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Cooling infrastructures contribute about 50% of total energy consumption in a typical data center. Computer models are pivotal in designing and optimizing energy-efficient cooling systems to reduce excessive cooling energy consumption. Compared to the conventional building energy simulation tools, equation-based object-oriented modeling language Modelica is an emerging approach that can enable fast modeling and simulation of the dynamic cooling system in data centers. In this paper, we introduce a newly developed open source data center package in the Modelica Buildings library to support the fast modeling and simulation of cooling and control systems of data centers. The data center package contains major thermal and control component models, such as Computer Room Air Handler, Computer Room Air Conditioner, models of different subsystem configurations such as chillers with differently configured waterside economizer, as well as templates for different systems. Furthermore, we utilize the new package to perform a case study on the operation of a cooling system in the data center. In this case study, we investigate the performances of the cooling system under normal conditions and emergency situations such as blackout. The case study shows that the dynamic modeling and multi-domain co-simulation in the Modelica-based tool make it convenient for users to investigate not only the thermal performance but also the electrical performance of the data centers.

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Data center cooling accounts for about 1% of electricity usage in the United States. Computer models are pivotal in designing and operating energy-efficient cooling systems. Compared to conventional building performance simulation programs, the equation-based object-oriented modeling language Modelica is an emerging approach that can enable fast prototyping and dynamic simulation of cooling systems. In this case study, we first modeled the cooling and control systems of an actual data center located in Massachusetts using the open-source Modelica Buildings library, and then calibrated a baseline model based on measurement data. The simulation of the baseline model identified several operation-related issues in the cooling and control systems, such as degraded cooling coils, improper dead band in control settings, and simultaneous cooling and heating in air handlers. Afterwards, we used a sequential search technique as well as an optimization scheme to investigate the energy saving potentials for different energy efficiency measures aiming to address the abovementioned issues. Simulation results show potential energy savings up to 24% by resolving identified control-related issues and optimizing the supply air temperature.