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Estimating the Elasticity to Real-Time Pricing: Evidence from the Spanish Electricity Market

Author

Listed:
  • Natalia Fabra
  • David Rapson
  • Mar Reguant
  • Jingyuan Wang

Abstract

We estimate consumer response to the first large-scale real-time electricity pricing (RTP) program. In 2015, RTP became the default tariff for residential customers in Spain, exposing them to prices that vary hourly based on day-ahead wholesale prices. Using expected national wind production as an instrument for price, we estimate household-level demand elasticities for RTP households and non-RTP (placebo) households. Estimates show no difference in behavior across RTP and non-RTP households. Reasons for nonresponse may include low potential gains or high nonmonetary costs of information acquisition and behavioral change. We discuss implications for optimal dynamic electricity pricing.

Suggested Citation

  • Natalia Fabra & David Rapson & Mar Reguant & Jingyuan Wang, 2021. "Estimating the Elasticity to Real-Time Pricing: Evidence from the Spanish Electricity Market," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 425-429, May.
    • Handle: RePEc:aea:apandp:v:111:y:2021:p:425-29
    DOI: 10.1257/pandp.20211007
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    Citations

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    Cited by:

    1. Olympia Bover & Natalia Fabra & Sandra García-Uribe & Aitor Lacuesta & Roberto Ramos, 2020. "Firms and households during the pandemic: what do we learn from their electricity consumption?," Occasional Papers 2031, Banco de España.
    2. Sai Bravo & Carole Haritchabalet, 2023. "Prosumers: Grid Storage vs Small Fuel-Cell," Working Papers hal-04119625, HAL.
    3. Fabra, Natalia & Motta, Massimo & Peitz, Martin, 2022. "Learning from electricity markets: How to design a resilience strategy," Energy Policy, Elsevier, vol. 168(C).
    4. Fabra, Natalia, 2021. "The energy transition: An industrial economics perspective," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    5. Benatia, David, 2022. "Ring the alarm! Electricity markets, renewables, and the pandemic," Energy Economics, Elsevier, vol. 106(C).
    6. Fabra, Natalia & Lacuesta, Aitor & Souza, Mateus, 2022. "The implicit cost of carbon abatement during the COVID-19 pandemic," European Economic Review, Elsevier, vol. 147(C).
    7. Petersen, Claire & Reguant, Mar & Segura, Lola, 2024. "Measuring the impact of wind power and intermittency," Energy Economics, Elsevier, vol. 129(C).
    8. Kim, Jihyo & Lee, Soomin & Jang, Heesun, 2022. "Lessons from residential electricity demand analysis on the time of use pricing experiment in South Korea," Energy Economics, Elsevier, vol. 113(C).
    9. Singhal, Puja & Pahle, Michael & Kalkuhl, Matthias & Levesque, Antoine & Sommer, Stephan & Berneiser, Jessica, 2022. "Beyond good faith: Why evidence-based policy is necessary to decarbonize buildings cost-effectively in Germany," Energy Policy, Elsevier, vol. 169(C).
    10. Pretto, Madeline, 2021. "Tail-risk Comprehension and Protection in Real-time Electricity Pricing : Experimental Evidence," Warwick-Monash Economics Student Papers 25, Warwick Monash Economics Student Papers.
    11. Ciarreta, Aitor & Espinosa, Maria Paz & Pizarro-Irizar, Cristina, 2023. "Pricing policies for efficient demand side management in liberalized electricity markets," Economic Modelling, Elsevier, vol. 121(C).
    12. Peña, Juan Ignacio & Rodríguez, Rosa & Mayoral, Silvia, 2022. "Cannibalization, depredation, and market remuneration of power plants," Energy Policy, Elsevier, vol. 167(C).
    13. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    14. Sai Bravo & Carole Haritchabalet, 2023. "Prosumers: Grid Storage vs Small Fuel-Cell," Working papers of Transitions Energétiques et Environnementales (TREE) hal-04119625, HAL.
    15. Silvana Tiedemann & Raffaele Sgarlato & Lion Hirth, 2023. "Price elasticity of electricity demand: Using instrumental variable regressions to address endogeneity and autocorrelation of high-frequency time series," Papers 2306.12863, arXiv.org.

    More about this item

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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