Skip to main content
Log in

A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup

  • Research Article
  • Published:
Journal of Revenue and Pricing Management Aims and scope

Abstract

Dynamic pricing with automation and option to sell in-house-generated renewable energy to the grid attract consumers to the future retail electricity market. Real-time pricing of electricity in the Indian context has not been relevant so far because the pricing in Indian urban retail electricity market is mostly restricted to block-rate pricing. This study describes a novel method for segmenting the retail electricity market in a university setup. The experiment was focused on differential pricing, and the data were collected from 173 respondents using a questionnaire. This study prescribes different criteria for classifying a consumer into the specified market segments using cluster analysis and discriminant analysis. The demand–price relationship and price elasticity are studied for each segment. Demand characteristics of four appliances are also discussed. The study found that in some cases, consumers’ willingness to pay is about five times higher than the present average price.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • An, L., F. Lupi, J. Liu, M. Linderman, and J. Huang. 2002. Modeling the choice to switch from fuelwood to electricity. Ecological Economics. 42 (3): 445–457.

    Article  Google Scholar 

  • Anjos, M.F., and J.A. Gómez. 2017. Operations research approaches for building demand response in a smart grid. In Leading developments from INFORMS communities, ed. R. Batta, J. Peng, J.C. Smith, and H.J. Greenberg, 131–152. Catonsville: INFORMS.

    Google Scholar 

  • Bose, R., and M. Shukla. 1999. Elasticities of electricity demand in India. Energy Policy 27 (3): 137–146.

    Article  Google Scholar 

  • Charles River Associates. 2005. Applications of dynamic pricing in developing and emerging economies. Geneva: The World Bank.

    Google Scholar 

  • Desai, K.R., and G. Dutta. 2013. A dynamic pricing approach to electricity prices in the Indian context. International Journal of Revenue Management 7 (3/4): 268–288.

    Article  Google Scholar 

  • Devicienti, F., K. Irina, and P. Stefano. 2004. Willingness to pay for water and energy: An introductory guide to contingent valuation and coping cost techniques. Energy and Mining Sector Board, 3 Newsletter. The World Bank.

  • Dütschke, E., and A. Paetz. 2013. Dynamic electricity pricing—Which programs do consumers prefer? Energy Policy 59: 226–234.

    Article  Google Scholar 

  • Dutta, G., and K. Mitra. 2017. A literature review on dynamic pricing of electricity. Journal of the Operational Research Society 68 (10): 1131–1145.

    Article  Google Scholar 

  • Faruqui, A., and S. Sergici. 2013. Arcturus: International evidence on dynamic pricing. The Electricity Journal 26 (7): 55–65.

    Article  Google Scholar 

  • Faruqui, A., S. Sergici, and L. Akaba. 2014. The impact of dynamic pricing on residential and small commercial and industrial usage: New experimental evidence from Connecticut. The Energy Journal 35 (1): 137–160.

    Article  Google Scholar 

  • Faruqui, A., S. Sergici, and L. Wood. 2009. Moving toward utility-scale deployment of dynamic pricing in mass markets. IEE Whitepaper, June.

  • Filippini, M., and S. Pachauri. 2004. Elasticities of electricity demand in urban Indian households. Energy Policy. 32 (3): 429–436.

    Article  Google Scholar 

  • Ifland, M., N. Exner, N. Doring, and D. Westermann. 2012. Influencing domestic customers' market behavior with time flexible tariffs. 3rd IEEE pes innovative smart grid technologies Europe (ISGT Europe) (Berlin), pp. 1–7.

  • Kirschen, D.S. 2003. Demand-side view of electricity markets. IEEE Transactions on Power Systems. 18 (2): 520–527.

    Article  Google Scholar 

  • Kirschen, D.S., G. Strbac, P. Cumperayot, and D. de Paiva Mendes. 2000. Factoring the elasticity of demand in electricity prices. IEEE Transactions on Power Systems. 15 (2): 612–617.

    Article  Google Scholar 

  • Letzler, R.J. 2007. Implementing opt-in, residential, dynamic electricity pricing: Insights from economics and psychology. Doctoral dissertation, University of California, Berkeley.

  • Livengood, D., and R. Larson. 2009. The energy box: Locally automated optimal control of residential electricity usage. Service Science 1 (1): 1–16.

    Article  Google Scholar 

  • Mak, J., and B. Chapman. 1993. A survey of current real-time pricing programs. The Electricity Journal 6 (7): 76–77.

    Article  Google Scholar 

  • Mitra, K., and G. Dutta. 2018. A two-part dynamic pricing policy for household electricity consumption scheduling with minimized expenditure. International Journal of Electrical Power & Energy Systems 100: 29–41.

    Article  Google Scholar 

  • Ozbafli, A., and G. Jenkins. 2016. Estimating willingness to pay for reliable electricity supply: A choice experiment study. Energy Economics. 56: 443–452.

    Article  Google Scholar 

  • Roozbehani, M., M.A. Dahleh, and S.K. Mitter. 2012. Volatility of power grids under real-time pricing. IEEE Transactions on Power Systems 27 (4): 1926–1940.

    Article  Google Scholar 

  • Thimmapuram, P.R., and J. Kim. 2013. Consumers’ price elasticity of demand modeling with economic effects on electricity markets using an agent-based model. IEEE Transactions on Smart Grid 4 (1): 390–397.

    Article  Google Scholar 

  • Tiwari, P. 2000. Architectural, demographic, and economic causes of electricity consumption in Bombay. Journal of Policy Modeling 22 (1): 81–98.

    Article  Google Scholar 

  • Twerefou, D. 2014. Willingness to pay for improved electricity supply in Ghana. Modern Economy 05 (05): 489–498.

    Article  Google Scholar 

  • Wolak, F.A. 2011. Do residential customers respond to hourly prices? Evidence from a dynamic pricing experiment. The American Economic Review 101 (3): 83–87.

    Article  Google Scholar 

  • Zhou, S., and F. Teng. 2013. Estimation of urban residential electricity demand in China using household survey data. Energy Policy 61: 394–402.

    Article  Google Scholar 

Download references

Acknowledgements

We sincerely thank Shivani Singh for her efforts in data collection. This work is supported by the fellowship from the University Grants Commission, India and by Research and Publications Committee of Indian Institute of Management, Ahmedabad.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krishnendranath Mitra.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mitra, K., Dutta, G. A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup. J Revenue Pricing Manag 20, 162–184 (2021). https://doi.org/10.1057/s41272-021-00298-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41272-021-00298-y

Keywords

Navigation