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 270–360MW (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.

[1]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[2]  Jian Ma,et al.  Operational Impacts of Wind Generation on California Power Systems , 2009, IEEE Transactions on Power Systems.

[3]  Christopher M. Jones,et al.  Deep carbon reductions in California require electrification and integration across economic sectors , 2013 .

[4]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[5]  Peter Cappers,et al.  Mass Market Demand Response and Variable Generation Integration Issues: A Scoping Study , 2012 .

[6]  Joseph H. Eto.  Demand Response Spinning Reserve Demonstration -- Phase 2 Findings from the Summer of 2008 , 2010 .

[7]  Ana Mileva,et al.  SunShot solar power reduces costs and uncertainty in future low-carbon electricity systems. , 2013, Environmental science & technology.

[8]  Peter Cappers,et al.  An assessment of market and policy barriers for demand response providing ancillary services in U.S. electricity markets , 2013 .

[9]  John Peirson,et al.  Electricity load and temperature: issues in dynamic specification , 1994 .

[10]  Duncan S. Callaway,et al.  Using Residential Electric Loads for Fast Demand Response: The Potential Resource and Revenues, the Costs, and Policy Recommendations , 2012 .

[11]  J. Kelly Kissock,et al.  Targeting Residential Energy Assistance , 2007 .

[12]  David E. Claridge,et al.  Development of a Toolkit for Calculating Linear, Change–Point Linear and Multiple–Linear Inverse Building Energy Analysis Models, ASHRAE Research Project 1050-RP, Detailed Test Results , 2001 .

[13]  M. Greenstone,et al.  Climate Change, Mortality, and Adaptation: Evidence from Annual Fluctuations in Weather in the Us , 2007 .

[14]  B.J. Kirby.  Load Response Fundamentally Matches Power System Reliability Requirements , 2007, 2007 IEEE Power Engineering Society General Meeting.

[15]  Duncan S. Callaway.  Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[16]  John Peirson,et al.  Non‐Linearities in Electricity Demand and Temperature: Parametric Versus Non‐Parametric Methods , 1997 .

[17]  Joseph H. Eto,et al.  Incorporating residential AC load control into ancillary service markets: Measurement and settlement , 2013 .

[18]  Ian Beausoleil-Morrison,et al.  Disaggregating categories of electrical energy end-use from whole-house hourly data , 2012 .

[19]  P. Denholm,et al.  Evaluating the Limits of Solar Photovoltaics (PV) in Traditional Electric Power Systems , 2007 .

[20]  Maximilian Auffhammer,et al.  IMPACTS OF CLIMATE CHANGE ON RESIDENTIAL ELECTRICITY CONSUMPTION: EVIDENCE FROM BILLING DATA , 2011 .

[21]  Peter Cappers,et al.  Market and Policy Barriers for Demand Response Providing Ancillary Services in U.S. Markets , 2014 .

[22]  David E. Claridge,et al.  Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings , 1998 .

[23]  Fred C. Schweppe,et al.  Modeling and weather-normalization of whole-house metered data for residential end-use load shape estimation , 1988 .