Using smart meter data to estimate demand response potential, with application to solar energy integration

This paper presents a new method for estimating the demand response potential of residential air conditioning (A/C), using hourly electricity consumption data (“smart meter” data) from 30,000 customer accounts in Northern California. We apply linear regression and unsupervised classification methods to hourly, whole-home consumption and outdoor air temperature data to determine the hours, if any, that each home׳s A/C is active, and the temperature dependence of consumption when it is active. When results from our sample are scaled up to the total population, we find a maximum of 270-360 MW (95% c.i.) of demand response potential over a 1-h duration with a 4 °F setpoint change, and up to 3.2-3.8 GW of short-term curtailment potential. The estimated resource correlates well with the evening decline of solar production on hot, summer afternoons, suggesting that demand response could potentially act as reserves for the grid during these periods in the near future with expected higher adoption rates of solar energy. Additionally, the top 5% of homes in the sample represent 40% of the total MW-hours of DR resource, suggesting that policies and programs to take advantage of this resource should target these high users to maximize cost-effectiveness.