| Abstract [eng] |
This thesis explores the potential for optimizing the control of heat pump systems in response to fluctuating electricity market prices. To reduce heating costs and enhance the building's energy flexibility, a mathematical model of a heat distribution substation was developed and analyzed in the MATLAB/Simulink environment. The model replicates the control logic of a real-world system and is supplemented with an algorithm that dynamically adjusts the return water temperature setpoint based on electricity price variations—introducing boost, pre-boost, and peak pricing modes. Three experiments were conducted, each comparing the performance of the non-optimized and optimized models. The simulations used real outdoor air temperature and electricity price data, while the evaluation was based on electricity consumption (kWh) and associated costs (EUR). The results demonstrated that the optimized control significantly reduced electricity expenses (by up to 23.3%), despite a slight increase in energy usage. Additionally, a fixed-tariff analysis (0.16 €/kWh and 0.18 €/kWh) was performed to assess the benefits of optimization under different market scenarios. Furthermore, a real-time electricity price transmission prototype was constructed. A Raspberry Pibased system was configured to automatically retrieve ENTSO-E electricity price data via REST API and transmit it to Modbus TCP/IP protocol registers. This infrastructure enables the practical deployment of control algorithms in automated building management systems. The findings of this study indicate that the proposed solution offers promising economic benefits for heat substation control, particularly in the context of increasingly volatile electricity pricing. |
