Power prediction and scheduling optimization method for large wind farm clusters
大型风电场集群功率预测与调度优化方法

摘要

大型风电场集群在并网运行过程中面临功率预测不确定性和调度优化的双重挑战。针对这一问题，提出一种融合高精度功率预测与自适应调度优化的综合方法。基于多源气象数据与风机运行特性，构建深度学习预测模型，实现对风电功率的高精度短期预测。结合预测结果与电网运行约束，采用多目标优化算法对风电场群的发电计划进行动态调整，以兼顾出力平稳性与经济性。仿真结果表明，该方法显著降低预测误差，提高风电场群整体调度效率，为大规模风电并网运行提供了有效技术支持。

Abstract

Large-scale wind farm clusters face dual challenges of power prediction uncertainty and dispatch optimization during grid-connected operation. To address this issue, a comprehensive method integrating high-precision power prediction with adaptive dispatch optimization is proposed. By leveraging multi-source meteorological data and wind turbine operational characteristics, a deep learning prediction model is constructed to achieve high-precision short-term wind power forecasting. Combining the predicted results with grid operation constraints, a multi-objective optimization algorithm is employed to dynamically adjust the generation plans of wind farm clusters, balancing output stability and economic efficiency. Simulation results demonstrate that this approach significantly reduces prediction errors and enhances overall dispatch efficiency of wind farm clusters, providing effective technical support for large-scale wind power grid integration.