Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control
1.1. Decarbonising the Building Sector with 5GDHC Systems
- to increase the efficiencies of the WSHP unit with a more stable operation and yield the possibility to operate when the heat demand is below the WSHP minimum capacity, hence limiting the number of on-off switching events that are a cause of stress of both the electrical and mechanical WSHP components, reducing their lifetime,
- to meet the user’s comfort requirements during thermal peak loads, mainly with respect to domestic hot water (DHW) delivery, and
- to increase the self-consumption of electricity from local decentralised non-dispatchable renewable energy sources, like rooftop photovoltaic systems. This is a value both for the user energy bill, as well as for the power distribution grid, benefiting from a form of flexibility that helps reduce the occurrence of power grid faults due to frequency or voltage variations. In fact, an aggregated WSHP pool can increase the electricity consumption when it is needed (negative balancing) and store it in the form of thermal energy or can partially reduce it (positive balancing).
1.2. Recent Publications about 5GDHC Systems and Urban Excess Heat Recovery
2. Materials and Methods
2.1. Physical Modelling and Laboratory Test of the 5GDHC Energy Transfer Station (ETS)
2.2. Physical Model Calibration, WSHP Limits, and Parametric Simulations on the Thermal Energy Storage System
- The outlet temperature at the evaporator () must be higher than 4.9 °C to avoid a freezing alarm. This critical condition can be reached during the WSHP operation around points A and B of Figure 6a.
- The outlet temperature at the condenser () must be lower than 57.3 °C to avoid an alarms for refrigerant high temperature (max 118 °C) and high pressure (max 40 bar at the compressor outlet). This critical condition can be reached during the WSHP operation around points B and C of Figure 6a.
- four values for the relative height of the sensor used to control the thermal energy storage temperature, as shown in Figure 7, and identified with a letter (A = 20%, B = 40%, C = 60%, and D = 80%);
- two values for the DHW TES volume identified by a number that follows the letter, equal to 0.5 m3 (1) and 1 m3 (2), respectively; and
- three values for the dead-band of the hysteresis implemented in the rule-based control that was fixed equal to 5 °C (1), 10 °C (2), and 15 °C (3).
- that aims at evaluating the fraction of time when the DHW tap water has a temperature below 44 °C with respect to the total draw-off period, according to Equation (6). It allows verifying whether the boundary conditions concerning the thermal energy storage capacity and control settings satisfy the user’s comfort.
- and , which are the average thermal energy supplied by the WSHP to the DHW TES and average electricity consumption per single charge, calculated as the yearly energy quantity divided by the yearly number of on-off cycles, according to Equations (7) and (8), respectively.
2.3. ANN Model and Training Algorithm
- is the thermal energy delivered by the on-off WSHP to the DHW TES in one control time step (4 kWhth) and represents the control input provided according to the results of the MPC algorithms during the on-line simulation (see Section 2.5).
- is the forecasted DHW load to cover under the hypothesis of the perfect prediction.
- and are the temperatures at the top and at the bottom of the TES, respectively.
- is the inlet temperature at the WSHP condenser here equal to .
- is the inlet temperature at the WSHP evaporator.
- is the electricity consumption of the substation during the DHW operation as a sum of the consumption of the heat pump compressor () and the hydraulic pumps ().
- is the thermal energy extracted by the WSHP from the DHC network.
2.4. ANN Model Training and Validation Results
2.5. MPC Implementation with an RBC Back-Up Controller and TRNSYS-LabVIEW Dynamic Link Library (DLL)
2.6. MPC vs. RBC Scenario Boundary Conditions
- The price of the thermal energy extracted from the 5GDHC grid is considered constant and equal to 0.05 (EUR/kWh) for the different scenarios.
- The electricity tariff during off-peak hours is considered constant and equal to 0.15 (EUR/kWh) for the different scenarios.
- The electricity tariff during on-peak hours varies, and it is equal to 0.30 (EUR/kWh) for MPC scenario 1 and 0.60 (EUR/kWh) for MPC scenario 2.
3. Results and Discussion
3.1. Parametric Analysis on the DHW TES System
3.2. Results of the Rule-Based Control (RBC) vs. Model Predictive Control (MPC) Scenario Analysis
Conflicts of Interest
Nomenclature and Abbreviations
|5GDHC||Fifth-generation district heating and cooling|
|ANN||Artificial neural network|
|COP||Coefficient of performance (-)|
|DHW||Domestic hot water|
|ETS||Energy transfer station|
|MPC||Model predictive control|
|TES||Thermal energy storage|
|WSHP||Water source heat pump|
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|DHW TES ANN Model||WSHP ANN Model|
|Eel,SUB,DHW (kWh)||Eth,HP eva,DHW (kWh)||COPsub|
|Eel,SUB,DHW,off-pek (kWh)||Eel, SUB,DHW,peak (kWh)||Non (-)|
|MPC scenario 1||0.15||0.3||54.3||41.3||454.3||1025.2||2.70||290||165||159|
|MPC scenario 2||0.15||0.6||54.3||41.4||458.2||1033.6||2.68||302||156||169|
|MPC scenario 1||0.15||0.3||0.05||92.8||51.3||144.1|
|MPC scenario 2||0.15||0.6||0.05||139.0||51.7||190.7|
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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
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/en13174339Chicago/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