【摘要】 在高比例光伏配网的新形势下，研究负荷可调节潜力对于电网安全稳定和调度精益化具有积极作用。为此，文中提出一种基于数据模型双驱动的负荷调节潜力评估及预测方法。首先，构建负荷调节潜力多维评估指标体系及评估模型，提出k-均值（k-means）算法负荷特征提取和自组织映射（self-organizing map, SOM）算法调节潜力特征融合的二次聚类方法，实现对各类负荷时序调节潜力的评估；然后，提出融合双向长短期记忆（bidirectional long short-term memory, BiLSTM）神经网络和动态模式分解（dynamic mode decomposition, DMD）的负荷调节潜力智能预测方法，实现对未来24 h 15 min级负荷调节潜力的预测；最后，使用某地负荷数据对所提策略开展仿真验证，评估结果验证了所提评估与预测方法的有效性，表明BiLSTM-DMD模型具有较高的预测精度。更多还原

【Abstract】 In the context of high penetration of photovoltaics in distribution networks, researching the load regulation potential is crucial for grid safety and refined dispatching. Thus, a data-driven and model-driven evaluation and prediction method for load regulation potential is proposed. Firstly, a multi-dimensional evaluation index system and assessment model for load regulation potential are constructed. A two-step clustering method, including k-means for load feature extraction and selforganizing map(SOM) for regulation potential feature fusion, is introduced. This enables the assessment of the temporal upregulation and down-regulation potential of various loads. Secondly, an intelligent prediction method that integrates bidirectional long short-term memory(BiLSTM) and dynamic mode decomposition(DMD) is proposed to predict the 15-minute level load regulation potential for the next day. Finally, simulation verification of the proposed strategy is conducted using local load data. The evaluation results confirm the effectiveness of the proposed assessment and prediction methods,demonstrating that the BiLSTM-DMD model can achieve high prediction accuracy.更多还原