Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Home
  • Home
Hostname: page-component-78dcdb465f-8p2q5 Total loading time: 0.257 Render date: 2021-04-16T18:43:36.771Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }
EnglishFrançais
Behavioural Public Policy Behavioural Public Policy

Article contents

Behavioral considerations for effective time-varying electricity prices

Published online by Cambridge University Press:  06 October 2017

IAN SCHNEIDER  and
CASS R. SUNSTEIN
IAN SCHNEIDER
Affiliation:
Institute for Data, Systems, and Society, MIT, Cambridge, MA, USA
CASS R. SUNSTEIN
Affiliation:
Harvard University, Cambridge, MA, USA
Corresponding
E-mail address:
ischneid@mit.edu
Get access
Rights & Permissions[Opens in a new window]

Abstract

Wholesale prices for electricity vary significantly due to high fluctuations and low elasticity of short-run demand. End-use customers have typically paid flat retail rates for their electricity consumption, and time-varying prices (TVPs) have been proposed to help reduce peak consumption and lower the overall cost of servicing demand. Unfortunately, the general practice is an opt-in system: a default rule in favor of TVPs would be far better. A behaviorally informed analysis also shows that when transaction costs and decision biases are taken into account, the most cost-reflective policies are not necessarily the most efficient. On reasonable assumptions, real-time prices can result in less peak conservation of manually controlled devices than time-of-use or critical-peak prices. For that reason, the trade-offs between engaging automated and manually controlled loads must be carefully considered in time-varying rate design. The rate type and accompanying program details should be designed with the behavioral biases of consumers in mind, while minimizing price distortions for automated devices.

Type
Articles
Information
Behavioural Public Policy , Volume 1 , Issue 2 , November 2017 , pp. 219 - 251
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below.
If you should have access and can't see this content please contact technical support.

References

Abadie, A. and Gay, S. (2006), ‘The impact of presumed consent legislation on cadaveric organ donation: A cross-country study’, Journal of health economics, 25(4): 599–620.CrossRefGoogle ScholarPubMed
Allcott, H. (2009), ‘Real Time Pricing and Electricity Markets’. https://www.gsb.stanford.edu/sites/default/files/documents/ame_03_09_allcott.pdf Google Scholar
Allcott, H., Knittel, C. and Taubinsky, D. (2015), ‘Tagging and targeting of energy efficiency subsidies’, American Economic Review: Papers & Proceedings 2015, 105(5): 187–191.CrossRefGoogle Scholar
Allcott, H. and Mullainathan, S. (2010), ‘Behavioral Science and Energy Policy’, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.208.5738&rep=rep1&type=pdf Google Scholar
Allcott, H. and Sunstein, C. (2015), ‘Regulating Internalities’, Social Science Research Network. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2571343 Google Scholar
Ayres, I., Raseman, S. and Shih, A. (2009), ‘Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage’, National Bureau of Economic Research, Working Paper. http://www.nber.org/papers/w15386 Google Scholar
Badtke-Berkow, M., Centore, M., Mohlin, K. and Spiller, B. (2015), ‘A primer on time-variant electricity pricing’, Environmental Defense Fund. https://www.edf.org/sites/default/files/a_primer_on_time-variant_pricing.pdf Google Scholar
Beshears, J., Choi, J., Laibson, D. and Madrian, B. (2009), ‘The importance of default options for retirement saving outcomes: Evidence from the United States’, Social Security Policy in a Changing Environment, University of Chicago Press, 167–195.CrossRefGoogle Scholar
Boisvert, R., Cappers, P., Goldman, C., Neenan, B. and Hopper, N. (2007), ‘Customer response to RTP in competitive markets: A study of Niagara Mohawk's standard offer tariff’, The Energy Journal, 28(1).CrossRefGoogle Scholar
Cappers, P., Spurlock, A., Todd, A., Baylis, P., Fowlie, M. and Wolfram, C. (2016), ‘Time-of-use as a Default Rate for Residential Customers: Issues and Insights’. https://emp.lbl.gov/sites/all/files/lbnl-1005704_0.pdf Google Scholar
Choi, J., Laibson, D., Madrian, B. and Metrick, A. (2004), ‘For better or for worse: Default effects and 401(k) savings behavior’, Perspectives on the Economics of Aging, University of Chicago Press, 81–126.CrossRefGoogle Scholar
Dinner, I., Goldstein, D., Johnson, E. and Liu, K. (2011), ‘Partitioning default effects: Why people choose not to choose’, Journal of Experimental Psychology: Applied 17(4): 332–341.Google ScholarPubMed
DOE (2016), ‘Recovery Act Smart Grid Programs’. https://www.smartgrid.gov/recovery_act/ Google Scholar
Dyson, M., Mandel, J., et al. ‘The Economics of Demand Flexibility: How ‘flexiwatts’ create quantifiable value for customers and the grid’, Rocky Mountain Institute, August 2015. http://www.rmi.org/electricity_demand_flexibility Google Scholar
Ebeling, F. and Lotz, S. (2015), ‘Domestic uptake of green energy promoted by opt-out tariffs’, Nature Climate Change, 5, 868–871.CrossRefGoogle Scholar
EIA (2016), ‘Electric power sales, revenue, and energy efficiency form EIA-861 detail data files’, January 2016 (data from 2014). https://www.eia.gov/electricity/data/eia861/ Google Scholar
ERCOT (2015), ‘48 Hour Real Time Gen and Load Data Reports 2014–2015’, ERCOT Market Information, 2015.Google Scholar
Faruqui, A. (2016), ‘Residential Demand Charges: An Overview’, The Brattle Group. http://www.brattle.com/system/publications/pdfs/000/005/276/original/Residential_Demand_Charges_An_Overview.pdf?1458061233 Google Scholar
Faruqui, A., Hledik, R. and Lessem, N. (2014), ‘Smart by Default’, Public Utilities Fortnightly, pp. 24–32. Retrieved from http://www.fortnightly.com/fortnightly/2014/08/smart-default Google Scholar
Faruqui, A. and Palmer, J. (2012), ‘The Discovery of Price Responsiveness – A Survey of Experiments Involving Dynamic Pricing of Electricity’, Social Science Research Network. http://ssrn.com/abstract=2020587 Google Scholar
Flaim, T., Robinson, J., Holmes, C. and Neehan, B. (2011), ‘A system for understanding retail electric rate structures’, Electric Power Research Institute (EPRI). http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000000001021962 Google Scholar
Girouard, C. (2015), ‘Time varying rates: an idea whose time has come?’, Advanced Energy Economy. http://blog.aee.net/time-varying-rates-an-idea-whose-time-has-come Google Scholar
Hogan, W. (2014), ‘Time-of-Use Rates and Real-Time Prices’, https://www.hks.harvard.edu/fs/whogan/Hogan_TOU_RTP_Newark_082314.pdf Google Scholar
Joskow, P. (2012), ‘Creating a Smarter U.S. Electricity Grid’, The Journal of Economic Perspectives 26(1): 29–48.CrossRefGoogle Scholar
Kressel, L. and Chapman, G. (2007), ‘The default effect in end-of-life medical treatment preferences’, Medical Decision Making, 27(3): 299–310.CrossRefGoogle ScholarPubMed
Madrian, B. and Shea, D. (2001), ‘The power of suggestion: inertia in 401(k) participation and savings behavior’, The Quarterly Journal of Economics, 116(4): 1149–1187.CrossRefGoogle Scholar
Morey, M. and Kirsch, L. (2016), ‘Retail Choice in Electricity: What Have We Learned in 20 Years?’, https://www.hks.harvard.edu/hepg/Papers/2016/Retail%20Choice%20in%20Electricity%20for%20EMRF%20Final.pdf Google Scholar
Mullainathan, S., Schwartzstein, J. and Congdon, W. (2012), ‘A reduced-form approach to behavioral public finance’, The Annual Review of Economics. http://www.dartmouth.edu/~jschwartzstein/papers/are.pdf Google Scholar
Park, C., Jun, S. and MacInnis, D. (2000), ‘Choosing what I want versus rejecting what I do not want: An application of decision framing to product option choice decisions’, Journal of Marketing Research, 37(2): 187–202.CrossRefGoogle Scholar
Patrick, R. and Wolak, F. (2001), ‘Estimating the customer-level demand for electricity under real-time market prices’, NBER Working Paper No. 8213.Google Scholar
Pichert, D. and Katsikopoulos, K. (2007), ‘Green defaults: Information presentation and proenvironmental behaviour’, Journal of Environmental Psychology, 28(1): 63–73.CrossRefGoogle Scholar
Pollitt, M. and Shaorshadze, I. (2011), ‘The Role of Behavioral Economics in Energy and Climate Policy’, ESRC Electricity Policy Research Group. http://www.old.cambridgeeprg.com/wp-content/uploads/2012/01/EPRG1130_Main.pdf Google Scholar
Robinson, J. and Flaim, T. (2014), ‘Understanding Electric Utility Customers – 2014 Update’, EPRI. http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?productId=000000003002001268 Google Scholar
Robinson, J., Neenan, B., Flaim, T. and Bosivert, R. (2012), ‘Understanding electric utility customers - summary report. What we know and what we need to know’, EPRI.Google Scholar
Sunstein, C. (2015), Choosing Not to Choose. Oxford: Oxford University Press.Google ScholarPubMed
Taylor, T., Schwartz, P. and Cochell, J. (2005), ‘24/7 hourly response to electricity real-time pricing with up to eight summers of experience’, Journal of Regulatory Economics, 27(3): 235.CrossRefGoogle Scholar
Tweed, K. (2016), ‘What Will Drive Investment in the Next 60 Million Smart Meters?’, Greentech Media, November 2015. http://www.greentechmedia.com/articles/read/what-will-drive-the-next-wave-of-smart-meters Google Scholar

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 242
Total number of PDF views: 827 *
View data table for this chart

* Views captured on Cambridge Core between 06th October 2017 - 16th April 2021. This data will be updated every 24 hours.

12
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Behavioral considerations for effective time-varying electricity prices
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Behavioral considerations for effective time-varying electricity prices
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Behavioral considerations for effective time-varying electricity prices
Available formats
×
×

Reply to: Submit a response

Your details

Conflicting interests

Do you have any conflicting interests? *