IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v68y2017i10d10.1057_s41274-016-0149-4.html
   My bibliography  Save this article

A literature review on dynamic pricing of electricity

Author

Listed:
  • Goutam Dutta

    (Indian Institute of Management)

  • Krishnendranath Mitra

    (University of Calcutta)

Abstract

Revenue management and dynamic pricing are concepts that have immense possibilities for application in the energy sector. Both can be considered as demand-side management tools that can facilitate the offering of different prices at different demand levels. This paper studies literature on various topics related to the dynamic pricing of electricity and lists future research avenues in pricing policies, consumers’ willingness to pay and market segmentation in this field. Demand and price forecasting play an important role in determining prices and scheduling load in dynamic pricing environments. This allows different forms of dynamic pricing policies to different markets and customers depending on customers’ willingness to pay. Consumers’ willingness to pay for electricity services is also necessary in setting price limits depending on the demand and demand response curve. Market segmentation can enhance the effects of such pricing schemes. Appropriate scheduling of electrical load enhances the consumer response to dynamic tariffs.

Suggested Citation

  • Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    • Handle: RePEc:pal:jorsoc:v:68:y:2017:i:10:d:10.1057_s41274-016-0149-4
    DOI: 10.1057/s41274-016-0149-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41274-016-0149-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41274-016-0149-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bose, Ranjan Kumar & Shukla, Megha, 1999. "Elasticities of electricity demand in India," Energy Policy, Elsevier, vol. 27(3), pages 137-146, March.
    2. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
    3. Wang, Chengshan & Zhou, Yue & Wang, Jidong & Peng, Peiyuan, 2013. "A novel Traversal-and-Pruning algorithm for household load scheduling," Applied Energy, Elsevier, vol. 102(C), pages 1430-1438.
    4. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    5. Severin Borenstein, 2007. "Wealth Transfers Among Large Customers from Implementing Real-Time Retail Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 131-150.
    6. Filippini, Massimo & Pachauri, Shonali, 2004. "Elasticities of electricity demand in urban Indian households," Energy Policy, Elsevier, vol. 32(3), pages 429-436, February.
    7. Stephen P. Holland & Erin T. Mansur, 2008. "Is Real-Time Pricing Green? The Environmental Impacts of Electricity Demand Variance," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 550-561, August.
    8. Faruqui, Ahmad, 2010. "The Ethics of Dynamic Pricing," The Electricity Journal, Elsevier, vol. 23(6), pages 13-27, July.
    9. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    10. Hledik, Ryan, 2009. "How Green Is the Smart Grid?," The Electricity Journal, Elsevier, vol. 22(3), pages 29-41, April.
    11. Hyland, Marie & Leahy, Eimear & Tol, Richard S.J., 2013. "The potential for segmentation of the retail market for electricity in Ireland," Energy Policy, Elsevier, vol. 61(C), pages 349-359.
    12. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    13. Severin Borenstein & Stephen Holland, 2005. "On the Efficiency of Competitive Electricity Markets with Time-Invariant Retail Prices," RAND Journal of Economics, The RAND Corporation, vol. 36(3), pages 469-493, Autumn.
    14. Mak, Juliet C. & Chapman, Bruce R., 1993. "A survey of current real-time pricing programs," The Electricity Journal, Elsevier, vol. 6(7), pages 76-77.
    15. Ozbafli, Aygul & Jenkins, Glenn P., 2016. "Estimating the willingness to pay for reliable electricity supply: A choice experiment study," Energy Economics, Elsevier, vol. 56(C), pages 443-452.
    16. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    17. Paul Simshauser and David Downer, 2016. "On the Inequity of Flat-rate Electricity Tariffs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    18. Desai, Kinnary R. & Dutta, Goutam, 2013. "A Dynamic Pricing Approach on Electricity Prices in Indian Context," IIMA Working Papers WP2013-12-07, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Paul L. Joskow & Catherine D. Wolfram, 2012. "Dynamic Pricing of Electricity," American Economic Review, American Economic Association, vol. 102(3), pages 381-385, May.
    20. Costello, Ken, 2004. "An Observation on Real-Time Pricing: Why Practice Lags Theory," The Electricity Journal, Elsevier, vol. 17(1), pages 21-25.
    21. Mirza, Faisal Mehmood & Bergland, Olvar, 2012. "Pass-through of wholesale price to the end user retail price in the Norwegian electricity market," Energy Economics, Elsevier, vol. 34(6), pages 2003-2012.
    22. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    23. Frank A. Wolak, 2011. "Do Residential Customers Respond to Hourly Prices? Evidence from a Dynamic Pricing Experiment," American Economic Review, American Economic Association, vol. 101(3), pages 83-87, May.
    24. Giulietti, Monica & Grossi, Luigi & Waterson, Michael, 2010. "Price transmission in the UK electricity market: Was NETA beneficial?," Energy Economics, Elsevier, vol. 32(5), pages 1165-1174, September.
    25. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    26. S. Borenstein, 2013. "Effective and Equitable Adoption of Opt-In Residential Dynamic Electricity Pricing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 42(2), pages 127-160, March.
    27. Ahmad Faruqui, Sanem Sergici, and Lamine Akaba, 2014. "The Impact of Dynamic Pricing on Residential and Small Commercial and Industrial Usage: New Experimental Evidence from Connecticut," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    28. Tiwari, Piyush, 2000. "Architectural, Demographic, and Economic Causes of Electricity Consumption in Bombay," Journal of Policy Modeling, Elsevier, vol. 22(1), pages 81-98, January.
    29. Gerpott, Torsten J. & Paukert, Mathias, 2013. "Determinants of willingness to pay for smart meters: An empirical analysis of household customers in Germany," Energy Policy, Elsevier, vol. 61(C), pages 483-495.
    30. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    31. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Krishnendranath Mitra & Goutam Dutta, 2021. "A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 162-184, April.
    3. Mitra, Krishnendranath & Dutta, Goutam, 2018. "Study of Retail Electricity Consumers’ Response and Perception Regarding Electricity Consumption," IIMA Working Papers WP 2018-10-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    4. Ahmed Ismail & Mustafa Baysal, 2023. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning," Energies, MDPI, vol. 16(14), pages 1-19, July.
    5. Aftab Ahmed Almani & Xueshan Han, 2023. "Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning," Energies, MDPI, vol. 16(5), pages 1-13, March.
    6. Fabra, Natalia, 2021. "The energy transition: An industrial economics perspective," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    7. Strong, Derek Ryan, 2017. "The Early Diffusion of Smart Meters in the US Electric Power Industry," Thesis Commons 7zprk, Center for Open Science.
    8. Freier, Julia & von Loessl, Victor, 2022. "Dynamic electricity tariffs: Designing reasonable pricing schemes for private households," Energy Economics, Elsevier, vol. 112(C).
    9. Krishnamurthy, Chandra Kiran B. & Vesterberg, Mattias & Böök, Herman & Lindfors, Anders V. & Svento, Rauli, 2018. "Real-time pricing revisited: Demand flexibility in the presence of micro-generation," Energy Policy, Elsevier, vol. 123(C), pages 642-658.
    10. Simshauser, Paul, 2018. "Price discrimination and the modes of failure in deregulated retail electricity markets," Energy Economics, Elsevier, vol. 75(C), pages 54-70.
    11. Biggar, Darryl R. & Hesamzadeh, Mohammad Reza, 2024. "Optimal retail contracts with contractible uncertainty," Energy Economics, Elsevier, vol. 136(C).
    12. Enrich, Jacint & Li, Ruoyi & Mizrahi, Alejandro & Reguant, Mar, 2024. "Measuring the impact of time-of-use pricing on electricity consumption: Evidence from Spain," Journal of Environmental Economics and Management, Elsevier, vol. 123(C).
    13. Weide Li & Demeng Kong & Jinran Wu, 2017. "A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(5), pages 1-16, May.
    14. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    15. Anette Boom & Sebastian Schwenen, 2021. "Is real-time pricing smart for consumers?," Journal of Regulatory Economics, Springer, vol. 60(2), pages 193-213, December.
    16. Tanrisever, Fehmi & Derinkuyu, Kursad & Heeren, Michael, 2013. "Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case," Energy, Elsevier, vol. 58(C), pages 247-257.
    17. Blonz, Joshua & Palmer, Karen & Wichman, Casey & Wietelman, Derek C., 2021. "Smart Thermostats, Automation, and Time-Varying Prices," RFF Working Paper Series 21-20, Resources for the Future.
    18. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
    19. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    20. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:68:y:2017:i:10:d:10.1057_s41274-016-0149-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.