A decision support model for waste heat recovery systems design in Data Center and High-Performance Computing clusters utilizing liquid cooling and Phase Change Materials

Abstract

The Data Center (DC) and High-Performance Computing (HPC) sector is increasingly becoming one of the single largest energy consuming sectors in the energy system and the demand for new DCs is growing with the rising use of cloud-based services, social media and internet usage in general. DCs and HPC clusters employ energy intensive cooling in order to dissipate the generated heat from the Information Technology (IT) equipment. Conventionally, the cooling is almost exclusively done by air-based systems; however, the use of liquid based cooling has shown potential to increase computational efficiency of the systems and decrease cooling needs by operation above the free cooling limit. Furthermore, the liquid coolant can operate at higher temperatures allowing high temperature waste heat recovery. In addition, load shifting through Latent Thermal Energy Storage (LTES) will allow the non-controllable waste heat resource to be stored seasonally, thereby granting the ability to cover larger District Heating (DH) loads. In this work, a decision support model is developed that will take basic information regarding a HPC cluster or DC as inputs. The decision support model will provide a parameterized output that shows different configurations and design parameters that can be utilized for the system. The main outputs includes yearly energy savings, yearly cost savings and efficiency gains through the Power Usage Efficiency and the Energy Reuse Efficiency. The decision support model is demonstrated in a Danish case study. Electricity savings between 8.14 % and 10.8 % of the total cluster electricity consumption and a waste heat recovery potential of 85 MWh/year to 576 MWh/year are obtained. It is shown that if the DH covered is needed to be self-sufficient the configuration would require an LTES with PCM mass of 500 kg, but system configurations that operate as an addition to an existing local heating source shows increases in yearly energy savings of 332 % compared to self-sufficient configurations. The goal of the decision support model is to assist the design of future waste heat recovery applications through selection of system parameters including coolant temperatures, energy storage design parameters, DH supply temperatures and DH load coverage from the DC or HPC cluster.