Investigation of system optimization and control logic on a solar geothermal hybrid heat pump system based on integral effect test data

The solar geothermal hybrid heat pump systems hold great promise for sustainable heating and cooling applications. However, the practical challenges and limitations have resulted in a lack of extensive field trials due to the time and financial constraints associated with conducting field trials. As a result, previous studies have primarily relied on dynamic simulation tools to estimate the performance of prototype systems. The annual performance and efficiency of these hybrid systems are heavily influenced by the control logic of their individual components such as PVT and GSHP unit and the overall operation of the system. Consequently, there is a pressing need to conduct a comprehensive study on the sensitivity of system performance to variations in the operation control, particularly when employing advanced logic, such as fuzzy logic control. When it comes to real-world implementation, there are concerns about the feasibility of integrating additional components like PVT and GSHP into the system. Advanced control strategies like fuzzy logic may also raise questions about the initial capital costs and whether the benefits outweigh the investments. Thus, the system optimization study needs regarding to energy consumption and economic feasibility analysis based on precisely predictable simulation model and practical initial capital cost. In the present study, a solar geothermal hybrid heat pump system has been modeled with simulation and designed IET (Integral Effect Test) facility in an industrial building based on component SET (Separate Effect Test) data, conducted a comparison study for validation of the simulation model based on real field heating and cooling weekly IET data. To optimize the system's performance, annual Operation and Maintenance (O&M) costs sensitivity study was conducted for four different Cases, considering the effects of PVT, GSHP components, and fuzzy logic control. Furthermore, an Net Present Value (NPV) method economic analysis was conducted to evaluate the economic feasibility analysis, considering the initial capital cost, inflation rate, and annual O&M costs savings. The results demonstrate that the simulation model can precisely estimate annual energy consumption within 7.4 % compared to heating and cooling IET operation data. The sensitivity study revealed that Case 4, incorporating fuzzy logic control, exhibited the lowest energy consumption of 9450 kWh/year and the highest annual O&M savings of $1869 among the four Cases studied. However, the advanced system control logic increased the initial capital cost by 34 % ($12,490) compared to Case 1's single-stage GSHP. The NPV economic feasibility analysis, considering the initial capital cost, inflation rate, and annual O&M savings in comparison to Case 1, indicated that Case 4 with fuzzy logic control system showed the shortest payback period of 8.5 years.