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Article

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

1
Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy
2
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309-0428, USA
3
National Renewable Energy Laboratory, Golden, CO 80401, USA
4
Renewable and Sustainable Energy Institute, Boulder, CO 80309, USA
*
Author to whom correspondence should be addressed.
Energies 2020, 13(17), 4339; https://doi.org/10.3390/en13174339
Received: 16 July 2020 / Revised: 8 August 2020 / Accepted: 11 August 2020 / Published: 21 August 2020
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. View Full-Text
Keywords: 5GDHC; cold district heating; ambient loops; heat pump systems; demand side management; smart energy systems 5GDHC; cold district heating; ambient loops; heat pump systems; demand side management; smart energy systems
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MDPI and ACS Style

Buffa, S.; Soppelsa, A.; Pipiciello, M.; Henze, G.; Fedrizzi, R. Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control. Energies 2020, 13, 4339. https://doi.org/10.3390/en13174339

AMA Style

Buffa S, Soppelsa A, Pipiciello M, Henze G, Fedrizzi R. Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control. Energies. 2020; 13(17):4339. https://doi.org/10.3390/en13174339

Chicago/Turabian Style

Buffa, Simone, Anton Soppelsa, Mauro Pipiciello, Gregor Henze, and Roberto Fedrizzi. 2020. "Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control" Energies 13, no. 17: 4339. https://doi.org/10.3390/en13174339

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