Multi-objective optimization and exergoeconomic evaluation of a hybrid geothermal-PVT system integrated with PCM

The main purpose of this research is to study the techno-economic analysis of a photovoltaic thermal collector (PVT) combined with phase change materials (PCM) and ground source heat pump (GSHP). In the first step of this study, a numerical transient simulation of a water-to-water GSHP was developed by using a vertical U-type ground source heat exchanger (GSHX) and variable speed drive (VSD) compressor in the multi-usage operation modes (heating and cooling). To achieve the most supreme system performance, the multi-objective optimization of the Levelized cost of energy (LCOE) and exergy efficiency was done by using the multi-objective non-dominated sorting genetic algorithm version || (NSGA-||). This algorithm considers substantial elements such as the number of boreholes, depth of drilling for the location of pipes in GSHX, setpoint temperature of fan coil, length of converters, the number of PVT panels, slope and direction of PVT panels, the number of batteries, volume of the storage tank of phase change materials and percentage of phase change materials in the tank. The simulation of the heat pump is accomplished through a numeral coding in the Engineering Equation Solver (EES) and also for the optimization part, a programming method used in the MATLAB program. Moreover, due to the evaluation of the transient response of the model, TRNSYS software was implemented. Based on the research achievements, the Single objective optimization of exergy efficiency for the combined PVT-PCM and GSHP systems can be conducted to the highest solar factor (SF) and the lowest amount of urban electricity consumption. Furthermore, the calculation of the annual required load of the building for different scenarios has shown that the use of collectors in this combined system has reduced the total load of the building by 6.5%. The maximum percentage of PVT energy efficiency belongs to the first scenario with a value of 53% and the lowest one with a difference of 43% is for the fourth scenario. (c) 2021 Elsevier Ltd. All rights reserved.