The present study explores the use of clustering techniques for the design and implementation of a demand response (DR) program for commercial and residential prosumers. The goal of the program is to alter the consumption behavior of the prosumers pertaining to a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, that occurs when generation from solar panels in the local grid exceeds consumption, and b) shave the system wide peak demand, that typically occurs during the hours of late afternoon. Regarding the clustering stage, three popular machine learning algorithms for electrical load clustering are employed -namely k-means, k-medoids and an agglomerative hierarchical clustering- alongside two different distance measures -namely euclidean and constrained dynamic time warping (DTW). We evaluate the methods using multiple validation metrics including a novel metric -namely peak performance score (PPS)- that we propose in the context of this study. The best model is employed to divide daily prosumer load profiles into clusters and each cluster is analyzed in terms of load shape, mean entropy, and load type distribution. These characteristics are then used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to appropriate DR schemes including time of use (TOU), critical peak pricing (CPP), and real-time pricing (RTP). The results of this study can be useful for network operators, utilities, and aggregators that aim to develop targeted DR programs for groups of prosumers within flexible energy communities.

翻译：本研究探索了聚类技术在商业和住宅产消者需求响应（DR）方案设计与实施中的应用。该方案旨在改变意大利某分布式能源社区内产消者的用电行为。这种聚合的目标是：a) 最小化主变电站的反向功率流——当本地电网中太阳能发电量超过用电量时发生的现象；b) 削除系统级峰值负荷——通常发生在傍晚时间。在聚类阶段，我们采用了三种常用的电力负荷聚类机器学习算法——即k-means、k-medoids和凝聚层次聚类——以及两种不同的距离度量——即欧氏距离和约束动态时间规整（DTW）。我们使用多种验证指标评估这些方法，包括本研究提出的新指标——峰值性能评分（PPS）。最佳模型用于将每日产消者负荷曲线划分为聚类，并分析每个聚类的负荷形状、平均熵和负荷类型分布。随后利用这些特征识别具有实现优化目标潜力的聚类，并将其与适当的需求响应方案（包括分时电价（TOU）、尖峰电价（CPP）和实时电价（RTP））相匹配。本研究结果可为电网运营商、公用事业公司和聚合商在灵活能源社区内针对产消者群体开发目标需求响应方案提供参考。