Research >> 1. Energy Saving from HVAC System >>Optimization for the operation of multiple heat sourse equipments


■ Optimization for the operation of multiple heat sourse equipments


□ Study on optimization for the operation of multiple heat sourse equipments
 in a actral heating/cooling plant using simulation

Background and Purpose of This Study
In recent years, environmental issues, including global warming, energy conservation and reducing Carbon-dioxide (CO2) emissions, are increasingly causing more attentions of all over the world. These issues are important in the field of building equipments industry as well. Buildings occupied by different types of tenant, whose work schedules might differ from each other, are equipped with multiple heat source equipments having different performance to satisfy different requirements, from large heating/cooling loads to small ones.
 To satisfy heating/cooling requirements, achieve energy conservation and ensure cost efficiency, it is important to study the combination and operation priority order of the heat source equipments and find out the optimal operation method.



Figure-1 Subject building



Figure-2 System model by MATLAB/Simulink



Concrete Content of This Study and the Results
An actual district heating/cooling plant located in Osaka Japan, which consist of two absorption chiller/heaters, one centrifugal chiller, one ice chiller, and two air-source heat pumps, is studied to find the optimal operation combination of the heat source equipments. In detail, the following studies are conducted. 1) Develops mathematical models of each equipment in the heat source system using the specification data and refining the model using the data measured by the Building Energy Management System (BEMS). 2) Connects all component models to construct the whole system model of the plant. 3) Uses the system model to simulate the energy consumption, running cost and carbon-dioxide emissions of several different combinations of heat source equipments to find an optimal operational combination. From the simulation results, the optimal combination case can reduce the energy consumption by 17.1%, running cost by 10.9% and carbon-dioxide emissions by 26.0%, compared to the present operational combination.