The diffusion of smart metering technology and intermittent renewable electricity generation capacity makes the deployment of time-varying electricity rates increasingly feasible and important to the functioning of electricity grids. Such rates, which economists advocate to more efficiently match supply and demand, remain rare, though experiments assessing consumer responses are not. This review synthesizes evaluations of these experiments in the context of a theory of consumer inattention and adjustment costs that posits a role for automation technology to boost the short-run price elasticity of demand and affect demand-side reductions that can lower generation costs.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Aigner D. 1985. The residential electricity time-of-use pricing experiments: What have we learned?. Social Experimentation JA Hausman, DA Wise 11–54 Chicago: Univ. Chicago Press [Google Scholar]
  2. Aigner DJ, Ghali K. 1989. Self-selection in the residential electricity time-of-use pricing experiments. J. Appl. Econ. 4:Suppl. 1131–44 [Google Scholar]
  3. Aigner DJ, Hausman JA. 1980. Correcting for truncation bias in the analysis of experiments in time-of-day pricing of electricity. Bell J. Econ. 11:1131–42 [Google Scholar]
  4. Allcott H. 2011a. Consumers’ perceptions and misperceptions of energy costs. Am. Econ. Rev. 101:398–104 [Google Scholar]
  5. Allcott H. 2011b. Rethinking real-time electricity pricing. Resour. Energy Econ. 33:4820–42 [Google Scholar]
  6. Bhargava S, Manoli D. 2012. Why are benefits left on the table? Assessing the role of information, complexity, and stigma on take-up with an IRS field experiment. Adv. Consumer Res. 40:298–302 [Google Scholar]
  7. Boiteux M. 1960. Peak-load pricing. J. Bus. 33:2157–79 [Google Scholar]
  8. Boiteux M. 1964. Marginal cost pricing. Marginal Cost Pricing in Practice JR Nelson 51–58 Englewood, NJ: Prentice Hall [Google Scholar]
  9. Bollinger B, Hartmann WR. 2016. Welfare effects of home automation technology with dynamic pricing Work. Pap. Fuqua Sch. Bus., Duke Univ. Durham, NC: http://www.bryanbollinger.com/index_files/HomeAutomation.pdf
  10. Bollinger B, Leslie P, Sorensen A. 2011. Calorie posting in chain restaurants. Am. Econ. J. Econ. Policy 3:191–128 [Google Scholar]
  11. Borenstein S. 2002. The trouble with electricity markets: understanding California's restructuring disaster. J. Econ. Perspect. 16:1191–211 [Google Scholar]
  12. Borenstein S. 2005a. The long-run efficiency of real-time electricity pricing. Energy J 26:393–116 [Google Scholar]
  13. Borenstein S. 2005b. Time-varying retail electricity prices: theory and practice. Electricity Deregulation: Choices and Challenges J Griffin, SL Puller 317–58 Chicago: Univ. Chicago Press [Google Scholar]
  14. Borenstein S. 2009. Electricity pricing that reflects its real-time cost. NBER Reporter 1: http://www.nber.org/reporter/2009number1/borenstein.html [Google Scholar]
  15. Borenstein S. 2012. The private and public economics of renewable electricity generation. J. Econ. Perspect. 26:167–92 [Google Scholar]
  16. Borenstein S, Bushnell J. 2015. The US electricity industry after 20 years of restructuring. Annu. Rev. Econ. 7:437–63 [Google Scholar]
  17. Borenstein S, Holland S. 2005. On the efficiency of competitive markets with time-invariant retail prices. RAND J. Econ. 36:3469–93 [Google Scholar]
  18. Borenstein S, Jaske M, Rosenfeld A. 2002. Dynamic pricing, advanced metering, and demand response in electricity markets Work. Pap. 105 Cent. Study Energy Mark., Univ. Calif. Berkeley:
  19. Braithwait S. 2000. Residential TOU price response in the presence of interactive communication equipment. Pricing in Competitive Electricity Markets A Faruqui, K Eakin 359–73 Berlin: Springer Sci. Bus. Media [Google Scholar]
  20. Brown A, Chua ZE, Camerer CF. 2009. Learning and visceral temptation in dynamic savings experiments. Q. J. Econ. 124:1197–231 [Google Scholar]
  21. Brown J, Hossain T, Morgan J. 2010. Shrouded attributes and information suppression: evidence from the field. Q. J. Econ. 125:2859–76 [Google Scholar]
  22. Busse M, Silva-Risso J, Zettelmeyer F. 2006. $1,000 cash back: the pass-through of auto manufacturer promotions. Am. Econ. Rev. 96:41253–70 [Google Scholar]
  23. Cappers P, Fowlie M, Spurlock CA, Todd A, Wolfram C, Baylis P. 2015. Default effects, follow-on behavior and welfare in residential electricity pricing programs Work. Pap. NBER Summer Inst. Cambridge, Mass: http://conference.nber.org/confer/2015/SI2015/EEE/Cappers_Fowlie_Spurlock_Todd_Wolfram_Baylis.pdf
  24. Chetty R, Looney A, Kroft K. 2009. Salience and taxation: theory and evidence. Am. Econ. Rev. 99:41145–77 [Google Scholar]
  25. Chetty R, Saez E. 2005. Dividend taxes and corporate behavior: evidence from the 2003 dividend tax cut. Q. J. Econ. 120:3791–833 [Google Scholar]
  26. Cho S, Rust J. 2010. The flat rental puzzle. Rev. Econ. Stud. 77:2534–59 [Google Scholar]
  27. Choi JJ, Laibson D, Madrian BC, Metrick A. 2004. For better or for worse: default effects and 401(k) savings behavior. Perspectives on the Economics of Aging DA Wise 81–126 Chicago: Univ. Chicago Press [Google Scholar]
  28. Clark JM. 1911. Rates for public utilities. Am. Econ. Rev. 1:3473–87 [Google Scholar]
  29. Cooper A. 2016. Electric company smart meter deployments: foundation for a smart grid Rep., Inst. Elect. Innov., Edison Found. Washington, DC: http://www.edisonfoundation.net/iei/publications/Documents/Final%20Electric%20Company%20Smart%20Meter%20Deployments-%20Foundation%20for%20A%20Smart%20Energy%20Grid.pdf
  30. DellaVigna S. 2009. Psychology and economics: evidence from the field. J. Econ. Lit. 47:2315–72 [Google Scholar]
  31. DellaVigna S, Pollet JM. 2009. Investor inattention and Friday earnings announcements. J. Finance 64:2709–49 [Google Scholar]
  32. Duflo E, Saez E. 2003. The role of information and social interactions in retirement plan decisions: evidence from a randomized experiment. Q. J. Econ. 118:3815–42 [Google Scholar]
  33. Eisenmenger HE. 1921. Central Station Rates in Theory and Practice Chicago: FJ Drake & Co.
  34. Faruqui A, George S. 2005. Quantifying customer response to dynamic pricing. Electr. J. 18:453–63 [Google Scholar]
  35. Faruqui A, Hledik R, Tsoukalis J. 2009. The power of dynamic pricing. Electr. J. 22:342–56 [Google Scholar]
  36. Faruqui A, Sergici S. 2010. Household response to dynamic pricing of electricity: a survey of 15 experiments. J. Regul. Econ. 38:2193–225 [Google Scholar]
  37. Faruqui A, Sergici S. 2011. Dynamic pricing of electricity in the mid-Atlantic region: econometric results from the Baltimore gas and electric company experiment. J. Regul. Econ. 40:182–109 [Google Scholar]
  38. Faruqui A, Sergici S. 2013. Arcturus: international evidence on dynamic pricing. Electr. J. 26:755–65 [Google Scholar]
  39. Faruqui A, Sergici S, Akaba L. 2012. Dynamic pricing in a moderate climate: the evidence from Connecticut SSRN Work. Pap. 2028178 Rochester, NY:
  40. Faruqui A, Sergici S, Akaba L. 2013. Dynamic pricing of electricity for residential customers: the evidence from Michigan. Energy Effic 6:3571–84 [Google Scholar]
  41. Faruqui A, Sergici S, Sharif A. 2010. The impact of informational feedback on energy consumption—a survey of the experimental evidence. Energy 35:41598–608 [Google Scholar]
  42. FERC (US Fed. Energy Reg. Comm.). 2011. 2010 assessment of demand response and advanced metering Staff Rep. FERC Washington, DC: http://www.ferc.gov/legal/staff-reports/2010-dr-report.pdf
  43. FERC (US Fed. Energy Reg. Comm.). 2015. 2015 assessment of demand response and advanced metering Staff Rep. FERC Washington, DC: https://www.ferc.gov/legal/staff-reports/2015/demand-response.pdf
  44. Gallagher KS, Muehlegger E. 2011. Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology. J. Environ. Econ. Manag. 61:11–15 [Google Scholar]
  45. Gilbert B, Graff Zivin JS. 2014. Dynamic salience with intermittent billing: evidence from smart electricity meters. J. Econ. Behav. Organ. 107:176–90 [Google Scholar]
  46. Grubb MD, Osborne M. 2015. Cellular service demand: biased beliefs, learning, and bill shock. Am. Econ. Rev. 105:1234–71 [Google Scholar]
  47. Harding M, Lamarche C. 2016. Empowering consumers through data and smart technology: experimental evidence on the consequences of time‐of‐use electricity pricing policies. J. Policy Anal. Manag. 35:4906–31 [Google Scholar]
  48. Hausman WJ, Neufeld JL. 1984. Time-of-day pricing in the U.S. electric power industry at the turn of the century. RAND J. Econ. 15:1116–26 [Google Scholar]
  49. Herter K. 2007. Residential implementation of critical-peak pricing of electricity. Energy Policy 35:42121–30 [Google Scholar]
  50. Herter K, Wayland S. 2010. Residential response to critical-peak pricing of electricity: California evidence. Energy 35:41561–67 [Google Scholar]
  51. Hogan WW. 1999. Getting the prices right in PJM: analysis and summary: April 1998 through March 1999 Work. Pap. Harvard Univ. Cambridge, Mass: https://www.hks.harvard.edu/fs/whogan/pjm0399.pdf
  52. Hossain T, Morgan J. 2006. Plus shipping and handling. Adv. Econ. Anal. Policy 6:1–27 [Google Scholar]
  53. Houser D, Keane M, McCabe K. 2004. Behavior in a dynamic decision problem: an analysis of experimental Bayesian type classification algorithm. Econometrica 72:3781–822 [Google Scholar]
  54. Houthakker HS. 1951. Electricity tariffs in theory and practice. Econ. J. 61:2411–25 [Google Scholar]
  55. Ito K. 2014. Do consumers respond to marginal or average price? Evidence from nonlinear electricity pricing. Am. Econ. Rev. 104:2537–63 [Google Scholar]
  56. Jessoe K, Rapson D. 2014. Knowledge is (less) power: experimental evidence from residential energy use. Am. Econ. Rev. 104:41417–38 [Google Scholar]
  57. Johnson EJ, Goldstein D. 2003. Do defaults save lives. Science 302:56491338–39 [Google Scholar]
  58. Joskow PL. 2001. California's electricity crisis. Oxf. Rev. Econ. Policy 17:3365–88 [Google Scholar]
  59. Joskow PL. 2012. Creating a smarter US electricity grid. J. Econ. Perspect. 26:129–47 [Google Scholar]
  60. Kahneman D. 2003. Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93:51449–75 [Google Scholar]
  61. Kahneman D, Knetsch JL, Thaler RH. 1991. Anomalies: the endowment effect, loss aversion, and status quo bias. J. Econ. Perspect. 5:1193–206 [Google Scholar]
  62. Krauss C, Cardwell D. 2015. A Texas utility offers a nighttime special: free electricity. New York Times Nov. 8. http://www.nytimes.com/2015/11/09/business/energy-environment/a-texas-utility-offers-a-nighttime-special-free-electricity.html
  63. Letzler RJ. 2007. Implementing opt-in, residential, dynamic electricity pricing: insights from economics and psychology PhD Thesis Univ. Calif. Berkeley:
  64. Madrian BC, Shea DF. 2001. The power of suggestion: inertia in 401(k) participation and savings behavior. Q. J. Econ. 116:41149–87 [Google Scholar]
  65. Malik N. 2016. A thunderstorm just drove New York power prices above $1,000. Bloomberg July 5. https://www.bloomberg.com/news/articles/2016-07-25/a-thunderstorm-just-drove-new-york-power-prices-above-1-000
  66. Malik N, Weber H. 2016. One thing California, Texas have in common is negative power. Bloomberg Apr. 5. https://www.bloomberg.com/news/articles/2016-04-05/one-thing-california-texas-have-in-common-is-negative-power
  67. Miedema AK, White SB. 1980. Time-of-use electricity price effects: summary I Rep. DOE/RG/08684-T6 Res. Triangle Inst., Res Triangle Park, NC:
  68. Mitchell B, Acton J. 1980. The effect of time-of-use rates in the Los Angeles electricity study Rep. N-1533-DWP/HF RAND Santa Monica, Calif: http://www.rand.org/content/dam/rand/pubs/notes/2007/N1533.pdf
  69. PJM Interconnect. 2015. State of the market Rep., Monit. Anal. Eagleville, Pa: http://www.monitoringanalytics.com/reports/PJM_State_of_the_Market/2015.shtml
  70. Price MK. 2014. Using field experiments to address environmental externalities and resource scarcity: major lessons learned and new directions for future research. Oxf. Rev. Econ. Policy 30:4621–38 [Google Scholar]
  71. Reiss PC, White MW. 2005. Household electricity demand, revisited. Rev. Econ. Stud. 72:3853–83 [Google Scholar]
  72. Sexton S. 2015. Automatic bill payment and salience effects: evidence from electricity consumption. Rev. Econ. Stat. 97:2229–41 [Google Scholar]
  73. Shin J-S. 1985. Perception of price when price information is costly: evidence from residential electricity demand. Rev. Econ. Stat. 67:4591–98 [Google Scholar]
  74. Simon HA. 1955. A behavioral model of rational choice. Q. J. Econ. 69:99–118 [Google Scholar]
  75. Steiner PO. 1957. Peak loads and efficient pricing. Q. J. Econ. 71:4585–610 [Google Scholar]
  76. Stigler GJ. 1961. The economics of information. J. Polit. Econ. 69:3213–25 [Google Scholar]
  77. Taylor TN, Schwarz PM, Cochell JE. 2005. 24/7 hourly response to electricity real-time pricing with up to eight summers of experience. J. Regul. Econ. 27:3235–62 [Google Scholar]
  78. Thaler RH, Sunstein CR. 2003. Libertarian paternalism. Am. Econ. Rev. 93:2175–79 [Google Scholar]
  79. Train K, Mehrez G. 1994. Optional time-of-use prices for electricity: econometric analysis of surplus and Pareto impacts. RAND J. Econ. 25:2263–83 [Google Scholar]
  80. Turvey R. 1968. Peak load pricing. J. Polit. Econ. 76:1101–13 [Google Scholar]
  81. Tversky A, Kahneman D. 1974. Judgment under uncertainty: heuristics and biases. Science 185:41571124–31 [Google Scholar]
  82. Tversky A, Kahneman D. 1991. Loss aversion in riskless choice: a reference-dependent model. Q. J. Econ. 106:41039–61 [Google Scholar]
  83. Wald ML. 2014. Power savings of smart meters prove slow to materialize. New York Times Dec. 6. http://www.nytimes.com/2014/12/06/business/energy-environment/power-savings-of-smart-meters-prove-slow-to-materialize.html?_r=0
  84. Watkins GP. 1915. A third factor in the variation of productivity: the load factor. Am. Econ. Rev. 5:4753–86 [Google Scholar]
  85. Watkins GP. 1916. The theory of differential rates. Q. J. Econ. 30:4682–703 [Google Scholar]
  86. Wattles J. 2016. New York City electricity price spikes 1000%. CNN.com July 25. http://money.cnn.com/2016/07/25/news/companies/new-york-energy-prices-storm/
  87. Weightman G. 2011. Children of Light: How Electricity Changed Britain Forever London: Atlantic
  88. Wolak FA. 2010. An experimental comparison of critical peak and hourly pricing: the PowerCentsDC Program Work. Pap. Stanford Univ. Stanford, Calif: http://www.stanford.edu/group/fwolak/cgi-bin/sites/default/files/files/An%20Experimental%20Comparison%20of%20Critical%20Peak%20and%20Hourly%20Pricing_March%202010_Wolak.pdf
  89. Wolak FA. 2011. Measuring the benefits of greater spatial granularity in short-term pricing in wholesale electricity markets. Am. Econ. Rev. 101:3247–52 [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error