Researchers from Austria’s University of Natural Resources and Life Sciences Vienna have presented a novel, low-tech model predictive control (MPC) algorithm for the grid-friendly operation of heat pumps. It is based on an algorithm introduced last year that was focused on optimizing thermal comfort. In the meantime, the group has enhanced the algorithm to also consider day-ahead electricity prices.
“The construction of thermally well-insulated buildings with thermal mass, coupled with heat pumps, is paramount in advancing the transition to an energy-efficient sector. A particularly grid-friendly system should be equipped with an intelligent control system in tandem with a decentralized renewable electricity generation source (e.g. photovoltaics),” said the group. “However, this approach makes it clear that the interaction of buildings with the electricity grid is unavoidable and intelligent control is necessary.”
The original model consisted of two components – the modeling of the house with its thermally activated components (TAB) and a prognosis-based control concept based on the analysis of weather forecast data. Its extension involved the consideration of price signals based on real electricity market prices or straightforward logic.
The new price component calculates the heating cost based on the predicted thermal demand, the heat pump’s coefficient of performance (COP), and the predicted electricity rates. It also includes parameters such as deviation factor and power factors, which allow for a more fine-tuned combination of heat comfort and cost savings. The addition was validated on a Matlab/Simulink simulation framework for December 2020.
“The validation of the new cost function, including deviation and power factors, is positive,” the academics said. “The comparison with the results of the original algorithm is satisfactory, and grid-supportive operation of heat pumps, considering price signals, is therefore possible. This validation is supported by the analysis of existing studies.”
Following the positive results from the validation, the group tested the upgraded MPC in a case study. That included real weather monitoring data from Viena, Austria, in 2023, along with the corresponding real exchange-based day-ahead prices. In addition, average grid costs, taxes, and levies of the Austrian market were added to the calculation.
The simulation was carried out on a low-energy building, with approximately 60 kWh/m2 and different thermally activated components (TABs). The heat pump in this house had a coefficient of performance (COP) of 4 and was assumed to be connected to a hot water buffer tank. The room temperature was set to 22 C throughout the entire month of the simulation. Overall, they examined four scenarios: the first accounted for thermal comfort; the second incorporated real energy prices; the third assumed prices were lower by 20%; and the fourth (No. 4) assumed they were higher by the same rate.
“The heating energy required is highest for the baseline scenario, while scenario No.4 requires around 100 kWh less energy. In addition, the energy costs are highest for the baseline scenario for all three cost variations, while the results with the new cost function lead to lower energy costs,” the group said. “Compared to the baseline scenario, the cost reduction for smaller electricity price fluctuations (reduced by 20%) is 6.65%, while larger price fluctuations (amplified by 20%) lead to a cost reduction of 12.5%.”
Concluding their research, the academics said that “this algorithm is a tool to increase the efficiency of heating and cooling technologies and to reduce the energy costs of buildings. In addition, the implementation of the first developed low-tech MPC in existing buildings proved the simple implementation (via single-board computer) and possible broad application of such a low-tech algorithm.”
Their findings were presented in “Extension of a low-tech MPC algorithm for grid-supportive heat pump operation,” published in Energy and Buildings.
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