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.

采用聚类技术实现需求响应节能项目，并结合预测、聚类和价格策略引擎为参与项目的生产者提供个性化的节约建议和定价策略。