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 shift the participants' consumption behavior to mitigate two issues a) the reverse power flow at the primary substation, that occurs when generation from solar panels in the local grid exceeds consumption and b) the system wide peak demand, that typically occurs during hours of the late afternoon. For the clustering stage, three popular algorithms for electrical load clustering are employed -- namely k-means, k-medoids and a hierarchical clustering algorithm -- alongside two different distance metrics -- namely euclidean and constrained Dynamic Time Warping (DTW). We evaluate the methods using different validation metrics including a novel metric -- namely peak performance score (PPS) -- that we propose in the context of this study. The best setup is employed to divide daily prosumer load profiles into clusters and each cluster is analyzed in terms of load shape, mean entropy and distribution of load profiles from each load type. These characteristics are then used to distinguish the clusters that would be most likely to aid with the DR schemes would fit each cluster. Finally, we conceptualize a DR system that combines forecasting, clustering and a price-based demand projection engine to produce daily individualized DR recommendations and pricing policies for prosumers participating in the program. The results of this study can be useful for network operators and utilities 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及住宅产消者需求响应(DR)程序设计中的应用。该程序旨在通过调整参与者的用电行为，缓解两个问题：一是当本地电网光伏发电量超过用电量时，主变电站出现的反向潮流问题；二是通常在傍晚时段出现的系统级用电高峰。在聚类阶段，我们采用了三种流行的电力负荷聚类算法——即k-means、k-medoids和层次聚类算法——并结合两种不同的距离度量方式——即欧氏距离和约束动态时间规整(DTW)。通过包括本研究提出的新型指标——即峰值性能评分(PPS)——在内的多种验证指标对方法进行评估。最佳方案被用于将日产消者负荷曲线划分为聚类簇，并从负荷形态、平均熵及各类负荷的分布特征等维度对每个簇进行分析。这些特征随后被用于区分最有可能对需求响应方案有帮助的簇，并确定适合每个簇的方案。最后，我们构建了一个整合预测、聚类及基于价格的需求预测引擎的DR系统，为参与该项目的产消者生成每日个性化需求响应建议及定价策略。本研究结果对于旨在灵活能源社区内为产消者群体制定针对性需求响应程序的电网运营商及公用事业公司具有参考价值。