Equipment capacity optimization of an educational building's CCHP system by genetic algorithm and sensitivity analysis

Combined cooling, heating, and power (CCHP) systems produce electricity, cooling, and heat due to their high efficiency and low emission. These systems have been widely applied in various building types, such as offices, hotels, hospitals and malls. In this paper, an economic and technical analysis to determine the size and operation of the required gas engine for specific electricity, cooling, and heating load curves during a year has been conducted for a building. To perform this task, an objective function net present value (NPV) was introduced and maximized by a genetic algorithm (GA). In addition, the results end up finding optimal capacities. Furthermore, a sensitivity analysis was necessary to show how the optimal solutions vary due to changes in some key parameters such as fuel price, buying electricity price, and selling electricity price. The results show that these parameters have an effect on the system’s performance.