technical feasilibity of data centre heat recovery in …

AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-

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TECHNICAL FEASILIBITY OF DATA CENTRE HEAT RECOVERY IN A

COMMUNITY ENERGY NETWORK

ADREON R. MURPHY MASc Student, Mechanical & Industrial Engineering Ryerson University

350 Victoria St, Toronto, Ontario, Canada M5B 2K3

[email protected]

ALAN S. FUNG, PHD, PENG, FCSME Associate Professor, Mechanical & Industrial Engineering Ryerson University

350 Victoria St, Toronto, Ontario, Canada M5B 2K3

[email protected]

ABOUT THE AUTHOR

Adreon Murphy is a Masters of Applied Science Student at Ryerson University in Toronto, Canada,

supervised by Dr Alan Fung. Adreon is working in tandem with Enwave Energy Corporation, a

district energy company to study data centre heat revocery in a community energy network as his

thesis project. Adreon has experience in community energy planning, especially in integrating ground

source heat pumps as well as good knowledge of residential high-rise HVAC design.

ABSTRACT

Due to their significant internal heat gain as a result of computer server banks, data centres require

cooling year-round creating an opportunity to transport the waste heat to neighbouring buildings

which are heat deficient. This paper evaluates the energy sharing potential of two data centres and 10

residential buildings; analysing the potential to heat and cool these buildings with geo-exchange. The

analysis indicated that 14,593MWh of energy should be shared, equating to 79% of the total energy

sharing potential. The coefficient of performance (COP) of a heat pump facilitating energy sharing

was found to be 4.2. Utilising the industry recognised Ground Loop Design (GLD) 2016 software;

simulations indicated geo-exchange bore field requires 500 vertical boreholes drilled to 244 meters

with 6.1 metre spacing. The seasonal coefficients of performance of for heating and cooling were

observed to be 3.1 and 7.1, respectively. Additionally, the bore field required a 2800kW dry cooler

capacity to prevent it from overheating due to an annual imbalance in the ground’s thermal loading.

Furthermore, the data centre waste heat recovery system suggests a reduction potential of 4930 tonnes

of CO2e annually equating to 71% of the existing emissions in the community.

1. INTRODUCTION

Data centres are becoming large contributors to GHG emissions globally. In 2010 data centres

accounted for 1.1% to 1.5% of global energy consumption, and 1.7% to 2.2% of energy consumption

in the U.S. (Koomey, 2011). Data centre power demand is expected to increase by 15-20% annually

(Brunschwiler, Smith, Ruetsche, & Michel, 2009). In addition to electricity, data centres consume

large volumes of water through their evaporative cooling towers. If this growth is to be sustainable,

data centre operations must be reimagined as thermal energy resources, thereby offsetting their

negative environmental impact and increasing their importance in society (Velkova, 2016).


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Data centres normally produce more heat than offices within the building housing the data centre can

consume. Typically, this excess heat is released to the atmosphere via cooling towers. District energy

or community energy systems are the best way—and in most cases, the only way—to effectively use

this waste heat. These systems use a network of pipes (below grade) to supply buildings with heating

and cooling from a variety of energy sources. Data centres can be an economically viable energy

source for these systems.

A data centre heat recovery to district energy system has been successfully completed in the past.

Fortum, a Swedish district energy company has completed four projects in which they have integrated

a data centre into their district heating network and in one of the cases their district cooling network

as well (Open District Heating , 2017) (Open District Heating , 2012). Enwave Energy Corporation,

a North American district energy provider, has started construction on a system to recover heat from

an 11 MW IT load data centre in Seattle, for use in their district heating network (IDEA Industry

News, 2016). Yandex, a Russian search engine connected a 6 MW IT load data centre to a Finnish

district heating network, in which it sells 3.6 MW of waste heat (Data Center Dynamics, 2015).

Davies et. al studied the potential for data centre waste heat recovery in London, England and

concluded that the best sources of heat are in the chilled water return or the computer room air

handling (CRAH) unit return air (Davies, Maidment, & Tozer, 2016). This was confirmed by

Ebrahimi et al. (Ebrahimi, Jones, & Fleischer, 2014). Among all of the aforementioned projects and

studies, data centre heat recovery for district energy systems has never been done with geo-exchange,

for thermal energy storage.

1.1 Scope

This paper aims to determine how much energy can be shared by supplying data centres with chilled

water from the process of heating residential buildings with a heat pump. This paper aims

simultaneously to provide additional cooling to data centres as well as heating and cooling to

residential buildings with geo-exchange, when energy sharing is incapable of providing the full loads.

The paper seeks to fill gaps in data centre waste heat recovery literature by evaluating the capability

of geo-exchange to provide efficient heating and cooling for this application.

2. METHODOLOGY

2.1 Methodology

The methodology for this analysis consisted of a three-step process. Hourly heating and cooling data

was collected for a data centre and residential building in, Toronto, Canada. This data was scaled to

represent a real community in Toronto, Canada, containing two data centres and 10 surrounding

multi-unit residential buildings. The exact location is kept confidential to protect the identity of the

data centres. The heating and cooling data was then analysed in MATLAB (MathWorks Inc, 2013),

to determine the portion of energy that is either shared directly, or sources from geo-exchange.

Finally, the load profiles were imported into Ground Loop Design (GLD) 2016; simulations were

performed to determine the bore field specifications and the seasonal coefficient of performance

(COP) of the ground source heat pump in heating and cooling mode (Thermal Dynamics, 2016).


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2.2 Preliminary concept

The following, Figure 1, shows the base case connection configuration. Rejected heat from the data

centres will be provided to heat pumps in multi-unit residential buildings (MURBs), based on heat

demand. The two data centres combined would meet a portion of the peak heating requirement in

multiple buildings. However, when the MURBs are demanding less heat than their contracted

amount, or have no demand for heat, the data centres will need to run their existing chillers and

cooling towers.

Figure 1. Scenario with no storage, only interacts during winter season (Scenario 1).

The second connection configuration uses a single bore field to store all of the heat rejected from the

data centres and the MURBs. The bore field thermal loading must be balanced between the amount

of heat rejected to and extracted annually. Dry coolers operating during periods of cold outdoor air

temperature must be used to extract excess heat stored in the bore field, thus maintaining a thermal

equilibrium.

The following, Figure 2 and Figure 3, illustrate the scenarios of having one bore field. The data centre

can choose to either cool from the bore field or the heat recovery chiller (heat pump), depending on

the heat demand from the MURBs. The MURBs can employ the heat recovery chiller to source heat

from the bore field during periods of peak demand, shown in Figure 2. The issue with this

configuration is that the water passing through the bore field will be lower grade heat than the water

coming directly from the data centre. Mixing the two fluid streams will result in a lower grade heat

source when compared to the data centre stream alone; meaning lower heat-pump heating efficiencies

as a result of lower heat-pump entering water temperatures (EWT). This option for excess heating

may need to be transported in a separate pipe from the warmer water stream coming from the data

centre (not illustrated in Figure 2).


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Figure 2. Single bore field scenario, showing winter season (Scenario 2).

The configuration illustrated in Figure 3 is also able to cool the MURBs during the summer. The

section of pipe that brings water from the data centre to the heat recovery chiller during the winter is

not used during the summer, as there is no demand for space heat. This section of pipe can be used to

deliver warm water from the heat recovery chiller to the bore field, by reversing the direction of flow

in this section by the means of an additional pump, illustrated in Figure 3. This configuration will

need to be modified to provide domestic hot water during the summer.

Figure 3. Single bore field scenario, showing summer season (Scenario 2).


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3. RESULTS AND DISCUSSION

3.1 Results

Data on residential building heating for the community is derived from an hourly simulation on a

high-rise residential building in Toronto, Canada. The data is scaled to represent the particular

community of combined 185,874 m2 by building area. In order to provide a better return on

investment (ROI) existing boilers in the residential buildings were assumed to provide 70% of the

heating capacity, leaving 30% of the capacity to be provided by the community energy network as a

baseload. The 30% baseload still left 77% of the heating energy to be provided by the community

energy network.

Figure 4 shows heating from the perspective of residential buildings connected to this CEN. The full

height of the bars shows the heating load profile of the residential buildings included in this analysis.

The red portion of the bar is the load above 30% of the heating peak, which will be provided by

existing capacity. The green portion of the bar represents the positive difference between the

residential heating load and the data centre cooling load. The green portion will be provided by geo-

exchange. The green portion represents the amount of energy that can be shared, being the lesser load

of residential heating and data centre cooling. Energy shared during the summer is due to

requirements for domestic hot water.

Figure 4. Residential heating sources.

Cooling data on data centres is derived from a data centre in Toronto, Canada and scaled to represent

two data centres in the particular community. Figure 5 represents the measured annual hourly cooling

load profile for the data centre. In another effort to improve return on investment for the network, the

data centres’ existing chillers were assumed to provide 65% of the required cooling capacity, still

leaving 61% of the energy to be provided by the CEN.

The full height of the bar in Figure 5 shows the cooling load profile of the two data centres in the

community. The red portion of the bar is any load above 35% of the cooling peak, which will be


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provided by the data centres’ existing capacity. The green blue portion of the bar will be provided by

geo-exchange and the blue portion of the bar is the energy that will be shared, identical to that in

Figure 4.

Figure 5. Data centre cooling sources.

Energy sharing was able to account for 49% and 42% of the total heating and cooling requirement

for the residential buildings and the data centres, respectively. Annual energy sharing potential was

calculated to be 18,448 MWh, however 21% of this energy was above the 30% capacity selection for

the CEN, therefore 14,593 MWh was considered to be the amount of energy shared.

Simulations indicate there is an imbalance of heating and cooling provided by geo-exchange, as 30%

of the residential buildings’ cooling capacity and 75% of cooling energy will also be provided by the

CEN. Table 1 shows the heating and cooling loads that will be provided by geo-exchange, and how

geo-exchange plays a role in providing thermal energy to buildings in the CEN.

Residential

Heating

Portion

of

Energy

Data Centre

Cooling

Portion of

Energy

Residential

Cooling

Portion

of

Energy

Existing Capacity 6,781 MWh 23% 13,582

MWh

39% 1,896

MWh

25%

Energy Provided

by Geo-exchange

8,594 MWh 28% 6,978 MWh 19% 5,656

MWh

75%

Energy Sharing 14,593

MWh

49% 14,593

MWh

42% 0 0%

Total 29,968

MWh

100% 35,153

MWh

100% 7,552

MWh

100%

Table 1. Thermal energy requirements for the bore field, after considering using existing peaking

capacity and energy sharing


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Table 2 shows the parameters used to balance the bore field. In order to balance the bore field, while

maintaining a high COP, a 2800kW dry cooler was considered to run at full load during the coldest

4000 hours per year. This simulates 11,200MWh being extracted from the ground. After estimating

the seasonal COPs of the ground source heat pumps (GSHPs) the annual energy extracted and rejected

to the ground is balanced within 8%.

Annual Heating/Extraction Annual Cooling/Rejection

Energy Requirement 8,594 MWh 12,634 MWh

Energy After Dry Cooler

Balancing

19,797 MWh 12,634 MWh

Seasonal COP of GSHP from

GLD

3.1 7.1

Energy Extracted/Rejected to

Ground

13,410 MWh 14,413 MWh

Table 2. Parameters used to balance the bore field

Figure 6 shows the timing and magnitude of the loads that would be met by geo-exchange. The dry

cooler is shown in blue, running at a constant maximum load for 4000 hours per year. The residential

heating and cooling are shown in orange and red, while data centre cooling is shown in light blue.

Figure 6. Loads met by bore field, where dry cooler simulates heat being extracted from the ground.

3.2 Ground loop design

A bore field simulation was performed using the GLD 2016 software program. Table 3 summarises

the input parameters that were used in the simulation (Alaica & Dworkin, 2017).


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Working Fluid 12.9% Propylene Glycol

Design System Flowrate 3.0 GPM/ton

Ground Temperature 10°C

Ground Thermal Conductivity 2.94 W/mK

Ground Thermal Diffusivity 0.072 m2/day

Borehole Thermal Resistance 0.136 mK/W

Pipe Size 40mm

Borehole Diameter 108mm

Heat Pump Entering Water Temperature Condenser

Side

38°C

Heat Pump Entering Water Temperature Evaporator

Side

16°C

Table 3. Bore field design parameter summary (Alaica & Dworkin, 2017).

An hourly 20-year simulation was performed using the GLD software to determine the number of

boreholes required to produce adequate temperatures for supply to the heat pump. The simulations

indicated 500 boreholes were required at a depth of 244 metres in order to produce the ground-loop

return temperatures (heat pump EWTs) indicated in Table 4. The boreholes were assumed to have 6.1

metre spacing, in a 25 by 20 grid configuration. Table 4 also shows the COPs of the heat pump in

heating and cooling mode.

Cooling Heating

Peak Heat Pump Inlet 26.3°C -1.9°C

Peak Heat Pump Outlet 29.7°C -4.7°C

Design Condition Heat Pump COP 6.1 2.7

Seasonal Heat Pump COP 7.1 3.1 Average Annual Electricity Consumption 1,690,000 kWh 6,220,000 kWh

Table 4. Outputs from GLD in both heating and cooling mode.

Figure 7 shows the average fluid temperature entering the heat pump on an hourly basis. The

temperature profile shows that the bore field is balanced over a 20 year period. The brief and sharp

drop in temperature during the winter is due to a power outage at the data centre, which caused no

energy to be shared and demanded a large heating load from geo-exchange.


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Figure 7. Average fluid temperature entering GSHP, showing a 20 year hourly simulation.

The current limitations of the GLD simulation are that it cannot consider different COPs for the dry

cooler and the ground source heat pump. Since the COP of the dry cooler is much higher than the

GSHP, the simulation is underestimating the amount of heat that the dry cooler is capable of

removing. Additionally, there can only be one EWT to the evaporator. The chilled water return

temperature for residential cooling would be lower than that of the data centre, so the COP would be

lower, which would reject more heat to the ground and change the balance of the bore field. However,

the difference in ground source heat pump COPs is only 2% and difference in heat rejected to the

ground would be 0.2%.

3.3 Emissions analysis

The current greenhouse gas (GHG) emissions resulting from the operation of two data centres and 10

residential buildings were calculated and compared to the GHG emissions produced by the CEN.

Table 5 shows the efficiencies of a conventional boiler and chiller as well as a data centre’s chiller.

Non-Condensing Natural Gas Boiler

Efficiency

78% (Green Match , 2017)

Residential Chiller Plant COP 4.5 (Pacific Northwest Laboratory, 2014)

Data Centre Chiller Plant COP 6 (Hewlett-Packard, 2006)

Natural Gas Emission Factor 176g CO2e/kWh (Natural Resources Canada ,

2013)

Ontario, Canada Electricity Grid Emission

Factor

50g CO2e/kWh (Government of Ontario, 2017)

Table 5. Typical efficiencies and emissions factors used to calculate existing emissions.

The COP of the heat pump during periods of energy sharing was calculated to be 4.2, with condenser

and evaporator EWTs of 38°C and 16°C. The COP of the dry cooler was calculated using AIACalc

software (LU-VE Sweden AB, n.d.). The average air temperature from November to April in Toronto

is 1°C (Government of Canada , 2016). Considering an EWT of 16°C and a leaving temperature of

10°C, the COP was calculated to be 31 or 0.1kW/ton, because of fan operation.


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Energy Sharing COP (EWT: 16°C, EWT: 38°C) 4.2

Average Dry Cooler COP (1°C average air

temperature)

31 (0.11kW/ton)

Table 6. Efficiencies of community energy network

Table 7 shows the GHG emission analysis. The emissions that remain unchanged are due to the fact

that the CEN does not provide all of the system’s thermal energy.

Annual Residential

Heating

Annual Data Centre

Cooling

Annual Residential

Cooling

Energy Requirement 29,968 MWh 35,153 MWh 7,552 MWh

Existing Emissions 6,593 tonnes 293 tonnes 84 tonnes

Remaining Unchanged 1,492 tonnes 113 tonnes 21 tonnes

Energy Sharing

Emissions

174 tonnes 0 tonnes N/A

Dry Cooler Emissions

10 tonnes 8 tonnes

Geo-exchange

Emissions

136 tonnes 47 tonnes 38 tonnes

Total GHG Savings 4,792 tonnes 123 tonnes 17 tonnes

Total GHG Savings 73% 42% 20%

Table 7. Comparison of existing emissions and CEN emissions

In total the CEN would save 4930 tonnes of equivalent CO2 emissions which is 71% of the existing

total. There would also be an environmental benefit due to water savings in cooling towers that is not

considered in this analysis.

4. CONCLUSIONS

The scope of this paper was to determine how much energy can be shared between data centres and

residential buildings in a CEN, while providing a portion of the remaining thermal energy requirement

with geo-exchange. The conducted analysis in this paper indicated that energy sharing was able to

account for 49% and 42% (14,593 MWh) of the total heating and cooling requirement for the

residential buildings and the data centres, respectively. The COP of the heat pump in energy sharing

mode was 4.2, which should greatly help the financial viability of this project.

500 boreholes drilled to 244 metre depth with 6.1 metre spacing in a 25 by 20 grid configuration was

required to provide cooling for two data centres and heating for 10 residential buildings constituting

1,995,000 ft2. A 2800kW dry cooler was also used to prevent the bore field from overheating. The

seasonal COP of the ground source heat pump in heating was 3.1 and 7.1 in cooling mode.

If implemented this CEN would save 4930 tonnes of CO2e emissions annually, or 71% of the existing

total for the community. This project would be the first data centre waste heat recovery system that

incorporates geo-exchange, and would be applicable anywhere a data centre is located near a heating

load.


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4.1 Future work

In future considerations of this research, the amount of buildings included in the CEN should be

optimised to maximise the amount of energy that is shared while reducing the peak requirement of

the bore field; reducing the ground loop length requirement, meaning avoided capital costs. The

amount of capacity allotted to the CEN should also be optimised to produce the maximum ROI.

A third scenario of having two separate bore fields for heating and cooling will also be considered.

This is theorised to create higher heating and cooling efficiencies, introducing controlled bore field

saturation to produce a temperature similar to that required for building side heating and cooling

supply temperatures.

Finally, a financial analysis will be conducted to determine the economic feasibility of the project,

and to determine which configuration poses the strongest business case.

ACKNOWLEDGEMENTS

I would like to thank Enwave Energy Corporation, Mitacs, Ontario Centre of Excellence, Ryerson

Faculty of Engineering, Dr Alan S. Fung and Adam A. Alaica for their support and funding.


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