4E analysis and tri-objective optimization of a novel solar 4th cogeneration system for a smart residential building in various climates of Iran

Abstract

Cogeneration systems are increasingly used in residential buildings due to their high efficiency, low cost, and compatibility with the grid. This investigation proposes a dynamic simulation of a novel intelligent building-integrated solar cogeneration system using TRNSYS software to provide the power demand of system equipment and the required electricity, heating, cooling, and freshwater of a typical residential building in Iran. This system is equipped with photovoltaic panels, evacuated tube collectors, a double-effect absorption chiller, a reverse osmosis water desalination unit, and a micro-gas turbine as a backup system. Using season-temperature sensors and differential controllers helps monitor and handle the energy rates in different parts of the system and building by comparing the outside temperature and relative humidity with comfort conditions in an intelligent method. The system performance is assessed and compared from energy, exergy, exergy-economic, and environmental impact points of view in four cities that cover Iran’s climate variety. EES software is employed to transfer the thermodynamic properties of air and water to the model in TRNSYS. Furthermore, the tri-objective optimization is conducted with a novel procedure through a combination of a machine learning method and genetic algorithm in MATLAB by considering the exergy efficiency, unit product cost, and CO2 emission as objective functions to find the best point of the system and present a 3D Pareto-Frontier. The results show that the system can satisfy all the building energy demands, and on average, the hourly cooling and heating energy production during a year in this system are 1.01 kWh and 1.37 kWh, respectively. Also, the excess produced power is sold to the grid to compensate for some of the system overheads. The tri-objective optimization upshot shows that the values of exergy efficiency, CO2 emission, and unit product cost of this system at the best point are 44.9%, 0.158 ton/MWh, and 12.26 €/MWh, respectively, in Tehran province. Based on the results, the system performance indexes are improved compared to similar technologies found in the literature.