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

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Title: Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control.
Authors: Buffa, Simone1 (AUTHOR) anton.soppelsa@eurac.edu, Soppelsa, Anton1 (AUTHOR) mauro.pipiciello@eurac.edu, Pipiciello, Mauro1 (AUTHOR) roberto.fedrizzi@eurac.edu, Henze, Gregor2,3,4 (AUTHOR) gregor.henze@colorado.edu, Fedrizzi, Roberto1 (AUTHOR)
Source: Energies (19961073). Sep2020, Vol. 13 Issue 17, p4339. 1p.
Subject Terms: *Heat storage, *Heating from central stations, *Prediction models, *Energy storage, *Artificial neural networks, *5G networks, *Heat pumps
Geographic Terms: Europe
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. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 145987719
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Sep2020, Vol. 13 Issue 17, p4339. 1p.
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  Data: *<searchLink fieldCode="DE" term="%22Heat+storage%22">Heat storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Heating+from+central+stations%22">Heating from central stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%225G+networks%22">5G networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Heat+pumps%22">Heat pumps</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Europe%22">Europe</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=145987719
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        Value: 10.3390/en13174339
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: 4339
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      – SubjectFull: Heat storage
        Type: general
      – SubjectFull: Heating from central stations
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: 5G networks
        Type: general
      – SubjectFull: Heat pumps
        Type: general
      – SubjectFull: Europe
        Type: general
    Titles:
      – TitleFull: Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control.
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            NameFull: Buffa, Simone
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            – D: 01
              M: 09
              Text: Sep2020
              Type: published
              Y: 2020
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              Value: 13
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              Value: 17
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            – TitleFull: Energies (19961073)
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