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Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

Author

Listed:
  • Simone Buffa

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Anton Soppelsa

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Mauro Pipiciello

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

  • Gregor Henze

    (Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309-0428, USA
    National Renewable Energy Laboratory, Golden, CO 80401, USA
    Renewable and Sustainable Energy Institute, Boulder, CO 80309, USA)

  • Roberto Fedrizzi

    (Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy)

Abstract

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

Suggested Citation

  • Simone Buffa & Anton Soppelsa & Mauro Pipiciello & Gregor Henze & Roberto Fedrizzi, 2020. "Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control," Energies, MDPI, vol. 13(17), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4339-:d:402291
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    2. Quirosa, Gonzalo & Torres, Miguel & Chacartegui, Ricardo, 2022. "Analysis of the integration of photovoltaic excess into a 5th generation district heating and cooling system for network energy storage," Energy, Elsevier, vol. 239(PC).
    3. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Romanov, D. & Leiss, B., 2022. "Geothermal energy at different depths for district heating and cooling of existing and future building stock," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Wirtz, Marco & Neumaier, Lisa & Remmen, Peter & Müller, Dirk, 2021. "Temperature control in 5th generation district heating and cooling networks: An MILP-based operation optimization," Applied Energy, Elsevier, vol. 288(C).
    6. Jordi García-Céspedes & Ignasi Herms & Georgina Arnó & José Juan de Felipe, 2022. "Fifth-Generation District Heating and Cooling Networks Based on Shallow Geothermal Energy: A review and Possible Solutions for Mediterranean Europe," Energies, MDPI, vol. 16(1), pages 1-31, December.
    7. Riccardo Toffanin & Paola Caputo & Marco Belliardi & Vinicio Curti, 2022. "Low and Ultra-Low Temperature District Heating Equipped by Heat Pumps—An Analysis of the Best Operative Conditions for a Swiss Case Study," Energies, MDPI, vol. 15(9), pages 1-19, May.
    8. Yao, Shuai & Wu, Jianzhong & Qadrdan, Meysam, 2024. "A state-of-the-art analysis and perspectives on the 4th/5th generation district heating and cooling systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    9. Tomasz Tietze & Piotr Szulc & Daniel Smykowski & Andrzej Sitka & Romuald Redzicki, 2021. "Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations," Energies, MDPI, vol. 14(12), pages 1-17, June.
    10. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    11. Giuseppe Edoardo Dino & Pietro Catrini & Valeria Palomba & Andrea Frazzica & Antonio Piacentino, 2023. "Promoting the Flexibility of Thermal Prosumers Equipped with Heat Pumps to Support Power Grid Management," Sustainability, MDPI, vol. 15(9), pages 1-22, May.
    12. Angelidis, O. & Ioannou, A. & Friedrich, D. & Thomson, A. & Falcone, G., 2023. "District heating and cooling networks with decentralised energy substations: Opportunities and barriers for holistic energy system decarbonisation," Energy, Elsevier, vol. 269(C).
    13. Lund, Henrik & Østergaard, Poul Alberg & Nielsen, Tore Bach & Werner, Sven & Thorsen, Jan Eric & Gudmundsson, Oddgeir & Arabkoohsar, Ahmad & Mathiesen, Brian Vad, 2021. "Perspectives on fourth and fifth generation district heating," Energy, Elsevier, vol. 227(C).
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