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.

翻译：配备集成储能系统的风电场（即混合风电场）能够存储能量，并依据运行策略向电网调度电力。对于具备集成储能容量的单个风电场，采用以本地电网需求及市场条件作为输入参数的数据驱动调度策略，有望最大化风能价值。基于大气条件模拟生成的合成发电数据为提升数据驱动调度策略的鲁棒性提供了另一途径。为此，本研究开发了两种深度学习框架：一是COVE-NN，一种针对单个风电场设计的基于LSTM的调度策略，在Pyron站点的案例研究中，其经过43年模拟运行将年度COVE降低了32.3%；二是发电建模框架，在Palouse风电场验证中，该框架将RMSE降低了9.5%，并将功率曲线相似度提升了18.9%。这些模型共同为开发更鲁棒的数据驱动调度策略及向其他可再生能源系统扩展应用奠定了基础。