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Option value of electricity demand response


  • Sezgen, Osman
  • Goldman, C.A.
  • Krishnarao, P.


As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.

Suggested Citation

  • Sezgen, Osman & Goldman, C.A. & Krishnarao, P., 2007. "Option value of electricity demand response," Energy, Elsevier, vol. 32(2), pages 108-119.
  • Handle: RePEc:eee:energy:v:32:y:2007:i:2:p:108-119
    DOI: 10.1016/

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    References listed on IDEAS

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    Cited by:

    1. Moore, J. & Woo, C.K. & Horii, B. & Price, S. & Olson, A., 2010. "Estimating the option value of a non-firm electricity tariff," Energy, Elsevier, vol. 35(4), pages 1609-1614.
    2. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    3. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Distributed multi-generation: A comprehensive view," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 535-551, April.
    4. Praktiknjo, Aaron J., 2014. "Stated preferences based estimation of power interruption costs in private households: An example from Germany," Energy, Elsevier, vol. 76(C), pages 82-90.
    5. Lopes, Rafael F. & Costa, Fabiano F. & Oliveira, Aurenice & de C. Lima, Antonio Cezar, 2018. "Algorithm based on particle swarm applied to electrical load scheduling in an industrial setting," Energy, Elsevier, vol. 147(C), pages 1007-1015.
    6. Biegel, Benjamin & Hansen, Lars Henrik & Stoustrup, Jakob & Andersen, Palle & Harbo, Silas, 2014. "Value of flexible consumption in the electricity markets," Energy, Elsevier, vol. 66(C), pages 354-362.
    7. Bertolini, Marina & D'Alpaos, Chiara & Moretto, Michele, 2018. "Do Smart Grids boost investments in domestic PV plants? Evidence from the Italian electricity market," Energy, Elsevier, vol. 149(C), pages 890-902.
    8. Jun, Eunju & Kim, Wonjoon & Chang, Soon Heung, 2009. "The analysis of security cost for different energy sources," Applied Energy, Elsevier, vol. 86(10), pages 1894-1901, October.
    9. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    10. Won, Chaehwan, 2009. "Valuation of investments in natural resources using contingent-claim framework with application to bituminous coal developments in Korea," Energy, Elsevier, vol. 34(9), pages 1215-1224.
    11. Schachter, J.A. & Mancarella, P., 2016. "A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 261-271.
    12. Nakada, Tatsuhiro & Shin, Kongjoo & Managi, Shunsuke, 2016. "The effect of demand response on purchase intention of distributed generation: Evidence from Japan," Energy Policy, Elsevier, vol. 94(C), pages 307-316.
    13. repec:gam:jsusta:v:12:y:2020:i:19:p:8052-:d:421629 is not listed on IDEAS
    14. Andreis, Luisa & Flora, Maria & Fontini, Fulvio & Vargiolu, Tiziano, 2020. "Pricing reliability options under different electricity price regimes," Energy Economics, Elsevier, vol. 87(C).
    15. Leehter Yao & Wei Hong Lim & Sew Sun Tiang & Teng Hwang Tan & Chin Hong Wong & Jia Yew Pang, 2018. "Demand Bidding Optimization for an Aggregator with a Genetic Algorithm," Energies, MDPI, Open Access Journal, vol. 11(10), pages 1-22, September.
    16. He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2012. "Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China," Energy, Elsevier, vol. 47(1), pages 230-236.
    17. Deng, Qianli & Jiang, Xianglin & Cui, Qingbin & Zhang, Limao, 2015. "Strategic design of cost savings guarantee in energy performance contracting under uncertainty," Applied Energy, Elsevier, vol. 139(C), pages 68-80.
    18. Deng, Shi-Jie & Xu, Li, 2009. "Mean-risk efficient portfolio analysis of demand response and supply resources," Energy, Elsevier, vol. 34(10), pages 1523-1529.
    19. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    20. Zare, Kazem & Moghaddam, Mohsen Parsa & Sheikh El Eslami, Mohammad Kazem, 2010. "Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology," Energy, Elsevier, vol. 35(7), pages 2999-3007.
    21. Kavvadias, K.C., 2016. "Energy price spread as a driving force for combined generation investments: A view on Europe," Energy, Elsevier, vol. 115(P3), pages 1632-1639.
    22. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.


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