A Data-Driven Approach for Demand Side Flexibility: Reducing Peak Demand and CO2 Emissions in Smart Grid
DOI:
https://doi.org/10.24237/djes.2026.19303Keywords:
Demand Response , Load Shifting , Clustering Analysis, Time-of-Use Pricing, Carbon Reduction , Smart GridAbstract
The volatility in energy supply and the flexible demand require more efficient demand management techniques. The residential demand response has the ability to provide a promising solution. It becomes difficult to identify as households differ widely in how they utilize electricity and how flexible their usage patterns are. To address this issue, this study suggests a data-driven dual-clustering framework that utilizes both the electricity consumption behaviour and its flexibility potential for targeting residential consumers. Consumption clusters are formed using cluster validity indices that are used along with a majority voting mechanism, while the flexibility clusters are formed using a novel Demand Response Separability Score that emphasizes operational relevance over relying solely on statistical criteria. The cross-cluster analysis of these clusters reveals how consumption patterns relate to flexibility levels and facilitates the identification of most suitable households for the demand response programs. A simulation to achieve demand response is conducted by shifting loads from peak-hour to off-peak periods for selected flexible groups under a time-of-use pricing scheme. The results show that the suggested strategy achieves $12,928.77 saving in the electricity costs and a reduction of 25,857.54 kg in CO₂ emissions over the study period. The proposed framework offers improved practical feasibility by matching DR participation with household-specific flexibility capabilities. The importance of integrating behavioral and operational characteristics in consumer segmentation provides useful information to design demand response program for the efficient, scalable, and sustainable smart grid.
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Copyright (c) 2026 Seema Pal, Kumar Shantanu, Niraj Kumar Choudhary, Nitin Singh
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