Deep Learning for Modeling and Dispatching Hybrid Wind Farm Power Generation

TL;DR

This paper develops deep learning models for optimizing dispatch and modeling power generation in hybrid wind farms, demonstrating significant improvements in operational efficiency and model accuracy through case studies.

Contribution

It introduces two novel deep learning frameworks: COVE-NN for dispatch optimization and a synthetic data-based power modeling approach, enhancing robustness and accuracy.

Findings

COVE-NN reduced annual COVE by 32.3% in simulations.

Power modeling framework decreased RMSE by 9.5%.

Power curve similarity improved by 18.9%.

Abstract

Wind farms with integrated energy storage, or hybrid wind farms, are able to store energy and dispatch it to the grid following an operational strategy. For individual wind farms with integrated energy storage capacity, data-driven dispatch strategies using localized grid demand and market conditions as input parameters stand to maximize wind energy value. Synthetic power generation data modeled on atmospheric conditions provide another avenue for improving the robustness of data-driven dispatch strategies. To these ends, the present work develops two deep learning frameworks: COVE-NN, an LSTM-based dispatch strategy tailored to individual wind farms, which reduced annual COVE by 32.3% over 43 years of simulated operations in a case study at the Pyron site; and a power generation modeling framework that reduced RMSE by 9.5% and improved power curve similarity by 18.9% when validated on the Palouse wind farm. Together, these models pave the way for more robust, data-driven dispatch strategies and potential extensions to other renewable energy systems.