Sailing the Wind: Evaluating the Impact of COMA on Multi-Agent Active Wake Control in Wind Farms

Sailing the Wind: Evaluating the Impact of COMA on Multi-Agent Active Wake Control in Wind Farms: What is the effect of COMA on the problem of AWC compared to single-agent RL algorithms?

Filimon, Mihai (TU Delft Electrical Engineering, Mathematics and Computer Science)

de Weerdt, M.M. (mentor)
Neustroev, G. (mentor)
Pawełczak, Przemysław (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-30

The close proximity of wind turbines to one another in a wind farm can lead to inefficiency in terms of power production due to wake effects. One technique to mitigate the losses is to veer from their individual optimal direction. As such, the wakes can be steered away from downstream turbines in order to increase the overall power output. Multi-Agent Reinforcement Learning (MARL) models the interactions between wind turbines and determines an optimal control strategy through agents that learn the collective consequences of their actions. To
analyse the benefit of multi-agent cooperation and centralised critic evaluation, I investigated the effect of Counterfactual Multi-Agent Policy Gradients (COMA) on Active Wake Control. Ultimately, experiments on wind farms of three and sixteen turbines indicate that the algorithm performs moderately, yet worse than single-agent Reinforcement Learning. In addition, high computation costs hinder its application on real-life environments.

Active Wake Control
Wind Energy
Multi Agent Reinforcement Learning

http://resolver.tudelft.nl/uuid:5f0c0e5f-59aa-4fd6-8d94-deeefa5b60a4

Student theses

bachelor thesis

© 2023 Mihai Filimon