Department of Chemical Engineering

Low-Temperature Geothermal Resources for

District HeatingAn Economic Analysis of Geothermal

District Heating System by Using ArcGIS

Xiaoning He1, Brian J. Anderson1 Department of Chemical Engineering, West Virginia UniversityMorgantown, WV, 26506, USAEmail: brian.anderson@mail.wvu.edu

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• Introduction• Modeling Approach• Result and Discussion• Acknowledgements

Outline

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• Geothermal energy: stable, environmentally-friendly, renewable, baseload energy supply.

• 20% of total energy is used for such low temperature end-use– geothermal energy can

satisfy this end-use.

Introduction70% of

whcih for heating and AC

30% of which for heating

Figure 1: US Energy consumption scenario[EIA, 2011]“Energy consumption estimates by sector, 1949-2010,” U.S. EIA, 2011

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• Geothermal District Heating System (GDHS) Basics– Injection well and

production well– Surface heat exchanger

system– Distribution network

Introduction

DistributionPipeline

Buildings

Figure 2: Two-well Geothermal System Schematic “The Future of Geothermal Energy”, MIT, 2006

Figure 3: Distribution Network Schematic [NREL, 2004]“H2A Scenarios for Delivering Hydrogen ,” NREL, 2004

Figure 4: Heat Exchanger Schematic

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• Levelized cost of geothermal energy

• Geothermal gradient factor– Drilling cost may contribute 60% of the initial

capital cost.

• Population density factor– A high energy demand will reduce the

levelized cost as well.

Modeling ApproachInclude wells’ drilling, surface equipment investment, pipeline network cost, O&Mcost, etc.

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• Data AnalysisGeothermal Gradient Factor

Original Data with depth, temperature, longitude,

latitude

Add into ArcMapCreate temperature maps at

each depth

Data interpolation by IDW to create temperature surface

Get mean temperature at each census tract by zonal

statistics as tables

Calculate gradient for each census tract

for each map

Figure 5: Data Analysis Procedure

for each map

[°C/km]T3~T8 is temperature at depth at 3.5km~8.5km

Pyt

hon

Cod

ing

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• Data Analysis ResultsGeothermal Gradient Factor

Figure 6: Geothermal Temperature at Depth at 3.5 km to 8.5 kmFigure 7: Geothermal Gradient Map

Gradient

13.5 - 16.0

16.0 - 18.0

18.0 - 20.0

20.0 - 22.0

22.0 - 25.0

°C

°C/km

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• Preliminary Work: WVU Case Study– Location: WVU Evansdale campus– Geothermal gradient: 26.44°C/km – Population: 30,000 students– Heating in winter and cooling in summer– Distribution network: current steam pipeline– Levelized cost of geothermal energy: $5.30/MMBtu

• Geothermal Gradient Influence–

Geothermal Gradient Factor

By changing the gradient of the case study, and keeping all the other factors constant, get and plot the levelized cost vs. gradient.

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Population Density Factor

Levelized

Cost

Distribution Network

Cost

Energy Consumpti

on Estimation

Surface Plant Cost

Q=mCpΔT

$𝑀𝑀𝐵𝑡𝑢

Q=mCpΔT

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Population Density FactorE

nerg

y C

on

sum

pti

on

E

stim

ati

on

Average Household Usage: 220MMBtu/year

Average Household Size:

2.5 people

Low-temperature End-use fraction:

0.68

Household Number: base on WV census tract D

istr

ibu

tion

Netw

ork

E

stim

ati

on

Pipe length: 1.5ADN1.04[1]

AD=

Electricity cost for pumping: $0.08/kWh

Pipe sizing: base on mass flow

rate

[1]: C. Yang, J.Ogden, “Determining the lowest-cost hydrogen delivery mode”, Institute of Transportation Studies, University of California, Davis, 2006

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• Other functions and assumption used:– Drilling cost(Million $): 1.0910-7D2+8.8310-4D+0.23 [1]

– Pressure Drop: – dP/dL=2fρv2/ID– Discount rate: 5%– One engineer salary: $70k/year– Operation and Maintenance Cost: $0.0047/kWh [2]

– Project’s lifetime: 30 years

• Population Density Influence– By changing the household number, therefore changing the

population density, and keeping all the other factors constant, get and plot the levelized cost vs. population density

Population Density Factor

[1]: C. Augustine, “Hydrothermal spallation drilling and advanced energy conversion technology for engineered geothermal systems,” Department of Chemical Engineering, Massachusetts Institution of Technology, 2003[2]: S.K. Sanyal, “Cost of geothermal power and factors that affect it,” Stanford Geothermal Workshop, 2004

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Result and Discussion

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 2812.0

12.4

12.8

13.2 f(x) = 0.00308684014451 x² − 0.18254226053931 x + 15.128356287477

Levelized Cost Function of Geothermal Gradient at Pop-

density of 2100ppl/km2

Geothermal Gradient

Leveli

zed C

ost

(°C/km)0 5000 10000 15000 20000 25000 30000 35000

0

5

10

15

20

25

30

f(x) = 486.196411031401 x^-0.494643509370829

Levelized Cost Function of Population Density at Geo-gradient of 25.71 °C/km

(ppl/km2)

($/M

MB

tu)

Population Density

Leve

lize

d C

ost

• With the constant geothermal gradient, levelized cost decreases with the increase of population density.

• With the constant population density, the levelized cost decreases with the increase of geothermal gradient.

($/M

MB

tu)

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Result and Discussion

• Gradient Factor (): LC= 0.0031-0.1825+15.128, at P=2100ppl/km2

• Population Density Factor (P): LC=486.2P-0.498, at =25.71 °C/km

• LC Function: LC=486.2P-0.498 + 0.0031-0.1825+8.3

Figure 8: 3D levelized cost function

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Levelized Cost

<12.0

12.0 - 21.6

21.6- 25.5

25.5 - 35.0

>35.0

Figure 9: Levelized Cost Map of West Virginia

WVU Case

Coal Plant

Natural Gas

Solar

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• To be improved:– Distribution network: estimation of pipeline length is rough. Want

to check if ArcMap can be used to develop a distribution network model.

– The geothermal gradient is based on the average temperature of each census tract. This makes the geothermal gradient smaller than reality.

– Try to find other maps with only middle or big cities, rather than the census tract map. This will save time when doing this model nationwide.

Result and Discussion

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• Project partners: – Mr. M. G. Bedre and Mr. J. Peluchette

• G & G department:– Dr. J. Conley, Dr. G. Elmes and Dr. K. Kuhn

• Lab workers:– Ms. N. Garapati, Mr. M. Gaddipati, Mr. S.

Velaga • Department of Energy’s Geothermal

Technologies Program, Project EE0002745 for funding

Acknowledgements