Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/17/4339 |
| _version_ | 1827708317940580352 |
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| author | Simone Buffa Anton Soppelsa Mauro Pipiciello Gregor Henze Roberto Fedrizzi |
| author_facet | Simone Buffa Anton Soppelsa Mauro Pipiciello Gregor Henze Roberto Fedrizzi |
| author_sort | Simone Buffa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:02:01Z |
| format | Article |
| id | doaj.art-92bb677b3f284729a624c8580d30b77b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T17:02:01Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-92bb677b3f284729a624c8580d30b77b2023-11-20T10:56:20ZengMDPI AGEnergies1996-10732020-08-011317433910.3390/en13174339Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive ControlSimone Buffa0Anton Soppelsa1Mauro Pipiciello2Gregor Henze3Roberto Fedrizzi4Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, ItalyEurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, ItalyEurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, ItalyDepartment of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309-0428, USAEurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, ItalyDistrict 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.https://www.mdpi.com/1996-1073/13/17/43395GDHCcold district heatingambient loopsheat pump systemsdemand side managementsmart energy systems |
| spellingShingle | Simone Buffa Anton Soppelsa Mauro Pipiciello Gregor Henze Roberto Fedrizzi Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control Energies 5GDHC cold district heating ambient loops heat pump systems demand side management smart energy systems |
| title | Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control |
| title_full | Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control |
| title_fullStr | Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control |
| title_full_unstemmed | Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control |
| title_short | Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control |
| title_sort | fifth generation district heating and cooling substations demand response with artificial neural network based model predictive control |
| topic | 5GDHC cold district heating ambient loops heat pump systems demand side management smart energy systems |
| url | https://www.mdpi.com/1996-1073/13/17/4339 |
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