Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability

TL;DR

This paper introduces a probabilistic inference method for wind farm layout optimization that achieves near-optimal solutions with higher efficiency and scalability compared to traditional algorithms, especially at higher discretization resolutions.

Contribution

It presents a novel probabilistic inference approach using sequential tree-reweighted message passing for wind farm layout optimization, demonstrating superior computational efficiency and scalability.

Findings

Near-optimal layouts within 3% of the best with low resolution

Up to 9% more power capacity at higher resolutions

Significantly reduced computational cost compared to state-of-the-art methods

Abstract

Successful development of wind farms relies on the optimal siting of wind turbines to maximize the power capacity under stochastic wind conditions and wake losses caused by neighboring turbines. This paper presents a novel method to quickly generate approximate optimal layouts to support infrastructure design decisions. We model the quadratic integer formulation of the discretized layout design problem with an undirected graph that succinctly captures the spatial dependencies of the design parameters caused by wake interactions. On the undirected graph, we apply probabilistic inference using sequential tree-reweighted message passing to approximate turbine siting. We assess the effectiveness of our method by benchmarking against a state-of-the-art branch and cut algorithm under varying wind regime complexities and wind farm discretization resolutions. For low resolutions, probabilistic inference can produce optimal or nearly optimal turbine layouts that are within 3% of the power capacity of the optimal layouts achieved by state-of-the-art formulations, at a fraction of the computational cost. As the discretization resolution (and thus the problem size) increases, probabilistic inference produces optimal layouts with up to 9% more power capacity than the best state-of-the-art solutions at a much lower computational cost.