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Electricity load level detail in computational general equilibrium – part II – welfare impacts of a demand response program

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  • Rodrigues, Renato
  • Linares, Pedro

Abstract

Demand Response (DR) programs send time-based signals to electricity consumers so that they may shift or reduce their loads to better adjust to the system requirements, thus creating interesting benefits for power systems. However, the assessment of these benefits is quite challenging, since it requires combining features from bottom-up and computable general equilibrium (CGE) models. This paper assesses the impacts of a DR program in Spain using a CGE model which includes both technological and temporal disaggregation. The model is able to account for the indirect effects characteristic of CGE models while also mimicking the detailed behavior of the electricity operation and investment available before only in bottom-up detailed models. Our results show clearly the advantages of using this approach for this type of policies.

Suggested Citation

  • Rodrigues, Renato & Linares, Pedro, 2015. "Electricity load level detail in computational general equilibrium – part II – welfare impacts of a demand response program," Energy Economics, Elsevier, vol. 47(C), pages 52-67.
    • Handle: RePEc:eee:eneeco:v:47:y:2015:i:c:p:52-67
    DOI: 10.1016/j.eneco.2014.10.015
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    References listed on IDEAS

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    1. Rodrigues, Renato & Linares, Pedro, 2014. "Electricity load level detail in computational general equilibrium – Part I – Data and calibration," Energy Economics, Elsevier, vol. 46(C), pages 258-266.
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    7. Sue Wing, Ian, 2008. "The synthesis of bottom-up and top-down approaches to climate policy modeling: Electric power technology detail in a social accounting framework," Energy Economics, Elsevier, vol. 30(2), pages 547-573, March.
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    Citations

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    Cited by:

    1. Cédric Clastres & Haikel Khalfallah, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Post-Print hal-03193212, HAL.
    2. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    3. Alimohammadisagvand, Behrang & Jokisalo, Juha & Sirén, Kai, 2018. "Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building," Applied Energy, Elsevier, vol. 209(C), pages 167-179.
    4. Bert Willems & Juulia Zhou, 2020. "The Clean Energy Package and Demand Response: Setting Correct Incentives," Energies, MDPI, vol. 13(21), pages 1-19, October.
    5. Pablo Pintos & Pedro Linares, 2016. "Assessing the EU ETS with an Integrated Model," Working Papers 01-2016, Economics for Energy.
    6. Rodrigues, Renato & Linares, Pedro, 2014. "Electricity load level detail in computational general equilibrium – Part I – Data and calibration," Energy Economics, Elsevier, vol. 46(C), pages 258-266.
    7. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    8. Clastres, Cédric & Khalfallah, Haikel, 2021. "Dynamic pricing efficiency with strategic retailers and consumers: An analytical analysis of short-term market interactions," Energy Economics, Elsevier, vol. 98(C).
    9. Peng Ou & Ruting Huang & Xin Yao, 2016. "Economic Impacts of Power Shortage," Sustainability, MDPI, vol. 8(7), pages 1-21, July.
    10. Cédric Clastres & Haikel Khalfallah, 2020. "Retailers' strategies facing demand response and markets interactions," Working Papers hal-03167543, HAL.

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    More about this item

    Keywords

    Computable general equilibrium (CGE); Electricity demand response;

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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