Quantifying the Demand Response Potential of Residential Loads in India

Siddhant Vibhute

Indian Institute of Technology Bombay, Mumbai, India

Deepti Shakya

Indian Institute of Technology Bombay, Mumbai, India

Anupama Kowli

Indian Institute of Technology Bombay, Mumbai, India
Corresponding Author: anu.kowli@iitb.ac.in

Cite this article

Vibhute, S., Shakya, D., Kowli, A. (2024). Quantifying the Demand Response Potential of Residential Loads in India. In Proceedings of Energise 2023- Lifestyle, Energy Efficiency, and Climate Action, pp 163–172, Alliance for an Energy Efficient Economy. https://doi.org/10.62576/XEDC4346


  • Smart meter data from residences in Pune City is analyzed for estimating urban residential load in India.
  • Appliance ownership data is exploited to infer electricity usage and peaks in the load curve for each household.
  • Potential of demand response (DR) programs is studied using this data set to shave peaks by load shifting.
  • Load composition of residences contributing to system peak is studied to estimate their DR potential.


The paper investigates how residential loads in India contribute to grid peak load and how to manage such peaks. Algorithms that process smart meter data from residential loads and compute attributes that capture their contribution to the system peak are devised and demonstrated in the paper. Specifically, a distribution system is considered based on a study on households in Pune, India. To enhance the household dataset for analysis, a synthetic data generation technique is employed. The algorithms developed here are applied to this system to showcase their capabilities. Results show how simple attributes such as peak amplitude and peak duration can extract sufficient information to ensure households that contribute to system peak are appropriately identified. Moreover, these attributes offer insights into appliance ownership, supporting the development of effective DR programs. A demonstration of how shifting of individual peaks can significantly impact system peak is also provided. The results lay the foundation for designing meaningful DR programs leveraging the smart meter data.


Demand Response, Residential Loads, Peak Load Shaving, Load Shifting


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