Implementing optimal flexibility through demand response

The management of flexibility sources aiming at effective demand response (DR) is a vital task across power grid stakeholders. To this end, ICCS has been developing an AI-based flexibility forecasting and demand response service that is based on clustering techniques alongside for ASM Terni (distribution system operator in Terni Italy).

The I-NERGY flexibility forecasting and demand service explores the use of clustering techniques for the design and implementation of a 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.

Throughout the analysis of participant prosumers in the flexibility scheme ICCS managed to extract fourteen clusters of prosumers also listing their characteristics as follows:

  • Cluster 0 – Generation only
  • Cluster 1 – Residential loads with a late-night peak
  • Cluster 2 – Residential loads with a late afternoon peak
  • Clusters 3 & 8 – Mixed loads with morning peak
  • Clusters 4 & 6 – Residential loads with evening peak
  • Cluster 5 – Residential loads with evening peak
  • Cluster 7 – Commercial and public loads with working hour peaks
  • Cluster 9 – Consumption and generation
  • Cluster 10 – Inactive loads and university campus
  • Cluster 11 – Residential loads with multiple peaks in late afternoon
  • Cluster 12 – Mixed loads with early morning peak
  • Cluster 13 – Mostly residential with midday peak

Based on this cluster analysis the prosumers were thereafter matched to specific DR programs (Program 1 – time of use, Program 2 – critical peak pricing, 3 – real time pricing) as illustrated in the table below.


In this fashion ICCS created a preliminary DR administration plan for the ASM pilot. For more details on our work on this service, please visit our recently published preprint.