Every minute counts: Wind farm power control during imbalance settlement using distributional soft actor-critic

Abstract

Wind farms can provide flexibility by curtailing their output during periods of excess generation in the power grid. This can be done through implicit balancing, where the power setpoints of the farm are adapted to anticipate the imbalance price. By doing so, the wind farm helps reduce the resulting system imbalance while also lowering its own costs. However, implicit balancing is challenging since the imbalance prices are unknown in advance and hard to predict. Furthermore, the control strategy of the wind farm can affect the imbalance price formation. To address both challenges, we develop a control policy with 1-minute setpoints using distributional soft actor-critic. Evaluated on real data of the Belgian grid and an offshore wind farm, we show that our method outperforms a price threshold controller, often used in industry, by 88% and performs on par with soft actor-critic. Furthermore, unlike soft actor-critic, our approach can learn a risk-averse policy by optimizing a risk measure, such as conditional value at risk. The risk-averse policy was found to better account for its own impact on the price, showing a reduction in overshoots, while achieving a similar degree of imbalance cost savings. Last, we find that using 1-minute instead of 15-minute setpoints further reduces imbalance costs for all evaluated control policies.