One of the major advantages of the computer simulated HVAC systems is the possibility to find the optimal size of system components using a certain optimization algorithm. The number of the optimization variables is flexible and depends on the system configuration and our needs. Each of these variables is constrained in its specific way. Apart from the costs, we can minimize the emissions or primary energy consumption. In multi-objective optimization, we minimize two or more of these objectives. Using certain weighting system, objectives within multi-objective simulation can be prioritized.
The most common objective function is the one with a goal to minimize the costs. It can be defined in several ways. Investment cost, such as purchase of components, connection to a supplier and installation, is a one time cost. On the other hand, the annual cost, which consists of running (energy cost, maintenance) and periodic cost (repair or exchange of components), is increasing with the time. We can minimize just the investment or just the running cost, but the best way is to take both of these components into consideration. One way to do this is to use the annuities to distribute investment cost within certain amount of years. We assume a certain yearly rise in fuel cost and calculate the yearly mean. The goal is to define a one-year total cost of the system, based on the typical lifetime.
The suitable optimization algorithm is chosen based on the characteristics of optimization variables, their constraints and objective function. The optimization is coupled to a whole year hourly system simulation. As a case study, a system containing a biomass boiler, a thermal storage and thermal solar collectors is being simulated and optimized based on different objective functions. Solar collector area, storage volume and boiler design power are the optimization variables. Different configurations are obtained for different climate conditions and the results are presented.