiagent Systems - Virtual
Duration: 3 May 20217 May 2021
https://aamas2021.soton.ac.uk/

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

ConferenceThe 20th International Conference on Autonomous Agents and Multiagent Systems
Abbreviated titleAAMAS 2021
Period3/05/217/05/21
Internet address

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