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The flexibility gap: Socioeconomic and geographical factors driving residential flexibility

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  • Ribó-Pérez, David
  • Heleno, Miguel
  • Álvarez-Bel, Carlos

Abstract

Residential consumers are moving to the center of electricity systems and their flexibility is seen as a key resource to integrate renewable energy sources and support the grid. However, residential flexibility capacities are not homogeneous, as they depend on household appliances, comfort patterns, occupancy, and climate conditions. Here, we calculate the technical flexibility capacities of 45 consumer types in mainland Spain, organised according to income and regional criteria. We show that flexibility gaps exist at both regional and socioeconomic (income) levels with flexibility differences of up to 10 times more capacity between the household groups from the lowest to the highest capacities. These geographical and socioeconomic gaps in flexibility can lead to distortions in national markets and have the potential to exclude citizens from the provision of flexibility services. Our results show in quantitative terms that a consumer-centered approach without considering correcting measures nor these gaps in drafting energy policies may lead to increasing inequality levels in the residential sector. Under an economic competitive paradigm, households with lower income levels or located in regions with lower flexibility potential may be excluded from the provision of flexibility to the detriment of households with larger potential, raising justice concerns in a flexibility-based energy transition.

Suggested Citation

  • Ribó-Pérez, David & Heleno, Miguel & Álvarez-Bel, Carlos, 2021. "The flexibility gap: Socioeconomic and geographical factors driving residential flexibility," Energy Policy, Elsevier, vol. 153(C).
  • Handle: RePEc:eee:enepol:v:153:y:2021:i:c:s0301421521001518
    DOI: 10.1016/j.enpol.2021.112282
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    2. Hurtado-Pérez, Elías & Bastida-Molina, Paula & Aparisi-Cerdá, Isabel & Alfonso-Solar, David & Fernández, Ana Rodríguez, 2024. "Multicriteria solar photovoltaic potential evaluation for high educational buildings. Case study of Polytechnic University of Valencia, Spain," Renewable Energy, Elsevier, vol. 227(C).
    3. Hennig, Roman J. & de Vries, Laurens J. & Tindemans, Simon H., 2023. "Congestion management in electricity distribution networks: Smart tariffs, local markets and direct control," Utilities Policy, Elsevier, vol. 85(C).
    4. Leila Luttenberger Marić & Hrvoje Keko & Marko Delimar, 2022. "The Role of Local Aggregator in Delivering Energy Savings to Household Consumers," Energies, MDPI, vol. 15(8), pages 1-27, April.
    5. Hennig, Roman J. & Ribó-Pérez, David & de Vries, Laurens J. & Tindemans, Simon H., 2022. "What is a good distribution network tariff?—Developing indicators for performance assessment," Applied Energy, Elsevier, vol. 318(C).
    6. Muttaqee, Mahmood & Stelmach, Greg & Zanocco, Chad & Flora, June & Rajagopal, Ram & Boudet, Hilary S., 2024. "Time of use pricing and likelihood of shifting energy activities, strategies, and timing," Energy Policy, Elsevier, vol. 187(C).
    7. Ribó-Pérez, D. & Carrión, A. & Rodríguez García, J. & Álvarez Bel, C., 2021. "Ex-post evaluation of Interruptible Load programs with a system optimisation perspective," Applied Energy, Elsevier, vol. 303(C).
    8. Baik, Sosung & Hines, Jeffrey F. & Sim, Jaeung, 2023. "Racial disparities in the energy burden beyond socio-economic inequality," Energy Economics, Elsevier, vol. 127(PA).

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