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Demand response with heuristic control strategies for modulating heat pumps

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  • Dengiz, Thomas
  • Jochem, Patrick
  • Fichtner, Wolf

Abstract

The flexibility of electrical heating devices can contribute to overcoming the challenges caused by increasing shares of volatile renewable energy sources in the energy system. Especially modulating heat pumps are suitable for using intelligent control strategies that vary the pumps’ power output based on demand response signals. In this paper, we define optimization problems for minimizing the heating costs and the surplus energy of a residential area, and we introduce novel heuristic control strategies for modulating heat pumps to solve these problems. The heuristic control strategies make use of a privacy preserving control and communication architecture that combines central and decentralized control approaches. All buildings use an underfloor heating system and a domestic hot water tank as thermal storages. Compared to a conventional control strategy, the results show average cost reductions of between 4.1% and 13.3% for the cost minimization heuristics, and average improvements of between 38.3% and 52.6% for the surplus energy minimization heuristic. Contrary to approaches for finding the globally optimal solution, the introduced heuristic control strategies have significantly lower computational times and do not require perfect foresight regarding future demands and electricity generation.

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  • Dengiz, Thomas & Jochem, Patrick & Fichtner, Wolf, 2019. "Demand response with heuristic control strategies for modulating heat pumps," Applied Energy, Elsevier, vol. 238(C), pages 1346-1360.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:1346-1360
    DOI: 10.1016/j.apenergy.2018.12.008
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    2. Maximilian Schulz & Thomas Kemmler & Julia Kumm & Kai Hufendiek & Bernd Thomas, 2020. "A More Realistic Heat Pump Control Approach by Application of an Integrated Two-Part Control," Energies, MDPI, vol. 13(11), pages 1-22, June.
    3. Lissy Langer, 2020. "An Optimal Peer-to-Peer Market Considering Modulating Heat Pumps and Photovoltaic Systems under the German Levy Regime," Energies, MDPI, vol. 13(20), pages 1-25, October.
    4. Beccali, Marco & Bellia, Laura & Fragliasso, Francesca & Bonomolo, Marina & Zizzo, Gaetano & Spada, Gennaro, 2020. "Assessing the lighting systems flexibility for reducing and managing the power peaks in smart grids," Applied Energy, Elsevier, vol. 268(C).
    5. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    6. Valeria Palomba & Efstratios Varvagiannis & Sotirios Karellas & Andrea Frazzica, 2019. "Hybrid Adsorption-Compression Systems for Air Conditioning in Efficient Buildings: Design through Validated Dynamic Models," Energies, MDPI, vol. 12(6), pages 1-28, March.
    7. Dengiz, Thomas & Jochem, Patrick, 2020. "Decentralized optimization approaches for using the load flexibility of electric heating devices," Energy, Elsevier, vol. 193(C).
    8. Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
    9. Finck, Christian & Li, Rongling & Zeiler, Wim, 2019. "Economic model predictive control for demand flexibility of a residential building," Energy, Elsevier, vol. 176(C), pages 365-379.
    10. D’Ettorre, F. & Banaei, M. & Ebrahimy, R. & Pourmousavi, S. Ali & Blomgren, E.M.V. & Kowalski, J. & Bohdanowicz, Z. & Łopaciuk-Gonczaryk, B. & Biele, C. & Madsen, H., 2022. "Exploiting demand-side flexibility: State-of-the-art, open issues and social perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    11. Meesenburg, Wiebke & Markussen, Wiebke Brix & Ommen, Torben & Elmegaard, Brian, 2020. "Optimizing control of two-stage ammonia heat pump for fast regulation of power uptake," Applied Energy, Elsevier, vol. 271(C).
    12. Xia, Mingchao & Song, Yuguang & Chen, Qifang, 2019. "Hierarchical control of thermostatically controlled loads oriented smart buildings," Applied Energy, Elsevier, vol. 254(C).
    13. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    14. Sasaki, Kento & Aki, Hirohisa & Ikegami, Takashi, 2022. "Application of model predictive control to grid flexibility provision by distributed energy resources in residential dwellings under uncertainty," Energy, Elsevier, vol. 239(PB).
    15. Golmohamadi, Hessam, 2021. "Stochastic energy optimization of residential heat pumps in uncertain electricity markets," Applied Energy, Elsevier, vol. 303(C).
    16. Dhirendran Munith Kumar & Pietro Catrini & Antonio Piacentino & Maurizio Cirrincione, 2023. "Integrated Thermodynamic and Control Modeling of an Air-to-Water Heat Pump for Estimating Energy-Saving Potential and Flexibility in the Building Sector," Sustainability, MDPI, vol. 15(11), pages 1-23, May.

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