【目的】为解决分布式智能配电网中因新能源出力随机性与负荷时变特性不匹配导致的弃能问题，本文提出了一种融合卷积神经网络(CNN)-双向门控循环单元(BiGRU)-多头注意力(MHA)机制的源荷功率超短期预测模型。【方法】首先，采用CNN提取风光功率的时空关联特征，通过BiGRU捕捉负荷序列的前后向时序依赖关系；其次，引入MHA机制动态加权关键时间步信息，采用牛顿-拉夫逊优化算法(NRBO)进行超参数自动调优，提升模型泛化能力；最后，给出了NRBO优化CNN-BiGRU-MHA模型的流程，实现了源荷功率超短期预测。【结果】案例仿真和比较结果表明，相较于CNN-BiGRU和BiGRU模型，所提CNN-BiGRU-MHA模型的相对误差分别降低了51.5%和74.1%；所提NRBO-CNN-BiGRU-MHA模型的预测准确率优于其他常用算法模型，其预测峰值的高度和趋势与实际值非常吻合，该模型擅于处理平缓特征，并且面对用电高峰与低谷工况的适应性和鲁棒性强。【结论】本文所提NRBO-CNN-BiGRU-MHA模型在不同天气条件下均表现出更稳定的预测性能，验证了其时空特征挖掘的有效性，为高比例新能源接入场景下的功率预测提供了新思路，对促进可再生能源消纳具有实用价值。

[Objective] To address the renewable energy curtailment caused by the mismatch between stochastic renewable generation and time-varying load characteristics in distributed smart grids, this paper proposes a source-load power ultra-short-term forecasting model integrating convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and multi-head attention (MHA) mechanisms. [Methods] Firstly, a CNN was emplayed to extract spatiotemporal features of renewable power. Subsequently, a BiGRU was utilized to capture bidirectional temporal dependencies in load sequences. Thirdly, MHA was introduced to dynamically assign weights to critical time-step information, and a Newton-Raphson based optimizer (NRBO) was utilized for the automatic hyperparameter tuning to enhance model generalization capability. Finally, this paper presented the NRBO-optimized CNN-BiGRU-MHA modeling framework, achieving accurate ultra-short-term forecasting of source-load power. [Results] Case studies demonstrated that compared to CNN-BiGRU and BiGRU models, the proposed CNN-BiGRU-MHA model reduced the relative error by 51.5% and 74.1%. The proposed NRBO-CNN-BiGRU-MHA model outperformed other commonly used algorithm models in prediction accuracy, with its predicted peak values closely matching the actual values in both magnitude and trend. The model excelled in handling smooth features and exhibited strong adaptability and robustness under both peak and off-peak power load conditions.[Conclusion] The model proposed in this paper demonstrates more stable prediction performance under different weather conditions, verifying the effectiveness of its spatio-temporal feature mining. It provides a new idea for power forecasting in scenarios with high-proportion new energy integration and has practical value for promoting the consumption of renewable energy.

南方电网科技项目(GDKJXM20231387)