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Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

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  • Héctor Marañón-Ledesma

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Asgeir Tomasgard

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

Abstract

One of the challenges in the transition towards a zero-emission power system in Europe will be to achieve an efficient and reliable operation with a high share of intermittent generation. The objective of this paper is to analyse the role that Demand Response (DR) potentially can play in a cost-efficient development until 2050. The benefits of DR consist of integrating renewable source generation and reducing peak load consumption, leading to a reduction in generation, transmission, and storage capacity investments. The capabilities of DR are implemented in the European Model for Power Investments with high shares of Renewable Energy (EMPIRE), which is an electricity sector model for long-term capacity and transmission expansion. The model uses a multi-horizon stochastic approach including operational uncertainty with hourly resolution and multiple investment periods in the long-term. DR is modelled through several classes of shiftable and curtailable loads in residential, commercial, and industrial sectors, including flexibility periods, operational costs, losses, and endogenous DR investments, for 31 European countries. Results of the case study shows that DR capacity partially substitutes flexible supply-side capacity from peak gas plants and battery storage, through enabling more solar PV generation. A European DR capacity at 91 GW in 2050 reduces the peak plant capacities by 11% and storage capacity by 86%.

Suggested Citation

  • Héctor Marañón-Ledesma & Asgeir Tomasgard, 2019. "Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market," Energies, MDPI, vol. 12(15), pages 1-24, August.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2976-:d:253963
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    3. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
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    5. Rolf Golombek & Mads Greaker & Snorre Kverndokk & Lin Ma, 2023. "Policies to Promote Carbon Capture and Storage Technologies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(1), pages 267-302, May.
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    7. Amjad Ali & Kashif Irshad & Mohammad Farhan Khan & Md Moinul Hossain & Ibrahim N. A. Al-Duais & Muhammad Zeeshan Malik, 2021. "Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    8. Rolf Golombek & Mads Greaker & Snorre Kverndokk & Lin Ma, 2021. "The Transition to Carbon Capture and Storage Technologies," CESifo Working Paper Series 9047, CESifo.
    9. Golombek, Rolf & Lind, Arne & Ringkjøb, Hans-Kristian & Seljom, Pernille, 2022. "The role of transmission and energy storage in European decarbonization towards 2050," Energy, Elsevier, vol. 239(PC).
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    13. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Seljom, Pernille & Lind, Arne & Wagner, Fabian & Mesfun, Sennai, 2020. "Short-term solar and wind variability in long-term energy system models - A European case study," Energy, Elsevier, vol. 209(C).

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