The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, occuring when generation from solar panels in the local grid exceeds consumption, and b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies.

翻译：本研究提出采用聚类技术为商业与居民产消者设计需求响应（DR）方案，旨在改变意大利分布式能源社区内产消者的消费行为。该聚合策略旨在：a）最小化主变电站的反向功率流（当本地电网太阳能发电量超过消费时发生）；b）转移通常发生在傍晚的系统峰值负荷。在聚类阶段，我们考虑日度产消者负荷曲线并将其划分为提取出的簇。研究采用了三种主流机器学习算法：k-means、k-medoids与凝聚式聚类。我们通过多项指标评估方法，包括本研究提出的新型指标——峰值性能得分（PPS）。采用动态时间弯曲距离的k-means算法在14个聚类下表现最佳，PPS达0.689。随后，我们从负荷形状、熵值及负荷类型角度分析每个提取簇，通过将其与合适DR方案（分时电价、尖峰电价与实时电价）匹配，识别具备优化目标实现潜力的簇。结果证实了所提聚类算法在生成有意义的灵活性簇方面的有效性，而导出的DR定价政策鼓励用户在非高峰时段消费。该方法对训练数据集可用性低及数据质量差具有鲁棒性，可被聚合商用于能源社区细分与个性化DR政策制定。