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Big Data, Consumer Behavior, Energy and Climate Change

Paper Session

Sunday, Jan. 6, 2019 10:15 AM - 12:15 PM

Atlanta Marriott Marquis, International 6
Hosted By: American Economic Association
  • Chair: Karen Palmer, Resources for the Future

Dynamic Electricity Pricing: Smart Consumers for Smart Pricing

Mar Reguant
,
Northwestern University
David Rapson
,
University of California-Davis
Natalia Fabra
,
University Carlos III of Madrid

Abstract

There is empirical evidence on the ability of consumers to respond to price changes in pilot programs, or randomized field experiments (Jessoe and Rapson (2014), Faruqui and Sergici (2010) and Wolak (2010)). While these experiments demonstrate that with information and appropriate incentives, consumers respond by altering their consumption efficiently, these studies suffer from a potential weakness: a problem of external validity can occur because the subjects participating in the experiments did so voluntarily. In addition, the number of participants was generally not large, exacerbating concerns about representativeness of the sample. We rely on a large data set of hourly electricity consumption, for a representative sample of Spanish households, who face real time pricing (RTP). We exploit exogenous variation in electricity prices to test consumer responses to price signals.

Structural Approach to Dynamic Energy Pricing and Consumer Welfare

Matthew Harding
,
University of California-Irvine
Jerry Hausman
,
Massachusetts Institute of Technology
Kyle Kettler
,
University of California-Irvine

Abstract

With the proliferation of smart meters and associated enabling technologies like smart thermostats, there is growing interest in policies to manage electricity demand in real time and in response to hourly fluctuations in the cost of production. Dynamic pricing enables suppliers to charge a different marginal price for electricity for different hours of the day. We analyze a panel of 15 minute household level observations from a large scale randomized control trial conducted in the summer of 2011 in the US. Experimental treatments include two price structures, and four enabling technologies for information provision and demand response at the household level about the prevailing marginal cost of electricity. We build a penalized structural demand model for each hour of the day in order to derive a data driven selection strategy to identify the relevant cross hour price elasticities to which households respond. We focus on identifying the welfare impact of dynamic pricing in a setting with heterogeneous agents. Our model accounts for both heterogeneity due to observable demographics and also latent heterogeneity in consumer types.

Creating comfort in a warming world: The role of smart thermostats

Joshua Blonz
,
Resources for the Future
Karen Palmer
,
Resources for the Future
Andrew Royal
,
Resources for the Future
Margaret Walls
,
Resources for the Future
Casey Wichman
,
Resources for the Future

Abstract

Recent projections suggest ambient temperatures will increase substantially by 2100 as a result of climate change. Air conditioning may mitigate the worst impacts but with potential negative feedback effects on emissions. In this paper, we use a novel dataset of real-time information from smart thermostat users to explore the extent to which technologies can mitigate these feedback effects and also efficiently produce home comfort. We analyze (i) how outdoor temperatures affect indoor behavior and user interaction with thermostat settings; (ii) how “smart” features of a thermostat improve indoor comfort, measured using data from the thermostat; and (iii) the tradeoff between energy savings and comfort from the smart features.

Smart Thermostats, Social Information, and Energy Conservation: Distributional Evidence from a Field Experiment

Alec Brandon
,
University of Chicago
John A. List
,
University of Chicago
Robert Metcalfe
,
Boston University
Michael Price
,
University of Alabama

Abstract

Combining theory with a field experiment, we explore different channels through which smart-grid technologies may influence household energy demand. Our theory provides a simple empirical test to parse the competing models: measure higher moments of energy use. Results suggest that pro-social motivations are the primary channel through which our smart technology, a smart thermostat, impacts energy use. However, cross-sectional variation in this response illustrates the importance of selection in fully leveraging these motivations. Finally, counterfactual simulations of the wholesale electricity market highlight meaningful savings from adoption at scale, with changes in variance driving more than one-third of the savings.
Discussant(s)
Judson Boomhower
,
University of California-San Diego
Richard Sweeney
,
Boston College
Kenneth Gillingham
,
Yale University
Katrina Jessoe
,
University of California-Davis
JEL Classifications
  • Q4 - Energy
  • C5 - Econometric Modeling