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Demand response in the service sector – Theoretical, technical and practical potentials

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  • Wohlfarth, Katharina
  • Klobasa, Marian
  • Gutknecht, Ralph

Abstract

Flexible energy demand (demand response, DR) is seen as one solution to integrate intermittent renewable energy sources into future energy systems.

Suggested Citation

  • Wohlfarth, Katharina & Klobasa, Marian & Gutknecht, Ralph, 2020. "Demand response in the service sector – Theoretical, technical and practical potentials," Applied Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:appene:v:258:y:2020:i:c:s0306261919317763
    DOI: 10.1016/j.apenergy.2019.114089
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    References listed on IDEAS

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    Cited by:

    1. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "How should price-responsive electricity tariffs evolve? An analysis of the German net demand case," Utilities Policy, Elsevier, vol. 66(C).
    2. El Gohary, Fouad, 2024. "The price signal paradigm – On the evolution and limitations of demand-side flexibility in the EU," Energy Policy, Elsevier, vol. 192(C).
    3. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
    4. Wenhui Zhao & Zilin Wu & Bo Zhou & Jiaoqian Gao, 2024. "Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    5. Sonja Arnold-Keifer & Simon Hirzel & Clemens Rohde, 2024. "Understanding Energy Demand of the Tertiary Sector by Energy Carriers and End-Uses: An Integrated Bottom-Up and Top-Down Model Taking Germany as the Example," Energies, MDPI, vol. 17(17), pages 1-25, September.
    6. 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.
    7. Yan Li & Zhiwen Liu & Chong Shao & Bingjun Lin & Jiayu Rong & Nan Dong & Buyun Su & Yuejia Hong, 2024. "A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control," Energies, MDPI, vol. 17(20), pages 1-14, October.
    8. van Zoest, Vera & El Gohary, Fouad & Ngai, Edith C.H. & Bartusch, Cajsa, 2021. "Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector," Applied Energy, Elsevier, vol. 302(C).
    9. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    10. Luchnikov, I. & Métivier, D. & Ouerdane, H. & Chertkov, M., 2021. "Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation," Applied Energy, Elsevier, vol. 285(C).
    11. 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).
    12. Li, He & Wang, Pengyu & Fang, Debin, 2024. "Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations," Energy Economics, Elsevier, vol. 136(C).
    13. Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(C).
    14. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2021. "Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach," Applied Energy, Elsevier, vol. 299(C).
    15. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).

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