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Assessing the value of demand response in a decarbonized energy system – A large-scale model application

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  • Misconel, Steffi
  • Zöphel, Christoph
  • Möst, Dominik

Abstract

This paper presents extensive insights on the value of applying demand response (DR) in a system perspective against the background of two strongly contrasting decarbonization pathways for a decentralized and centralized European energy system with a 100% renewable share and sector coupling. The pathways are characterized by structural differences concerning the combination of installed renewable capacities, the acceptance for activated demand response potentials and different electricity, heat and hydrogen demands. The objective is to determine the potential role of demand response and its impact on the optimal combinations of flexibility options in a decentralized vs. centralized scenario framework model-endogenously. Therefore, openly available data and hourly time series of country-specific demand response potentials are implemented into a large-scale linear optimization model. Sensitivities concerning varying shares of demand response availability are used to identify main influencing factors on selected components of the electricity system such as the capacity and generation mix, storage requirements, renewable integration and their market value factors, CO2 emissions and total system costs. Model results show a higher reduction of total system costs and CO2 emissions per activated demand response unit in the photovoltaic dominated decentralized scenario (–55 MEUR/GWDR, –0.045 MtCO2/GWDR), compared to the wind dominated centralized scenario (–39 MEUR/GWDR, –0.037 MtCO2/GWDR). The outcomes conclude that the daily photovoltaic feed-in characteristics have a higher correlation with the time pattern of load shifting and shedding demand response appliances than wind feed-in characteristics.

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  • Misconel, Steffi & Zöphel, Christoph & Möst, Dominik, 2021. "Assessing the value of demand response in a decarbonized energy system – A large-scale model application," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921007364
    DOI: 10.1016/j.apenergy.2021.117326
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    2. Sevdari, Kristian & Calearo, Lisa & Andersen, Peter Bach & Marinelli, Mattia, 2022. "Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    4. Kostelac, Matija & Pavić, Ivan & Zhang, Ning & Capuder, Tomislav, 2022. "Uncertainty modelling of an industry facility as a multi-energy demand response provider," Applied Energy, Elsevier, vol. 307(C).
    5. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    6. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    7. Misconel, Steffi & Prina, Matteo Giacomo & Hobbie, Hannes & Möst, Dominik & Sparber, Wolfram, 2022. "How to determine bottom-up model-derived marginal CO2 abatement cost curves with high temporal, sectoral, and techno-economic resolution?," EconStor Preprints 260472, ZBW - Leibniz Information Centre for Economics.
    8. Kazmi, Hussain & Mehmood, Fahad & Shah, Maryam, 2024. "Quantifying residential energy flexibility potential for demand response programs using observational data from grid outages: Evidence from Pakistan," Energy Policy, Elsevier, vol. 188(C).
    9. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).

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