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Hybrid geothermal heat pumps for cooling telecommunications data centers
Accepted Manuscript
Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers David P. Zurmuhl , Maciej Z. Lukawski , Gloria A. Aguirre , William R. Law , George P. Schnaars , Koenraad F. Beckers , C. Lindsay Anderson , Jefferson W. Tester PII: DOI: Reference:
S0378-7788(18)33143-8 https://doi.org/10.1016/j.enbuild.2019.01.042 ENB 9012
To appear in:
Energy & Buildings
Received date: Revised date: Accepted date:
9 October 2018 21 December 2018 31 January 2019
Please cite this article as: David P. Zurmuhl , Maciej Z. Lukawski , Gloria A. Aguirre , William R. Law , George P. Schnaars , Koenraad F. Beckers , C. Lindsay Anderson , Jefferson W. Tester , Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers, Energy & Buildings (2019), doi: https://doi.org/10.1016/j.enbuild.2019.01.042
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Highlights
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Geothermal heat pump (GHP) and hybrid GHP cooling systems operate more efficiently than airsource heat pump (ASHP) cooling systems. ASHP cooling systems tend to have lower total costs of ownerships than GHP and hybrid GHP cooling systems, but incentives can cover this difference in cost. GHP and hybrid GHP systems perform best in colder climates with higher ambient temperature variability.
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Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers David P. Zurmuhl1,2,3, Maciej Z. Lukawski1,3, Gloria A. Aguirre1,4, William R. Law1,5, George P. Schnaars1,3, Koenraad F. Beckers1,3,7, C. Lindsay Anderson1,6, Jefferson W. Tester1,3,4,* 1
Cornell Energy Systems Institute, Cornell University, Ithaca, NY 14853, USA Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA 3 Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA 4 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA 5 Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK 6 Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA 7 National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA *corresponding author: [email protected]
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Abstract
Geothermal heat pump Ground-source heat pump Techno-economic modeling Data center cooling Cooling dominated application Computer room air-conditioning Air-source heat pump Dry cooler Waterside economizer TRNSYS
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Keywords
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The technical and economic performance of hybrid geothermal heat pump (GHP) systems supplying year-round cooling to small data centers were analyzed and compared to air-source heat pump (ASHP) cooling systems. Numerical TRNSYS models were used to simulate the operation of five configurations that included GHPs, ASHPs, air-cooled heat exchangers – dry coolers (DCs), and/or waterside economizers (WSEs). The models were validated against data measured from an experimental GHP cooling system in Ithaca, NY, USA. The average coefficient of performance (COP) and the total cost of ownership (TCO) of the optimized systems were evaluated and compared to a base case ASHP cooling system. The results showed that the GHP systems had higher lifetime average COPs than the ASHP system, while the TCOs of the GHP systems were slightly higher than the ASHP system. The reductions in cooling capacity and performance of the geothermal systems due to subsurface temperature increases were calculated and showed that the addition of a DC or WSE mitigated capacity reductions and improved lifetime performance. The economic performance of the GHP systems relative to ASHP systems were analyzed using weather data from several locations across the U.S., with sensitivities to soil conductivity, price of electricity, and cost of drilling the geothermal wells quantified. GHP systems performed best in colder climates with greater temperature variability both seasonally and diurnally.
1. Introduction
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Geothermal (ground-source) heat pump (GHP) systems utilize the relatively uniform temperatures of the ground at depths up to 160 m as a heat source or heat sink to provide space heating, space cooling, and/or water heating. Depending on factors such as climate – particularly ambient surface temperatures, soil thermal properties, electricity price, costs of drilling geothermal wells, and availability of economic incentives, GHPs are often the most energy efficient and cost-effective systems for space heating and cooling.
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The IT equipment in data centers produces large amounts of heat and typically requires year-round cooling. About 40% of the energy consumed in a data center is used for cooling the IT equipment, which corresponds to about 0.5% of the world’s electricity demand (Song, Zhang, & Eriksson, 2015). This study focused on cooling for small telecommunications data centers that house a high density of electrical equipment associated with telecommunications operations. The most commonly used cooling technologies in these types of data centers rely on air-source heat pumps (ASHPs), which use the atmosphere as a heat sink. An alternative solution uses GHPs utilizing a set of vertical boreholes. This method is typically more efficient because the ground remains at a relatively constant temperature year-round, whereas the ambient air temperature fluctuates throughout the year. Although the initial cost of GHP systems is typically higher than that of ASHPs, the reduced electricity consumption of GHP systems over the course of their lifetimes can allow that cost to be recovered within a reasonable timeframe, while reducing the carbon footprint of these data centers.
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Unlike GHPs used in residential buildings for both space heating and cooling, GHP systems used for cooling data centers transfer heat to the subsurface year-round. In systems with conduction dominated subsurface conditions, this thermal imbalance leads to an increase in the temperature of the subsurface over the lifetime of the cooling system, which will reduce the efficiency and cooling capacity of the heat pumps. To mitigate the expected increase in subsurface temperatures, a supplementary air-cooled heat exchanger – dry cooler (DC) – can be added to the system to transfer heat generated by the IT equipment and stored in the subsurface to the atmosphere when ambient temperatures are sufficiently low. Another means of increasing the efficiency of a heat pump system is the addition of a waterside economizer (WSE). A waterside economizer eliminates the power consumption of the compressors in the heat pumps when the inlet temperature of the heat sink fluid to the heat pumps is sufficiently low, providing relatively low-cost cooling.
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There have been several studies pertaining to hybrid GHP systems for cooling dominated applications. The performance and optimization of space conditioning systems utilizing GHPs equipped with cooling towers to reconcile the thermal imbalance between heating and cooling seasons have been investigated for a hypothetical residential building in Hong Kong (Man, Yang, & Wang, 2010), an office building in Greece (Sagia, Rakopoulos, & Kakaras, 2012), a research facility in Oklahoma, USA (Gentry, Spitler, Fisher, & Xu, 2006), and several commercial buildings in China (Gang, Wang, & Wang, 2014; Yang, Xu, Hu, Zhu, & Chen, 2014; Fan, Gao, Hua, Deng, & Shi, 2014; Cui, Zhou, & Liu, 2015; Zhou, Cui, Li, & Liu, 2016). Zhou, Cui, Li, & Liu (2016) further investigated the addition of a chiller to a GHP system with a cooling tower. Other configurations that have been investigated include a GHP with a chiller for a residential building in Korea (Jeon, Lee, Hong, & Kim, 2010) and a GHP with borehole free cooling for a building in China (Yuan, et al., 2017). The use of cooling ponds (Ramamoorthy, Jin, Chiasson, & Spitler), pavement heating systems (Chiasson, Spitler, Rees, & Smith, 2000), and dry coolers (Johansson, 2012) as supplemental heat rejecters were investigated in three simulation studies. The incorporation of time-ofuse electricity pricing into a more economical control strategy of a hybrid GHP system employing ground
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pre-cooling has also been analyzed (Alaica & Dworkin, 2017). An experimental comparison of GHPs to ASHPs was conducted for one year of operation in an academic building in Spain (Urchueguía, et al., 2008) and models of several combinations of GHPs, ASHPs, and thermal storage devices were used to analyze the performance of different space conditioning systems in an office building in Spain (Pardo, Montero, Martos, & Urchueguía, 2010).
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An earlier study comparing the performance and economics of different cooling systems for cellular tower shelters (including GHPs and ASHPs equipped with DCs and/or air-economizers) was conducted in our group and is documented in Beckers, Aguirre, & Tester (2018) and Aguirre et al. (2017). The study utilized numerical models validated against data recorded at a Verizon cellular tower demonstration site in Varna, NY. Using available climate and hydrogeological data, the performance of the cooling systems for cellular tower shelters in several U.S. cities were evaluated. The main outcome of this study was that in most cases, an ASHP combined with an air-economizer provided the lowest total cost of ownership (TCO), while a GHP combined with an air-economizer provided the lowest lifetime electricity consumption and the smallest carbon footprint.
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2. Objective
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This paper provides a techno-economic assessment and performance comparison of GHP, hybrid GHP, and ASHP cooling systems. The analysis utilized numerical models of the cooling systems that were calibrated and validated using data from an experimental hybrid GHP cooling system recently installed at a small Verizon telecommunications data center in Ithaca, NY, USA. This paper aims to provide guidelines for the design and operation of hybrid GHP cooling systems for similar telecommunications data centers. The impact of location dependent technical and economic parameters is also quantified through a sensitivity analysis. Lastly, an evaluation of the policy solutions required to drive the market growth of GHP cooling systems is provided. The novelty of this study is the evaluation of a new coolingonly hybrid GHP application and a generalized sensitivity analysis of the performance of these systems to a number of technical and economic parameters. The work presented in this paper is based in part on preliminary results from our group documented in Zurmuhl et al. (2018).
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Section 3 of this paper discusses the methods of experimental data collection, simulation of the operation of the cooling systems, validation of the numerical models, and economic analysis of the systems. A case study of the technical and economic performance of the cooling systems located in Ithaca, NY is presented in Section 4. An analysis of the sensitivity of the relative TCOs of the systems to ambient temperature, soil conductivity, price of electricity, and cost of drilling is presented in Section 5. The results of the case study and sensitivity analysis are further discussed in Section 6.
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3. Methodology
3.1 Experimental GHP Cooling System An experimental hybrid GHP cooling system was installed in a telecommunications data center in Ithaca, NY with an approximate equipment cooling load of 14.5 kWth. The system was equipped with a comprehensive data acquisition system to record relevant temperature, flowrate, control, and power consumption data at 5-minute intervals. The cooling system presented in the schematic in Figure 1 consists of two loops, each with a pump circulating a propylene glycol solution. The two loops are connected by a heat exchanger that transfers heat from the building loop to the subsurface loop. The
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building loop circulates fluid between two 35.2 kWth (10 tons of cooling each) water-to-air GHPs (ClimateMaster TCV120), a DC, and the heat exchanger. The subsurface loop circulates fluid between the heat exchanger and the geothermal well field. The geothermal well field consists of three sets of boreholes connected in parallel, each set consisting of three 139 m boreholes connected in series. The nine borehole heat exchangers (BHEs) have a total length of 1248 m.
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The cooling system is designed to maintain the server room temperature between 25.6°C and 27.8°C (78°F to 82°F), and the two GHPs can each operate in part load or full load. The combined cooling capacity of the heat pumps is 70.4 kWth – almost five times greater than the current equipment cooling load of 14.5 kWth – to provide backup capacity and to facilitate possible future expansion of the data center. The DC operates at full capacity when the temperature difference between the glycol entering the DC and the ambient air ΔTDC is greater than 4.4°C (8°F) and the temperature of the glycol leaving the DC is greater than 1.7°C (35°F). Otherwise the DC is bypassed and its fan is switched off.
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Data Center Building Building Circulation Pump
Borehole Heat Exchangers (BHEs)
Wellfield Circulation Pump
Indoor air E-3 Geothermal Heat Pump
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Heat Exchanger
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Dry Cooler (DC)
Ambient air
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Figure 1: Simplified schematic of the hybrid GHP cooling system at the experimental data center site. 3.2 Numerical TRNSYS Model
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Long-term operation of the GHP system was modeled using TRNSYS – Transient System Simulation Tool, a software environment developed to simulate performance of thermal and electrical energy systems (Klein, et al., 2014). TRNSYS includes an extensive library of components that can be used to model the performance of each part of the system of interest. Operation of the data center cooling systems was simulated using a 10-minute time step in TRNSYS to produce predicted system performance for 20 years. The performance of the ClimateMaster TCV120 heat pumps units was modeled using tables provided by the manufacturer, which were implemented in the TRNSYS model (ClimateMaster, 2017). The tables provided cooling and electricity consumption data for discrete input values of temperature and flowrate of the entering glycol-water mixture as well as the temperature, humidity, and flowrate of the entering indoor air. The TRNSYS conditioning equipment component (type 42) interpolates between these values to determine heat pump performance at each time step. Only full load operation was modeled due to a
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lack of performance data for part load operation. The DC was modeled using the counter flow heat exchanger component (type 5). The model uses the specified heat exchanger coefficient as input to calculate the heat exchanger effectiveness. The server room in the data center was modeled using the lumped capacitance building component (type 88), which simulates the building as a single zone. The model assumes constant heat gains from equipment, neglects solar heat gains, and lumps heat transfer through the building envelope and ventilation into an overall heat loss coefficient. The annual ambient temperature inputs to this model came from a typical meteorological year v3 (TMY3) file for the Elmira Regional Airport which is located about 30 miles south of Ithaca (NREL, 2005). TMY3 data represent the hourly weather conditions for a typical meteorological year based on multiple years of recorded data (Wilcox & Marion, 2008). The glycol heat exchanger was modeled using the effectiveness heat exchanger component (type 91). This model uses a specified value of heat exchanger effectiveness to calculate the heat transfer rate between the two circulation loops (Klein, et al., 2014).
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The thermal behavior of the BHEs was simulated in MATLAB using a previously validated semi-analytical slender-body heat transfer model (Beckers K. F., 2016). The TRNSYS model called the MATLAB script at each time step to calculate the outlet temperature of the BHEs. The thermal conductivity and diffusivity of the soil were obtained from a BHE thermal response test performed at the site and specified as 3.3 W/(m∙K) and 0.90×10-6 m2/s respectively. The far-field temperature of the subsurface was assumed to be the average ambient temperature calculated from the TMY3 data (9.0°C for Ithaca).
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Heat pump operation was controlled using the five-stage room thermostat component (type 108). This component controls two stages of cooling and was set so that the first heat pump would begin operation if the indoor temperature went above 25.6°C (78°F) and the second heat pump would switch on if the indoor temperature exceeded 28.1°C (82.5°F). DC operation was controlled using the differential controller component (type 2). The controller used a specified temperature dead band to determine the fan control signal based on the difference between the temperatures of the entering glycol solution and the ambient air ΔTDC. The circulation pumps were assumed to be in operation if either one of the heat pumps or the DC were operating (Klein, et al., 2014).
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The model of the hybrid system (GHP+DC) was modified to represent a GHP system without a DC by setting the DC permanently off. A model of an ASHP system was developed using the GHP+DC model with the glycol heat exchanger and subsurface loop components removed. The use of waterside economizers (WSEs) was also investigated for both GHP and ASHP systems. WSEs can be used to reduce electricity consumption when the ambient temperature is low enough that the DC can cool the glycol to a sufficiently low temperature. The cool glycol bypasses the GHP unit and instead flows through the coils of the WSE that cool the building air directly, eliminating the power consumption of the compressors in the heat pumps. Models of a hybrid GHP system with a waterside economizer (GHP+WSE) and an ASHP system with a waterside economizer (ASHP+WSE) were developed by modifying the GHP+DC and ASHP models respectively. The models were augmented to include conditioning equipment components (type 42) to interpolate waterside economizer performance data provided by the manufacturer (ClimateMaster, 2017) to determine the performance of the waterside economizer at each time step. The controller was updated so during periods when the ambient temperature was within a specified range, power consumption of the heat pump compressors would be set to zero and the heat transfer from the room to the circulating glycol would be determined by the WSE performance, rather than heat pump performance.
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3.3 Validation of TRNSYS Model
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The numerical TRNSYS model was calibrated and validated using data recorded at the experimental site between August 11, 2017 and September 27, 2017. Average flowrates were calculated for both the subsurface and the building loops and used in the TRNSYS model. An average value of heat gains from the IT equipment was determined based on the measurements of flowrate in the building loop and the temperatures at the inlet and outlet of the heat pumps. Only measurements at times when the difference between the indoor and ambient temperatures was less than 0.5°C were used so that heat transfer to or from the atmosphere was minimized. The inlet and outlet temperatures of the DC were used to determine its heat transfer coefficient. Similarly, the inlet and outlet temperatures of the glycol heat exchanger were used to determine its heat transfer effectiveness. It was not feasible to calculate the building heat loss coefficient from the data recorded at any single point in time due to the sensitivity of this parameter to the fluctuating cooling load. Therefore, the cooling load data was plotted and a value of the building heat loss coefficient was determined that aligned the TRNSYS results most closely with the cooling load recorded at the experimental site.
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The simulated behavior of the system was validated against the data recorded at the experimental site. For consistency, the ambient temperature input to the TRNSYS model from the TMY3 file was replaced with the recorded local ambient temperature. The data from the experimental site was compared to the TRNSYS results for several important parameters including the heat transfer rate to the BHEs shown in Figure 2 and the heat removed from the data center by the heat pumps shown in Figure 3. The data were displayed as binned averages to eliminate the noise associated with the on/off fluctuations of the cooling systems.
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Figures 2 and 3 validate the ability of the TRNSYS model to simulate the trends in the experimental data, however there were limitations in the experimental data that prevented an even better agreement between the two data sets. The first was that the experimental cooling system began operation before the data acquisition system was installed and began operating. Therefore, a significant amount of heat had been transferred to the experimental BHEs by the time data recording began. This caused the temperature in the experimental BHEs to be higher than was predicted by TRNSYS for the first 800 hours of the simulation until the short-term transient effects in the subsurface became less pronounced. We also lacked performance data for modeling partial load operation of the heat pumps and could not model short-term changes to the cooling load from equipment or changes to the building envelope, such as when a door was opened. These factors likely caused some discrepancy between the two data sets.
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Figure 2: Borehole heat transfer rate (QBHE) vs. time recorded at experimental site from August 11, 2017 to September 27, 2017 and simulated in TRNSYS.
Figure 3: Heat removed from data center by heat pumps (QHP) vs. time recorded at experimental site from August 11, 2017 to September 27, 2017 and simulated in TRNSYS.
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Nonetheless, the TRNSYS model was shown to accurately represent the performance of the experimental system using several methods. First, the BHE heat transfer model was previously validated in the cellular tower shelter cooling study performed by our group (Beckers, Aguirre, & Tester, 2018; Beckers K. F., 2016). Also, the modeling parameters for the components of the system were calibrated based on the experimental data as described earlier in this section. Third, the averages of the rates of heat transfer to the BHEs over the validation period are within 1% of one another for the experimental and simulated data sets. The rates of heat removed from the building by the heat pumps over the validation period are within 3% of one another for the two data sets. Based on these factors, we expect that the TRNSYS model accurately predicts yearly and lifetime performance of the cooling systems. 3.4 Economic Analysis
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The economic performance of the five types of cooling systems was compared using their total costs of ownership (TCO) defined as a sum of the present values of the capital costs, operation costs (including electricity purchased), and maintenance costs of the systems over their expected lifetimes (20 years). The cost information used for the analysis is presented in Table 1. All cost data in this paper is provided in 2017 USD. While the various project costs will vary from site to site, the main capital cost difference between the geothermal and air-source systems is due to the cost of drilling and installing the subsurface loop. The total cost of the subsurface loop was estimated based on regional average costs per foot of BHEs in the U.S. (Battocletti & Glassley, 2013). The main operational cost differences between geothermal and air-source systems are due to differences in costs of electricity consumption. Electricity prices were based on state and national average rates for commercial customers in the U.S. (EIA, 2017). TCO was calculated assuming a real (i.e. inflation adjusted) discount rate of 5%. The base economic analyses provide relatively conservative estimates of TCO since economic incentives such as investment tax credits or government grants, feed-in tariffs for efficiency improvements, carbon credits, or potential drilling cost improvements were not accounted for. Table 1: Cost data used in the economic analysis given in 2017 USD and based on actual capital and maintenance costs from the experimental site in Ithaca, NY.
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Cost Component
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Heat pump units (2) Dry cooler Waterside economizer Circulation pumps and piping Ductwork and insulation Control equipment Commissioning and air balance Project management Overhead and profit Heat pump maintenance Dry cooler maintenance
GHP
Cost for Each Case GHP+DC GHP+WSE ASHP+WSE $30,000 $7,500 $2,100 $10,000
$5,000 $17,000
$6,500
$5,000 $2,500 $4,000 $11,000 $300/year $500/year
ASHP
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4. Ithaca, NY Case Study
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The values of total BHE length and DC temperature set point in the validated TRNSYS model were optimized to 760 m and 20°C respectively to provide the lowest TCOs. Technical performance of the systems was analyzed using two metrics: the average coefficient of performance (COP) and the percent reduction in capacity of the heat pumps over the 20-year lifetime. COP is defined as the ratio of the heat removed from the building by the cooling system to the electrical input to the system including the heat pump compressors, the building and subsurface circulation pumps, and the dry cooler fan. Subsurface temperature increases are a concern for data center GHP cooling systems because higher subsurface temperatures will diminish the total capacity of the heat pumps. The effect of the DCs in reducing subsurface temperature increases in the GHP+DC and GHP+WSE cases was quantified using the percent reduction in capacity of the systems from year 1 to year 20 and was compared to the capacity reductions for the GHP case.
Figure 4: Yearly average coefficient of performance (COP) for each cooling system configuration in Ithaca, NY.
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The yearly average COPs for each cooling system are plotted in Figure 4 and the lifetime average COPs are presented in Table 2. It should be noted that these average COP data are based on systems with economically optimized BHE lengths and dry cooler set points and that much higher COPs for geothermal systems could be achieved with greater BHE lengths (Zurmuhl, et al., 2018). The yearly average COPs of the geothermal systems diminish over the 20-year lifetimes of the systems, but are still higher than the yearly average COPs of the air-source systems even in year 20. The COP reductions in the GHP+DC and GHP+WSE systems are much less pronounced than those in the GHP system due to the supplementary heat rejection to the atmosphere by the DC. The yearly average COPs of the GHP+DC system are lower than those of the GHP system in the first two years due to the additional power draw
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of the DC, but the benefits of the DC are shown in later years when the yearly average COPs are higher for the GHP+DC than for the GHP due to lower subsurface temperatures. Furthermore, the reductions in capacity of the heat pumps for the GHP+DC and GHP+WSE systems are significantly less than those in the GHP system as shown in Table 2. The capacity reductions of the GHP+DC case were less than those of the GHP+WSE case, because the DC works to thermally recharge the subsurface in the winter in the GHP+DC case, but not in the GHP+WSE case. However, even though the temperature of the soil increased more significantly for the GHP+WSE case, the system had a higher lifetime average COP due to the reduced electricity consumption of the system while operating the WSE during periods of low ambient temperature. Table 2: Lifetime average COPs of each cooling systems and the reduction in the combined capacity of the geothermal heat pumps from year 1 to year 20. Minimum capacity in year 1 [kWth] 67.6 67.9 67.8 -
Minimum capacity in year 20 [kWth] 62.5 65.6 63.8 -
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GHP GHP+DC GHP+WSE ASHP+WSE ASHP
Lifetime average COP 2.84 2.99 3.16 2.64 2.54
Percent reduction 7.5% 3.4% 5.9% -
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The TCOs of each system based on the economic assumptions presented in Section 3.4 including a state average electricity price of 15.03¢/kWh and a regional average drilling cost of $52.59/m are plotted in blue in Figure 5. Under these assumptions, the reduced electricity consumption of the geothermal systems was not enough to recover the additional capital cost of the geothermal systems compared to the ASHP. However, the calculations of these TCOs did not account for any type of incentives and therefore provide fairly conservative estimates.
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In a separate part of this study, the effect of economic incentives on the TCO of the geothermal systems was analyzed in the form of an investment tax credit (ITC). An ITC effectively reduces the capital cost of the system by a specified percentage. The levels of ITCs required for the GHP, GHP+DC, and GHP+WSE systems to break even with the ASHP system are 20%, 27%, and 29% respectively, which are within the range of incentives that are currently available. For example, similar GHP installations in Ithaca, NY could qualify for a 10% ITC from the federal government (DSIRE, 2018) as well as a $24,000 rebate (equivalent to an additional 18-20% of the capital cost) from the New York State Energy Research and Development Authority (NYSERDA, 2018). The TCOs of the cooling systems with these incentives applied are plotted in orange in Figure 5. With incentives included, the TCOs of the GHP and GHP+DC systems are 7% and 1% less than the TCO of the ASHP systems respectively and the TCO of the GHP+WSE is only 1% greater.
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Figure 5: Total cost of ownership (TCO) of each cooling system in Ithaca, NY including and not including state and federal incentives.
5. Sensitivity Analysis
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5.1 Ambient Temperature
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The models of the cooling systems in Ithaca, NY were modified in order to analyze the sensitivities of the relative TCOs of the systems to different ambient temperature inputs. TMY3 datasets for twelve locations in the U.S. were selected to represent a spectrum of the mean and variability of ambient temperature. The locations represented by the TMY3 data and their associated ambient temperature statistics are presented in Table 3. The cooling degree days of each location are also provided in Table 3, but only as a reference, as cooling of the data center would occur year-round, even when the outdoor ambient temperature is less than the temperature of the indoor air. The performance of the cooling systems was simulated using each of the twelve TMY3 datasets as ambient temperature inputs. The purpose of these simulations was not to represent the actual performance of these systems in the specific locations represented by the TMY3 datasets. Rather, the intention was to use the TMY3 datasets to represent the same cooling systems with differing average values and standard deviations of the ambient temperature input in order to evaluate the sensitivities of the relative TCOs to these parameters. Therefore, base case values of soil conductivity (2.6 W/(m∙K)), price of electricity (11¢/kWh), and cost of drilling ($48/m) were selected and used as inputs to the TRNSYS models and economic analysis. The total BHE length and dry cooler set point inputs were optimized for each TMY3 input.
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Table 3: Locations represented by the TMY3 datasets used in the sensitivity study. Location
Average Tamb [°C]
Standard deviation of Tamb [°C]
A B C D E F G H I J K L
Juneau, AK Minneapolis, MN Ithaca, NY Denver, CO Seattle, WA Philadelphia, PA Sacramento, CA San Diego, CA Dallas, TX Las Vegas, NV New Orleans, LA Miami, FL
5.5 7.7 9.0 10.9 11.2 12.7 15.5 17.7 18.7 19.8 20.4 24.5
7.6 12.9 9.7 10.9 6.0 10.3 7.8 3.8 9.8 10.6 7.6 4.5
Cooling degree days [°C·days] 22 494 284 620 161 707 774 452 1573 1927 1606 2385
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The results of the ambient temperature sensitivity study are plotted in Figures 6, 7, and 8 along with surface fits of the data. The data points on the plots are labeled based on the TMY3 file used to simulate the system performance reflected by each data point. The relative performance of the ASHP+WSE is not plotted, because the TCO of the ASHP+WSE systems was higher than the TCO of the ASHP systems for all weather inputs analyzed and the addition of a WSE to an ASHP system provided minimal efficiency improvements.
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The data in Figures 6, 7, and 8 suggest that GHP systems with and without hybridization perform better relative to ASHP systems in colder climates with higher temperature variability. One reason the geothermal systems perform better in colder climates is because the initial COPs of these systems are higher than those in warmer climates due to the lower initial ground temperature. Also, the COPs of the heat pumps decrease more significantly with respect to water/glycol inlet temperature as the water/glycol inlet temperature increases. Therefore, the systems in colder climates where the initial ground temperature is lower experience less significant efficiency reductions over their lifetimes. ASHP systems perform much more poorly in highly variable climates for the same reason. The heat pumps in geothermal systems in climates with highly variable ambient temperatures benefit from minimal diurnal or seasonal fluctuations in inlet temperature. However, ASHP systems in similar climates do not have this benefit and will have high inlet temperatures to the heat pump units during periods when the ambient temperature is high.
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Figure 6: Difference in TCO between GHP and ASHP systems as a percentage of the TCO of the ASHP systems with respect to the average and standard deviation of the ambient temperature inputs.
Figure 7: Difference in TCO between GHP+DC and ASHP systems as a percentage of the TCO of the ASHP systems with respect to the average and standard deviation of the ambient temperature inputs.
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Figure 8: Difference in TCO between GHP+WSE and ASHP systems as a percentage of the TCO of the ASHP systems with respect to the average and standard deviation of the ambient temperature inputs.
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Overall, the GHP systems performed better economically compared to the GHP+DC and GHP+WSE systems, suggesting that the additional capital cost of DCs or WSEs is not recovered through reduced electricity consumption in most cases. In fact, in some of the hotter climates, the use of the WSE in the GHP+WSE systems was so minimal that there was little impact on the efficiency of the system at all. These results could potentially change if differently sized DCs were considered. 5.2 Soil Conductivity, Price of Electricity, and Cost of Drilling
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The sensitivities of the relative TCOs of the systems to changes in soil conductivity, price of electricity, and cost of drilling were also analyzed for each of the twelve TMY3 input datasets. The values of each of these parameters were modified by ±20% to demonstrate the relative impacts each of these parameters had on the difference in TCO between the geothermal systems and the ASHP system (ΔTCO). The effects were fairly consistent regardless of ambient temperature input. The results are presented in Table 4 as the percent change to ΔTCO averaged for all of the weather inputs. Our decision to vary these parameters by ±20% is not necessarily representative of the range of values found in the U.S. This percentage was chosen for comparison of the impacts of each of these parameters on the economic performance of the systems. Of the three parameters shown in Table 4, cost of drilling had the most significant impact on ΔTCO. Price of electricity may have a significant impact on the TCOs of the individual systems, but due to the fact that the TCOs of both geothermal and air-source systems are impacted by an increase or decrease in electricity price, the impact to ΔTCO is minimal. Soil conductivity had a relatively large impact on the GHP systems, but because the GHP+DC and GHP+WSE systems do not rely solely on the ground as a heat sink, the impact is less significant for those systems. Also, while the effect of increasing or
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decreasing the price of electricity or cost of drilling on the ΔTCOs seems fairly linear, the value of increasing soil conductivity diminishes as soil conductivity increases. Table 4: Sensitivity of the difference in total cost of ownership (ΔTCO) between geothermal systems and ASHP systems to variations in soil conductivity, price of electricity, and cost of drilling.
Soil conductivity Price of electricity Cost of drilling
Percent change to input parameter -20% +20% -20% +20% -20% +20%
GHP +20% -12% +2% -3% -27% +27%
GHP+WSE +10% -6% +3% -4% -19% +20%
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5.3 Economic Incentives
Percent change to ΔTCO GHP+DC +4% -3% +4% -5% -21% +22%
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Parameter
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For the base case assumptions, GHP systems require between 20% and 30% ITCs to break even with ASHP systems, as shown in Figure 9. These levels of ITCs are reasonable and attainable through a combination of state and federal policies. In fact, ITCs within this range are currently available in New York State as discussed in Section 4. However, these results could vary widely based on the soil conductivity, price of electricity, and cost of drilling for different sites around the U.S. The necessary ITCs could also decrease with improvements to drilling technologies, implementation of carbon cap and trade, or the economy of scale resulting from an increased adoption of GHPs.
Figure 9: Level of investment tax credits (ITCs) required for GHP systems to break even with ASHP systems with respect to the average and standard deviation of ambient temperature.
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6. Discussion
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The results show that even for the examined cases with less ideal input parameters for GHP performance (i.e. high ambient temperature, low temperature variability, and low soil conductivity, low price of electricity, or high drilling costs), there are still geothermal systems that outperform ASHP systems in terms of average COP. The simulated performance is limited by the assumptions used and the results should be treated as a base case. For example, the total BHE lengths of the geothermal systems were optimized for minimal TCO. However, in cases where incentives could provide a significantly lower TCO for an economically optimized geothermal system than for an ASHP, it could make sense to increase the total BHE length, further increasing COP while maintaining TCO below that of an ASHP system. Also, due to computational limitations, the total BHE length optimized for GHP cases was used in the GHP+DC cases and only the DC set point was optimized for. In an actual GHP+DC system, there would be a tradeoff between total BHE length drilled and lifetime DC usage/electricity consumption that would need to be accounted for in the design of the system.
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As mentioned in Section 5.2, the sensitivities of soil conductivity, price of electricity, and cost of drilling were varied by ±20% in order to demonstrate their relative impacts on economic performance. Nationally representative values for soil conductivity, however, could reasonably vary more than ±20% (Aguirre, 2018). Values of cost of drilling the BHEs vary approximately ±10% across the U.S. (Battocletti & Glassley, 2013) and the price of electricity can vary significantly more than ±20%, particularly in the positive direction for states such as California or Connecticut (EIA, 2017).
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Future work that would be beneficial to this study includes an analysis of GHP+WSE systems that utilize the DC and heat pump compressors at a specified range of outdoor temperatures. This configuration may allow for higher average COPs than those reported for the GHP+DC or GHP+WSE configurations investigated in this study. Also, the subsurface temperature increase and heat pump capacity reductions could be further investigated for GHP data center cooling systems that integrate other heating needs. Such systems could be thermally balanced without the parasitic electricity consumption of a DC.
7. Conclusions
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This paper evaluated the technical and economic performance of heat pumps used for cooling small telecommunications data centers. Numerical models developed in TRNSYS software were validated using an experimental hybrid geothermal heat pump cooling system located in Ithaca, NY, USA. Using the validated models with optimized input values of total BHE length and dry cooler set point, a case study of the performance of five cooling system configurations including GHPs, ASHPs, DCs, and/or WSEs was performed. The results showed that the GHP and hybrid GHP systems had higher lifetime average COPs and that the DCs and WSEs mitigated capacity reductions due to subsurface temperature increases and improved lifetime average COP. Although the base estimations of the TCOs of the geothermal systems were higher than the TCOs of the air-source systems, the levels of incentives required to reconcile these cost differences were within the range of incentives available in New York State. A sensitivity analysis of the TCOs of the geothermal systems relative to the ASHP systems to the ambient temperature, soil conductivity, price of electricity, and cost of drilling the geothermal wells was also performed using weather inputs from locations across the U.S. and selected base case technical and economic input parameters. GHP systems with and without hybridization performed better than ASHP systems in colder climates with higher temperature variability. The geothermal systems benefit in colder
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climates from the lower initial ground temperature and less significant efficiency reductions over the course of their lifetimes. The geothermal systems in climates with high ambient temperature variability benefit from minimal short-term fluctuations in the inlet temperature to the heat pump units. Among the other three parameters analyzed, cost of drilling had the greatest impact on the relative economic performance of the geothermal systems compared to the ASHP systems.
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This study demonstrates the ability of the presented numerical model to quantitatively characterize the technical and economic performance of GHP and hybrid GHP cooling systems under a range of conditions. The results of the study illustrate the significance of the effects of location dependent technical and economic parameters on the performance of the cooling systems. The numerical model provides a versatile tool that could be implemented in the design of future GHP cooling systems to optimize for the conditions present at a particular telecommunications data center.
Acknowledgements
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The authors would like to gratefully acknowledge Verizon Communications for their major financial support of this work. The authors would also like to thank Charles Kellum, David Hampton, and Sean Clements from Verizon, Caren Rubin from Labella Associates, Rodney Scouten from Day Automation Systems, Chris Lesperance from Lono Mechanical, and Thomas Piekunka from RF Peck for their collaboration and advice on this project. The Cornell Energy Systems Institute and the Atkinson Center for a Sustainable Future are also acknowledged for their partial support.
List of Abbreviations
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air-source heat pump air-source heat pump with waterside economizer borehole heat exchanger coefficient of performance dry cooler geothermal heat pump geothermal heat pump with dry cooler geothermal heat pump with dry cooler and waterside economizer information technology investment tax credit [%] total cost of ownership [$] typical meteorological year version 3 Transient Systems Simulation Tool (Klein, et al., 2014) waterside economizer temperature difference between the ambient air and the glycol/water mixture at the inlet to the dry cooler [°C] difference in total cost of ownership between geothermal system and ASHP system [$]
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ASHP ASHP+WSE BHE COP DC GHP GHP+DC GHP+WSE IT ITC TCO TMY3 TRNSYS WSE ΔTDC ΔTCO
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Yuan, T., Ding, Y., Zhang, Q., Zhu, N., Yang, K., & He, Q. (2017). Thermodynamic and economic analysis for ground-source heat pump system coupled with borehole free cooling. Energy and Buildings, 185-197. Zhou, S., Cui, W., Li, Z., & Liu, X. (2016). Feasibility study on two schemes for alleviating the underground heat accumulation of the ground source heat pump. Sustainable Cities and Society, 1-9.
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Zurmuhl, D. P., Lukawski, M. Z., Aguirre, G. A., Schnaars, G. P., Beckers, K. F., Anderson, C. L., & Tester, J. W. (2018). Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers. Stanford Geothermal Workshop. Stanford, CA, USA.
Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers David P. Zurmuhl , Maciej Z. Lukawski , Gloria A. Aguirre , William R. Law , George P. Schnaars , Koenraad F. Beckers , C. Lindsay Anderson , Jefferson W. Tester PII: DOI: Reference:
S0378-7788(18)33143-8 https://doi.org/10.1016/j.enbuild.2019.01.042 ENB 9012
To appear in:
Energy & Buildings
Received date: Revised date: Accepted date:
9 October 2018 21 December 2018 31 January 2019
Please cite this article as: David P. Zurmuhl , Maciej Z. Lukawski , Gloria A. Aguirre , William R. Law , George P. Schnaars , Koenraad F. Beckers , C. Lindsay Anderson , Jefferson W. Tester , Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers, Energy & Buildings (2019), doi: https://doi.org/10.1016/j.enbuild.2019.01.042
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Highlights
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Geothermal heat pump (GHP) and hybrid GHP cooling systems operate more efficiently than airsource heat pump (ASHP) cooling systems. ASHP cooling systems tend to have lower total costs of ownerships than GHP and hybrid GHP cooling systems, but incentives can cover this difference in cost. GHP and hybrid GHP systems perform best in colder climates with higher ambient temperature variability.
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Hybrid Geothermal Heat Pumps for Cooling Telecommunications Data Centers David P. Zurmuhl1,2,3, Maciej Z. Lukawski1,3, Gloria A. Aguirre1,4, William R. Law1,5, George P. Schnaars1,3, Koenraad F. Beckers1,3,7, C. Lindsay Anderson1,6, Jefferson W. Tester1,3,4,* 1
Cornell Energy Systems Institute, Cornell University, Ithaca, NY 14853, USA Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA 3 Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA 4 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA 5 Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK 6 Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA 7 National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA *corresponding author: [email protected]
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Abstract
Geothermal heat pump Ground-source heat pump Techno-economic modeling Data center cooling Cooling dominated application Computer room air-conditioning Air-source heat pump Dry cooler Waterside economizer TRNSYS
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Keywords
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The technical and economic performance of hybrid geothermal heat pump (GHP) systems supplying year-round cooling to small data centers were analyzed and compared to air-source heat pump (ASHP) cooling systems. Numerical TRNSYS models were used to simulate the operation of five configurations that included GHPs, ASHPs, air-cooled heat exchangers – dry coolers (DCs), and/or waterside economizers (WSEs). The models were validated against data measured from an experimental GHP cooling system in Ithaca, NY, USA. The average coefficient of performance (COP) and the total cost of ownership (TCO) of the optimized systems were evaluated and compared to a base case ASHP cooling system. The results showed that the GHP systems had higher lifetime average COPs than the ASHP system, while the TCOs of the GHP systems were slightly higher than the ASHP system. The reductions in cooling capacity and performance of the geothermal systems due to subsurface temperature increases were calculated and showed that the addition of a DC or WSE mitigated capacity reductions and improved lifetime performance. The economic performance of the GHP systems relative to ASHP systems were analyzed using weather data from several locations across the U.S., with sensitivities to soil conductivity, price of electricity, and cost of drilling the geothermal wells quantified. GHP systems performed best in colder climates with greater temperature variability both seasonally and diurnally.
1. Introduction
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Geothermal (ground-source) heat pump (GHP) systems utilize the relatively uniform temperatures of the ground at depths up to 160 m as a heat source or heat sink to provide space heating, space cooling, and/or water heating. Depending on factors such as climate – particularly ambient surface temperatures, soil thermal properties, electricity price, costs of drilling geothermal wells, and availability of economic incentives, GHPs are often the most energy efficient and cost-effective systems for space heating and cooling.
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The IT equipment in data centers produces large amounts of heat and typically requires year-round cooling. About 40% of the energy consumed in a data center is used for cooling the IT equipment, which corresponds to about 0.5% of the world’s electricity demand (Song, Zhang, & Eriksson, 2015). This study focused on cooling for small telecommunications data centers that house a high density of electrical equipment associated with telecommunications operations. The most commonly used cooling technologies in these types of data centers rely on air-source heat pumps (ASHPs), which use the atmosphere as a heat sink. An alternative solution uses GHPs utilizing a set of vertical boreholes. This method is typically more efficient because the ground remains at a relatively constant temperature year-round, whereas the ambient air temperature fluctuates throughout the year. Although the initial cost of GHP systems is typically higher than that of ASHPs, the reduced electricity consumption of GHP systems over the course of their lifetimes can allow that cost to be recovered within a reasonable timeframe, while reducing the carbon footprint of these data centers.
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Unlike GHPs used in residential buildings for both space heating and cooling, GHP systems used for cooling data centers transfer heat to the subsurface year-round. In systems with conduction dominated subsurface conditions, this thermal imbalance leads to an increase in the temperature of the subsurface over the lifetime of the cooling system, which will reduce the efficiency and cooling capacity of the heat pumps. To mitigate the expected increase in subsurface temperatures, a supplementary air-cooled heat exchanger – dry cooler (DC) – can be added to the system to transfer heat generated by the IT equipment and stored in the subsurface to the atmosphere when ambient temperatures are sufficiently low. Another means of increasing the efficiency of a heat pump system is the addition of a waterside economizer (WSE). A waterside economizer eliminates the power consumption of the compressors in the heat pumps when the inlet temperature of the heat sink fluid to the heat pumps is sufficiently low, providing relatively low-cost cooling.
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There have been several studies pertaining to hybrid GHP systems for cooling dominated applications. The performance and optimization of space conditioning systems utilizing GHPs equipped with cooling towers to reconcile the thermal imbalance between heating and cooling seasons have been investigated for a hypothetical residential building in Hong Kong (Man, Yang, & Wang, 2010), an office building in Greece (Sagia, Rakopoulos, & Kakaras, 2012), a research facility in Oklahoma, USA (Gentry, Spitler, Fisher, & Xu, 2006), and several commercial buildings in China (Gang, Wang, & Wang, 2014; Yang, Xu, Hu, Zhu, & Chen, 2014; Fan, Gao, Hua, Deng, & Shi, 2014; Cui, Zhou, & Liu, 2015; Zhou, Cui, Li, & Liu, 2016). Zhou, Cui, Li, & Liu (2016) further investigated the addition of a chiller to a GHP system with a cooling tower. Other configurations that have been investigated include a GHP with a chiller for a residential building in Korea (Jeon, Lee, Hong, & Kim, 2010) and a GHP with borehole free cooling for a building in China (Yuan, et al., 2017). The use of cooling ponds (Ramamoorthy, Jin, Chiasson, & Spitler), pavement heating systems (Chiasson, Spitler, Rees, & Smith, 2000), and dry coolers (Johansson, 2012) as supplemental heat rejecters were investigated in three simulation studies. The incorporation of time-ofuse electricity pricing into a more economical control strategy of a hybrid GHP system employing ground
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pre-cooling has also been analyzed (Alaica & Dworkin, 2017). An experimental comparison of GHPs to ASHPs was conducted for one year of operation in an academic building in Spain (Urchueguía, et al., 2008) and models of several combinations of GHPs, ASHPs, and thermal storage devices were used to analyze the performance of different space conditioning systems in an office building in Spain (Pardo, Montero, Martos, & Urchueguía, 2010).
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An earlier study comparing the performance and economics of different cooling systems for cellular tower shelters (including GHPs and ASHPs equipped with DCs and/or air-economizers) was conducted in our group and is documented in Beckers, Aguirre, & Tester (2018) and Aguirre et al. (2017). The study utilized numerical models validated against data recorded at a Verizon cellular tower demonstration site in Varna, NY. Using available climate and hydrogeological data, the performance of the cooling systems for cellular tower shelters in several U.S. cities were evaluated. The main outcome of this study was that in most cases, an ASHP combined with an air-economizer provided the lowest total cost of ownership (TCO), while a GHP combined with an air-economizer provided the lowest lifetime electricity consumption and the smallest carbon footprint.
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2. Objective
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This paper provides a techno-economic assessment and performance comparison of GHP, hybrid GHP, and ASHP cooling systems. The analysis utilized numerical models of the cooling systems that were calibrated and validated using data from an experimental hybrid GHP cooling system recently installed at a small Verizon telecommunications data center in Ithaca, NY, USA. This paper aims to provide guidelines for the design and operation of hybrid GHP cooling systems for similar telecommunications data centers. The impact of location dependent technical and economic parameters is also quantified through a sensitivity analysis. Lastly, an evaluation of the policy solutions required to drive the market growth of GHP cooling systems is provided. The novelty of this study is the evaluation of a new coolingonly hybrid GHP application and a generalized sensitivity analysis of the performance of these systems to a number of technical and economic parameters. The work presented in this paper is based in part on preliminary results from our group documented in Zurmuhl et al. (2018).
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Section 3 of this paper discusses the methods of experimental data collection, simulation of the operation of the cooling systems, validation of the numerical models, and economic analysis of the systems. A case study of the technical and economic performance of the cooling systems located in Ithaca, NY is presented in Section 4. An analysis of the sensitivity of the relative TCOs of the systems to ambient temperature, soil conductivity, price of electricity, and cost of drilling is presented in Section 5. The results of the case study and sensitivity analysis are further discussed in Section 6.
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3. Methodology
3.1 Experimental GHP Cooling System An experimental hybrid GHP cooling system was installed in a telecommunications data center in Ithaca, NY with an approximate equipment cooling load of 14.5 kWth. The system was equipped with a comprehensive data acquisition system to record relevant temperature, flowrate, control, and power consumption data at 5-minute intervals. The cooling system presented in the schematic in Figure 1 consists of two loops, each with a pump circulating a propylene glycol solution. The two loops are connected by a heat exchanger that transfers heat from the building loop to the subsurface loop. The
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building loop circulates fluid between two 35.2 kWth (10 tons of cooling each) water-to-air GHPs (ClimateMaster TCV120), a DC, and the heat exchanger. The subsurface loop circulates fluid between the heat exchanger and the geothermal well field. The geothermal well field consists of three sets of boreholes connected in parallel, each set consisting of three 139 m boreholes connected in series. The nine borehole heat exchangers (BHEs) have a total length of 1248 m.
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The cooling system is designed to maintain the server room temperature between 25.6°C and 27.8°C (78°F to 82°F), and the two GHPs can each operate in part load or full load. The combined cooling capacity of the heat pumps is 70.4 kWth – almost five times greater than the current equipment cooling load of 14.5 kWth – to provide backup capacity and to facilitate possible future expansion of the data center. The DC operates at full capacity when the temperature difference between the glycol entering the DC and the ambient air ΔTDC is greater than 4.4°C (8°F) and the temperature of the glycol leaving the DC is greater than 1.7°C (35°F). Otherwise the DC is bypassed and its fan is switched off.
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Data Center Building Building Circulation Pump
Borehole Heat Exchangers (BHEs)
Wellfield Circulation Pump
Indoor air E-3 Geothermal Heat Pump
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Heat Exchanger
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Dry Cooler (DC)
Ambient air
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Figure 1: Simplified schematic of the hybrid GHP cooling system at the experimental data center site. 3.2 Numerical TRNSYS Model
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Long-term operation of the GHP system was modeled using TRNSYS – Transient System Simulation Tool, a software environment developed to simulate performance of thermal and electrical energy systems (Klein, et al., 2014). TRNSYS includes an extensive library of components that can be used to model the performance of each part of the system of interest. Operation of the data center cooling systems was simulated using a 10-minute time step in TRNSYS to produce predicted system performance for 20 years. The performance of the ClimateMaster TCV120 heat pumps units was modeled using tables provided by the manufacturer, which were implemented in the TRNSYS model (ClimateMaster, 2017). The tables provided cooling and electricity consumption data for discrete input values of temperature and flowrate of the entering glycol-water mixture as well as the temperature, humidity, and flowrate of the entering indoor air. The TRNSYS conditioning equipment component (type 42) interpolates between these values to determine heat pump performance at each time step. Only full load operation was modeled due to a
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lack of performance data for part load operation. The DC was modeled using the counter flow heat exchanger component (type 5). The model uses the specified heat exchanger coefficient as input to calculate the heat exchanger effectiveness. The server room in the data center was modeled using the lumped capacitance building component (type 88), which simulates the building as a single zone. The model assumes constant heat gains from equipment, neglects solar heat gains, and lumps heat transfer through the building envelope and ventilation into an overall heat loss coefficient. The annual ambient temperature inputs to this model came from a typical meteorological year v3 (TMY3) file for the Elmira Regional Airport which is located about 30 miles south of Ithaca (NREL, 2005). TMY3 data represent the hourly weather conditions for a typical meteorological year based on multiple years of recorded data (Wilcox & Marion, 2008). The glycol heat exchanger was modeled using the effectiveness heat exchanger component (type 91). This model uses a specified value of heat exchanger effectiveness to calculate the heat transfer rate between the two circulation loops (Klein, et al., 2014).
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The thermal behavior of the BHEs was simulated in MATLAB using a previously validated semi-analytical slender-body heat transfer model (Beckers K. F., 2016). The TRNSYS model called the MATLAB script at each time step to calculate the outlet temperature of the BHEs. The thermal conductivity and diffusivity of the soil were obtained from a BHE thermal response test performed at the site and specified as 3.3 W/(m∙K) and 0.90×10-6 m2/s respectively. The far-field temperature of the subsurface was assumed to be the average ambient temperature calculated from the TMY3 data (9.0°C for Ithaca).
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Heat pump operation was controlled using the five-stage room thermostat component (type 108). This component controls two stages of cooling and was set so that the first heat pump would begin operation if the indoor temperature went above 25.6°C (78°F) and the second heat pump would switch on if the indoor temperature exceeded 28.1°C (82.5°F). DC operation was controlled using the differential controller component (type 2). The controller used a specified temperature dead band to determine the fan control signal based on the difference between the temperatures of the entering glycol solution and the ambient air ΔTDC. The circulation pumps were assumed to be in operation if either one of the heat pumps or the DC were operating (Klein, et al., 2014).
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The model of the hybrid system (GHP+DC) was modified to represent a GHP system without a DC by setting the DC permanently off. A model of an ASHP system was developed using the GHP+DC model with the glycol heat exchanger and subsurface loop components removed. The use of waterside economizers (WSEs) was also investigated for both GHP and ASHP systems. WSEs can be used to reduce electricity consumption when the ambient temperature is low enough that the DC can cool the glycol to a sufficiently low temperature. The cool glycol bypasses the GHP unit and instead flows through the coils of the WSE that cool the building air directly, eliminating the power consumption of the compressors in the heat pumps. Models of a hybrid GHP system with a waterside economizer (GHP+WSE) and an ASHP system with a waterside economizer (ASHP+WSE) were developed by modifying the GHP+DC and ASHP models respectively. The models were augmented to include conditioning equipment components (type 42) to interpolate waterside economizer performance data provided by the manufacturer (ClimateMaster, 2017) to determine the performance of the waterside economizer at each time step. The controller was updated so during periods when the ambient temperature was within a specified range, power consumption of the heat pump compressors would be set to zero and the heat transfer from the room to the circulating glycol would be determined by the WSE performance, rather than heat pump performance.
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3.3 Validation of TRNSYS Model
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The numerical TRNSYS model was calibrated and validated using data recorded at the experimental site between August 11, 2017 and September 27, 2017. Average flowrates were calculated for both the subsurface and the building loops and used in the TRNSYS model. An average value of heat gains from the IT equipment was determined based on the measurements of flowrate in the building loop and the temperatures at the inlet and outlet of the heat pumps. Only measurements at times when the difference between the indoor and ambient temperatures was less than 0.5°C were used so that heat transfer to or from the atmosphere was minimized. The inlet and outlet temperatures of the DC were used to determine its heat transfer coefficient. Similarly, the inlet and outlet temperatures of the glycol heat exchanger were used to determine its heat transfer effectiveness. It was not feasible to calculate the building heat loss coefficient from the data recorded at any single point in time due to the sensitivity of this parameter to the fluctuating cooling load. Therefore, the cooling load data was plotted and a value of the building heat loss coefficient was determined that aligned the TRNSYS results most closely with the cooling load recorded at the experimental site.
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The simulated behavior of the system was validated against the data recorded at the experimental site. For consistency, the ambient temperature input to the TRNSYS model from the TMY3 file was replaced with the recorded local ambient temperature. The data from the experimental site was compared to the TRNSYS results for several important parameters including the heat transfer rate to the BHEs shown in Figure 2 and the heat removed from the data center by the heat pumps shown in Figure 3. The data were displayed as binned averages to eliminate the noise associated with the on/off fluctuations of the cooling systems.
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Figures 2 and 3 validate the ability of the TRNSYS model to simulate the trends in the experimental data, however there were limitations in the experimental data that prevented an even better agreement between the two data sets. The first was that the experimental cooling system began operation before the data acquisition system was installed and began operating. Therefore, a significant amount of heat had been transferred to the experimental BHEs by the time data recording began. This caused the temperature in the experimental BHEs to be higher than was predicted by TRNSYS for the first 800 hours of the simulation until the short-term transient effects in the subsurface became less pronounced. We also lacked performance data for modeling partial load operation of the heat pumps and could not model short-term changes to the cooling load from equipment or changes to the building envelope, such as when a door was opened. These factors likely caused some discrepancy between the two data sets.
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Figure 2: Borehole heat transfer rate (QBHE) vs. time recorded at experimental site from August 11, 2017 to September 27, 2017 and simulated in TRNSYS.
Figure 3: Heat removed from data center by heat pumps (QHP) vs. time recorded at experimental site from August 11, 2017 to September 27, 2017 and simulated in TRNSYS.
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Nonetheless, the TRNSYS model was shown to accurately represent the performance of the experimental system using several methods. First, the BHE heat transfer model was previously validated in the cellular tower shelter cooling study performed by our group (Beckers, Aguirre, & Tester, 2018; Beckers K. F., 2016). Also, the modeling parameters for the components of the system were calibrated based on the experimental data as described earlier in this section. Third, the averages of the rates of heat transfer to the BHEs over the validation period are within 1% of one another for the experimental and simulated data sets. The rates of heat removed from the building by the heat pumps over the validation period are within 3% of one another for the two data sets. Based on these factors, we expect that the TRNSYS model accurately predicts yearly and lifetime performance of the cooling systems. 3.4 Economic Analysis
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The economic performance of the five types of cooling systems was compared using their total costs of ownership (TCO) defined as a sum of the present values of the capital costs, operation costs (including electricity purchased), and maintenance costs of the systems over their expected lifetimes (20 years). The cost information used for the analysis is presented in Table 1. All cost data in this paper is provided in 2017 USD. While the various project costs will vary from site to site, the main capital cost difference between the geothermal and air-source systems is due to the cost of drilling and installing the subsurface loop. The total cost of the subsurface loop was estimated based on regional average costs per foot of BHEs in the U.S. (Battocletti & Glassley, 2013). The main operational cost differences between geothermal and air-source systems are due to differences in costs of electricity consumption. Electricity prices were based on state and national average rates for commercial customers in the U.S. (EIA, 2017). TCO was calculated assuming a real (i.e. inflation adjusted) discount rate of 5%. The base economic analyses provide relatively conservative estimates of TCO since economic incentives such as investment tax credits or government grants, feed-in tariffs for efficiency improvements, carbon credits, or potential drilling cost improvements were not accounted for. Table 1: Cost data used in the economic analysis given in 2017 USD and based on actual capital and maintenance costs from the experimental site in Ithaca, NY.
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Cost Component
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Heat pump units (2) Dry cooler Waterside economizer Circulation pumps and piping Ductwork and insulation Control equipment Commissioning and air balance Project management Overhead and profit Heat pump maintenance Dry cooler maintenance
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Cost for Each Case GHP+DC GHP+WSE ASHP+WSE $30,000 $7,500 $2,100 $10,000
$5,000 $17,000
$6,500
$5,000 $2,500 $4,000 $11,000 $300/year $500/year
ASHP
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4. Ithaca, NY Case Study
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The values of total BHE length and DC temperature set point in the validated TRNSYS model were optimized to 760 m and 20°C respectively to provide the lowest TCOs. Technical performance of the systems was analyzed using two metrics: the average coefficient of performance (COP) and the percent reduction in capacity of the heat pumps over the 20-year lifetime. COP is defined as the ratio of the heat removed from the building by the cooling system to the electrical input to the system including the heat pump compressors, the building and subsurface circulation pumps, and the dry cooler fan. Subsurface temperature increases are a concern for data center GHP cooling systems because higher subsurface temperatures will diminish the total capacity of the heat pumps. The effect of the DCs in reducing subsurface temperature increases in the GHP+DC and GHP+WSE cases was quantified using the percent reduction in capacity of the systems from year 1 to year 20 and was compared to the capacity reductions for the GHP case.
Figure 4: Yearly average coefficient of performance (COP) for each cooling system configuration in Ithaca, NY.
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The yearly average COPs for each cooling system are plotted in Figure 4 and the lifetime average COPs are presented in Table 2. It should be noted that these average COP data are based on systems with economically optimized BHE lengths and dry cooler set points and that much higher COPs for geothermal systems could be achieved with greater BHE lengths (Zurmuhl, et al., 2018). The yearly average COPs of the geothermal systems diminish over the 20-year lifetimes of the systems, but are still higher than the yearly average COPs of the air-source systems even in year 20. The COP reductions in the GHP+DC and GHP+WSE systems are much less pronounced than those in the GHP system due to the supplementary heat rejection to the atmosphere by the DC. The yearly average COPs of the GHP+DC system are lower than those of the GHP system in the first two years due to the additional power draw
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of the DC, but the benefits of the DC are shown in later years when the yearly average COPs are higher for the GHP+DC than for the GHP due to lower subsurface temperatures. Furthermore, the reductions in capacity of the heat pumps for the GHP+DC and GHP+WSE systems are significantly less than those in the GHP system as shown in Table 2. The capacity reductions of the GHP+DC case were less than those of the GHP+WSE case, because the DC works to thermally recharge the subsurface in the winter in the GHP+DC case, but not in the GHP+WSE case. However, even though the temperature of the soil increased more significantly for the GHP+WSE case, the system had a higher lifetime average COP due to the reduced electricity consumption of the system while operating the WSE during periods of low ambient temperature. Table 2: Lifetime average COPs of each cooling systems and the reduction in the combined capacity of the geothermal heat pumps from year 1 to year 20. Minimum capacity in year 1 [kWth] 67.6 67.9 67.8 -
Minimum capacity in year 20 [kWth] 62.5 65.6 63.8 -
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GHP GHP+DC GHP+WSE ASHP+WSE ASHP
Lifetime average COP 2.84 2.99 3.16 2.64 2.54
Percent reduction 7.5% 3.4% 5.9% -
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The TCOs of each system based on the economic assumptions presented in Section 3.4 including a state average electricity price of 15.03¢/kWh and a regional average drilling cost of $52.59/m are plotted in blue in Figure 5. Under these assumptions, the reduced electricity consumption of the geothermal systems was not enough to recover the additional capital cost of the geothermal systems compared to the ASHP. However, the calculations of these TCOs did not account for any type of incentives and therefore provide fairly conservative estimates.
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In a separate part of this study, the effect of economic incentives on the TCO of the geothermal systems was analyzed in the form of an investment tax credit (ITC). An ITC effectively reduces the capital cost of the system by a specified percentage. The levels of ITCs required for the GHP, GHP+DC, and GHP+WSE systems to break even with the ASHP system are 20%, 27%, and 29% respectively, which are within the range of incentives that are currently available. For example, similar GHP installations in Ithaca, NY could qualify for a 10% ITC from the federal government (DSIRE, 2018) as well as a $24,000 rebate (equivalent to an additional 18-20% of the capital cost) from the New York State Energy Research and Development Authority (NYSERDA, 2018). The TCOs of the cooling systems with these incentives applied are plotted in orange in Figure 5. With incentives included, the TCOs of the GHP and GHP+DC systems are 7% and 1% less than the TCO of the ASHP systems respectively and the TCO of the GHP+WSE is only 1% greater.
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Figure 5: Total cost of ownership (TCO) of each cooling system in Ithaca, NY including and not including state and federal incentives.
5. Sensitivity Analysis
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5.1 Ambient Temperature
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The models of the cooling systems in Ithaca, NY were modified in order to analyze the sensitivities of the relative TCOs of the systems to different ambient temperature inputs. TMY3 datasets for twelve locations in the U.S. were selected to represent a spectrum of the mean and variability of ambient temperature. The locations represented by the TMY3 data and their associated ambient temperature statistics are presented in Table 3. The cooling degree days of each location are also provided in Table 3, but only as a reference, as cooling of the data center would occur year-round, even when the outdoor ambient temperature is less than the temperature of the indoor air. The performance of the cooling systems was simulated using each of the twelve TMY3 datasets as ambient temperature inputs. The purpose of these simulations was not to represent the actual performance of these systems in the specific locations represented by the TMY3 datasets. Rather, the intention was to use the TMY3 datasets to represent the same cooling systems with differing average values and standard deviations of the ambient temperature input in order to evaluate the sensitivities of the relative TCOs to these parameters. Therefore, base case values of soil conductivity (2.6 W/(m∙K)), price of electricity (11¢/kWh), and cost of drilling ($48/m) were selected and used as inputs to the TRNSYS models and economic analysis. The total BHE length and dry cooler set point inputs were optimized for each TMY3 input.
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Table 3: Locations represented by the TMY3 datasets used in the sensitivity study. Location
Average Tamb [°C]
Standard deviation of Tamb [°C]
A B C D E F G H I J K L
Juneau, AK Minneapolis, MN Ithaca, NY Denver, CO Seattle, WA Philadelphia, PA Sacramento, CA San Diego, CA Dallas, TX Las Vegas, NV New Orleans, LA Miami, FL
5.5 7.7 9.0 10.9 11.2 12.7 15.5 17.7 18.7 19.8 20.4 24.5
7.6 12.9 9.7 10.9 6.0 10.3 7.8 3.8 9.8 10.6 7.6 4.5
Cooling degree days [°C·days] 22 494 284 620 161 707 774 452 1573 1927 1606 2385
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The results of the ambient temperature sensitivity study are plotted in Figures 6, 7, and 8 along with surface fits of the data. The data points on the plots are labeled based on the TMY3 file used to simulate the system performance reflected by each data point. The relative performance of the ASHP+WSE is not plotted, because the TCO of the ASHP+WSE systems was higher than the TCO of the ASHP systems for all weather inputs analyzed and the addition of a WSE to an ASHP system provided minimal efficiency improvements.
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The data in Figures 6, 7, and 8 suggest that GHP systems with and without hybridization perform better relative to ASHP systems in colder climates with higher temperature variability. One reason the geothermal systems perform better in colder climates is because the initial COPs of these systems are higher than those in warmer climates due to the lower initial ground temperature. Also, the COPs of the heat pumps decrease more significantly with respect to water/glycol inlet temperature as the water/glycol inlet temperature increases. Therefore, the systems in colder climates where the initial ground temperature is lower experience less significant efficiency reductions over their lifetimes. ASHP systems perform much more poorly in highly variable climates for the same reason. The heat pumps in geothermal systems in climates with highly variable ambient temperatures benefit from minimal diurnal or seasonal fluctuations in inlet temperature. However, ASHP systems in similar climates do not have this benefit and will have high inlet temperatures to the heat pump units during periods when the ambient temperature is high.
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Figure 6: Difference in TCO between GHP and ASHP systems as a percentage of the TCO of the ASHP systems with respect to the average and standard deviation of the ambient temperature inputs.
Figure 7: Difference in TCO between GHP+DC and ASHP systems as a percentage of the TCO of the ASHP systems with respect to the average and standard deviation of the ambient temperature inputs.
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Figure 8: Difference in TCO between GHP+WSE and ASHP systems as a percentage of the TCO of the ASHP systems with respect to the average and standard deviation of the ambient temperature inputs.
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Overall, the GHP systems performed better economically compared to the GHP+DC and GHP+WSE systems, suggesting that the additional capital cost of DCs or WSEs is not recovered through reduced electricity consumption in most cases. In fact, in some of the hotter climates, the use of the WSE in the GHP+WSE systems was so minimal that there was little impact on the efficiency of the system at all. These results could potentially change if differently sized DCs were considered. 5.2 Soil Conductivity, Price of Electricity, and Cost of Drilling
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The sensitivities of the relative TCOs of the systems to changes in soil conductivity, price of electricity, and cost of drilling were also analyzed for each of the twelve TMY3 input datasets. The values of each of these parameters were modified by ±20% to demonstrate the relative impacts each of these parameters had on the difference in TCO between the geothermal systems and the ASHP system (ΔTCO). The effects were fairly consistent regardless of ambient temperature input. The results are presented in Table 4 as the percent change to ΔTCO averaged for all of the weather inputs. Our decision to vary these parameters by ±20% is not necessarily representative of the range of values found in the U.S. This percentage was chosen for comparison of the impacts of each of these parameters on the economic performance of the systems. Of the three parameters shown in Table 4, cost of drilling had the most significant impact on ΔTCO. Price of electricity may have a significant impact on the TCOs of the individual systems, but due to the fact that the TCOs of both geothermal and air-source systems are impacted by an increase or decrease in electricity price, the impact to ΔTCO is minimal. Soil conductivity had a relatively large impact on the GHP systems, but because the GHP+DC and GHP+WSE systems do not rely solely on the ground as a heat sink, the impact is less significant for those systems. Also, while the effect of increasing or
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decreasing the price of electricity or cost of drilling on the ΔTCOs seems fairly linear, the value of increasing soil conductivity diminishes as soil conductivity increases. Table 4: Sensitivity of the difference in total cost of ownership (ΔTCO) between geothermal systems and ASHP systems to variations in soil conductivity, price of electricity, and cost of drilling.
Soil conductivity Price of electricity Cost of drilling
Percent change to input parameter -20% +20% -20% +20% -20% +20%
GHP +20% -12% +2% -3% -27% +27%
GHP+WSE +10% -6% +3% -4% -19% +20%
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5.3 Economic Incentives
Percent change to ΔTCO GHP+DC +4% -3% +4% -5% -21% +22%
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Parameter
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For the base case assumptions, GHP systems require between 20% and 30% ITCs to break even with ASHP systems, as shown in Figure 9. These levels of ITCs are reasonable and attainable through a combination of state and federal policies. In fact, ITCs within this range are currently available in New York State as discussed in Section 4. However, these results could vary widely based on the soil conductivity, price of electricity, and cost of drilling for different sites around the U.S. The necessary ITCs could also decrease with improvements to drilling technologies, implementation of carbon cap and trade, or the economy of scale resulting from an increased adoption of GHPs.
Figure 9: Level of investment tax credits (ITCs) required for GHP systems to break even with ASHP systems with respect to the average and standard deviation of ambient temperature.
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6. Discussion
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The results show that even for the examined cases with less ideal input parameters for GHP performance (i.e. high ambient temperature, low temperature variability, and low soil conductivity, low price of electricity, or high drilling costs), there are still geothermal systems that outperform ASHP systems in terms of average COP. The simulated performance is limited by the assumptions used and the results should be treated as a base case. For example, the total BHE lengths of the geothermal systems were optimized for minimal TCO. However, in cases where incentives could provide a significantly lower TCO for an economically optimized geothermal system than for an ASHP, it could make sense to increase the total BHE length, further increasing COP while maintaining TCO below that of an ASHP system. Also, due to computational limitations, the total BHE length optimized for GHP cases was used in the GHP+DC cases and only the DC set point was optimized for. In an actual GHP+DC system, there would be a tradeoff between total BHE length drilled and lifetime DC usage/electricity consumption that would need to be accounted for in the design of the system.
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As mentioned in Section 5.2, the sensitivities of soil conductivity, price of electricity, and cost of drilling were varied by ±20% in order to demonstrate their relative impacts on economic performance. Nationally representative values for soil conductivity, however, could reasonably vary more than ±20% (Aguirre, 2018). Values of cost of drilling the BHEs vary approximately ±10% across the U.S. (Battocletti & Glassley, 2013) and the price of electricity can vary significantly more than ±20%, particularly in the positive direction for states such as California or Connecticut (EIA, 2017).
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Future work that would be beneficial to this study includes an analysis of GHP+WSE systems that utilize the DC and heat pump compressors at a specified range of outdoor temperatures. This configuration may allow for higher average COPs than those reported for the GHP+DC or GHP+WSE configurations investigated in this study. Also, the subsurface temperature increase and heat pump capacity reductions could be further investigated for GHP data center cooling systems that integrate other heating needs. Such systems could be thermally balanced without the parasitic electricity consumption of a DC.
7. Conclusions
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This paper evaluated the technical and economic performance of heat pumps used for cooling small telecommunications data centers. Numerical models developed in TRNSYS software were validated using an experimental hybrid geothermal heat pump cooling system located in Ithaca, NY, USA. Using the validated models with optimized input values of total BHE length and dry cooler set point, a case study of the performance of five cooling system configurations including GHPs, ASHPs, DCs, and/or WSEs was performed. The results showed that the GHP and hybrid GHP systems had higher lifetime average COPs and that the DCs and WSEs mitigated capacity reductions due to subsurface temperature increases and improved lifetime average COP. Although the base estimations of the TCOs of the geothermal systems were higher than the TCOs of the air-source systems, the levels of incentives required to reconcile these cost differences were within the range of incentives available in New York State. A sensitivity analysis of the TCOs of the geothermal systems relative to the ASHP systems to the ambient temperature, soil conductivity, price of electricity, and cost of drilling the geothermal wells was also performed using weather inputs from locations across the U.S. and selected base case technical and economic input parameters. GHP systems with and without hybridization performed better than ASHP systems in colder climates with higher temperature variability. The geothermal systems benefit in colder
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climates from the lower initial ground temperature and less significant efficiency reductions over the course of their lifetimes. The geothermal systems in climates with high ambient temperature variability benefit from minimal short-term fluctuations in the inlet temperature to the heat pump units. Among the other three parameters analyzed, cost of drilling had the greatest impact on the relative economic performance of the geothermal systems compared to the ASHP systems.
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This study demonstrates the ability of the presented numerical model to quantitatively characterize the technical and economic performance of GHP and hybrid GHP cooling systems under a range of conditions. The results of the study illustrate the significance of the effects of location dependent technical and economic parameters on the performance of the cooling systems. The numerical model provides a versatile tool that could be implemented in the design of future GHP cooling systems to optimize for the conditions present at a particular telecommunications data center.
Acknowledgements
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The authors would like to gratefully acknowledge Verizon Communications for their major financial support of this work. The authors would also like to thank Charles Kellum, David Hampton, and Sean Clements from Verizon, Caren Rubin from Labella Associates, Rodney Scouten from Day Automation Systems, Chris Lesperance from Lono Mechanical, and Thomas Piekunka from RF Peck for their collaboration and advice on this project. The Cornell Energy Systems Institute and the Atkinson Center for a Sustainable Future are also acknowledged for their partial support.
List of Abbreviations
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air-source heat pump air-source heat pump with waterside economizer borehole heat exchanger coefficient of performance dry cooler geothermal heat pump geothermal heat pump with dry cooler geothermal heat pump with dry cooler and waterside economizer information technology investment tax credit [%] total cost of ownership [$] typical meteorological year version 3 Transient Systems Simulation Tool (Klein, et al., 2014) waterside economizer temperature difference between the ambient air and the glycol/water mixture at the inlet to the dry cooler [°C] difference in total cost of ownership between geothermal system and ASHP system [$]
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ASHP ASHP+WSE BHE COP DC GHP GHP+DC GHP+WSE IT ITC TCO TMY3 TRNSYS WSE ΔTDC ΔTCO
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