Projects per year
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.
|Title of host publication||Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems|
|Number of pages||9|
|Publication status||Published - 3 May 2021|
|Event||The 20th International Conference on Autonomous Agents and Multiagent Systems - Virtual|
Duration: 3 May 2021 → 7 May 2021
|Name||Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS|
|Conference||The 20th International Conference on Autonomous Agents and Multiagent Systems|
|Abbreviated title||AAMAS 2021|
|Period||3/05/21 → 7/05/21|
Bibliographical noteFunding Information:
The authors would like to acknowledge FWO (Fonds Wetenschappelijk Onderzoek) for their support through the SB grants of Timothy Verstraeten (#1S47617N) and Eugenio Bargiacchi (#1SA2820N), and the post-doctoral grant of Pieter J.K. Libin (#1242021N). This research was supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme and under the VLAIO Supersized 4.0 ICON project.
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