Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control
- Eurac Research, Bolzano (Italy). Inst. for Renewable Energy
- Univ. of Colorado, Boulder, CO (United States). Dept. of Civil, Environmental and Architectural Engineering; National Renewable Energy Lab. (NREL), Golden, CO (United States); Renewable and Sustainable Energy Inst. (RASEI), Boulder, CO (United States)
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.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); European Research Council (ERC)
- Grant/Contract Number:
- AC36-08GO28308; 857811
- OSTI ID:
- 1677445
- Report Number(s):
- NREL/JA-5500-78014; MainId:31923; UUID:3f14db91-7c46-48c9-873f-7803fafdef41; MainAdminID:18634
- Journal Information:
- Energies, Vol. 13, Issue 17; ISSN 1996-1073
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- United States
- Language:
- English
Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review
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journal | January 2021 |
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