Deep Learning for Modeling and Dispatching Hybrid Wind Farm Power Generation
By: Zach Lawrence, Jessica Yao, Chris Qin
Potential Business Impact:
Smarter wind farms sell more clean energy.
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.
Similar Papers
Load and Renewable Energy Forecasting Using Deep Learning for Grid Stability
Machine Learning (CS)
Predicts wind and sun power for a stable grid.
Towards Accurate Forecasting of Renewable Energy : Building Datasets and Benchmarking Machine Learning Models for Solar and Wind Power in France
Signal Processing
Predicts wind and sun power for the whole country.
How to craft a deep reinforcement learning policy for wind farm flow control
Machine Learning (CS)
Makes wind turbines create more power.