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

• Published in: Energy policy 2014-10, Vol.73, p.607-619
• Main Authors: Dyson, Mark E.H., Borgeson, Samuel D., Tabone, Michaelangelo D., Callaway, Duncan S.
• Format: Article
• Language: English
• Subjects: Air conditioning; Air conditioning systems; Air conditioning. Ventilation; Applied sciences; California; Classification; Cost effectiveness; Demand response; Demand side management; Economic data; Electric energy; Electric power; Electricity; Energy; Energy consumption; Energy economics; Energy management; Energy. Thermal use of fuels; Estimating; Exact sciences and technology; General, economic and professional studies; Heating, air conditioning and ventilation; Linear models; Measuring instruments; Meters; Methodology. Modelling; Natural energy; Policies; Population; Production; Renewable integration; Reserves; Smart grid technology; Smart meter data; Solar energy; Studies; U.S.A
• ISSN: 0301-4215; 1873-6777
• DOI: 10.1016/j.enpol.2014.05.053

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 270–360MW (95% c.i.) of demand response potential over a 1-h duration with a 4°F setpoint change, and up to 3.2–3.8GW 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. •We use hourly electricity use data to estimate residential demand response (DR) potential.•The residential cooling DR resource is large and well-matched to solar variability.•Customer heterogeneity is large; programs should target high potential customers.