PROCEEDINGS, Thirty-Eighth Workshop on Geothermal Reservoir Engineering
Stanford University, Stanford, California, February 11-13, 2013
SGP-TR-198
ESTIMATED POWER GENERATION COSTS FOR EGS
Greg Mines1 and Jay Nathwani
2
1Idaho National Laboratory;
2US Department of Energy,
e-mail: [email protected];
ABSTRACT
The Department of Energy‟s (DOE) Geothermal
Technologies Office has developed a model that
provides representative cost and performance
estimates for the generation of power from
geothermal resources. This model, Geothermal
Electricity Technology Evaluation Model (GETEM),
was originally developed to evaluate the generation
costs from hydrothermal resources and to provide a
means of assessing how technology improvements
could impact those costs. Recent modifications to the
model have focused on incorporating the costs
associated with developing geothermal resources that
utilize EGS technologies for power production. This
paper provides an overview of both those changes to
GETEM, and the EGS resource scenarios that the
DOE is using in its assessment of the impact of
technology on future generation costs. The basis for
the EGS specific scenarios is discussed along with a
summary of the estimated costs and levelized-cost-
of-electricity (LCOE) for the EGS scenarios being
considered.
BACKGROUND
In the mid 2000‟s the DOE Geothermal Technologies
Office (GTO) developed GETEM to provide both a
method of conforming to GPRA (Government
Progress and Results Act), and a tool that could both
identify major contributors to generation costs and
provide a method of assessing how technology could
impact those costs (Entingh, 2006). The focus of
early versions of the model was on the technologies
and costs associated with conventional hydrothermal
resources. The model‟s estimates were to be
„representative‟ of the costs that one would encounter
in developing a defined hydrothermal resource
scenario using either an air-cooled binary or flash-
steam technology for the power plant. Subsequent
work model has focused on better characterizing the
costs and performance of each of the project phases,
with an emphasis on EGS resources.
In 2011, feedback was received from the geothermal
industry that GTO‟s estimates of generation costs for
new geothermal developments were considerably
lower than those the industry was encountering. One
area where the estimates were considered to be low
was for those activities associated with both
exploration and confirming that a resource has been
found that is commercially viable. A team comprised
of GTO, national lab, and contractor personnel was
subsequently formed to make a concerted effort to
improve the model‟s characterization of cost and
performance for all project development phases for
both undiscovered hydrothermal and EGS resources.
The resulting changes that have been made to the
model have incorporated input received from both
interviews with industry and solicitations for industry
comment on cost and performance estimates
produced by the model.
MODEL UPDATES
The focus of the efforts by the GTO analysis team
has been on updating the model to:
Show the impact of the variability in reservoir
conditions (temperature, flow/permeability and
depth) on the generation cost;
Reflect the risk associated with early project
phases by including a methodology that
accommodates both down-selection to the final
site selected for commercial development and
exploration/confirmation failures at non-selected
sites,
Assess the impact of the uncertainty associated
with the model‟s cost and performance estimates
for the different phases and elements of the
project development;
Identify areas where the model‟s characterization
of cost and/or performance need improvement,
and where possible making those improvements;
Estimate generation costs using a methodology
that is consistent with other DOE EERE
programs; this methodology replicates a
discounted cash flow analysis accounting for
both the time required and the discount rate for
each phase of the project.
In order to show how the variations in the resource
conditions can impact the generation costs, five
scenarios were established for EGS. These scenarios
were derived internally within the GTO, taking into
account different factors including the EGS resource
potential (Augustine, 2011) and geographic diversity,
as well as consistency with other GTO programmatic
activities. They are intended to be representative of
the range of resources conditions that might be
considered for new EGS developments, and not the
near-field expansion of hydrothermal resources. The
conditions for the scenarios developed are shown in
Table 1 (the highlighted Scenario C is discussed in
more detail in this paper). The selection of the values
used for flow rate, ratio of production to injection
wells and plant size are based in part on
conversations with industry, as well as discussions
within the GTO, with the assumption that they could
be achieved using current technology at the nth
EGS
project.
The effect of risk associated with the early phases of
a project is now accounted for by allowing duration
and discount rate (cost of money) to be varied for
each phase, as well as allowing for failures during the
exploration and confirmation phase. The values used
in the Reference Cases that define the five EGS
scenarios are shown in Table 2. As indicated, a
higher discount rate is applied to the exploration and
confirmation phases than is applied to the well field
development phase; and a higher rate is applied to the
well field development phase than to the power plant
construction. A model user can vary these rates, as
well as the time required to complete each phase.
To reflect the probability that not all potential
resources considered will result in a successful
development, the model now allows for multiple
exploration sites to be considered before any drilling
is done. Some or all of the sites considered will have
subsequent drilling activities, with one of these sites
ultimately resulting in commercial power production.
The costs for all exploration work and subsequent
drilling activities, including those sites that are not
used for commercial development, are included in the
model‟s estimate of power generation costs. These
changes to the model were primarily intended for
evaluation of undiscovered hydrothermal scenarios;
however the same methodology was used for EGS. In
assessing the LCOE for EGS resources, the GTO
assumed that for every 4 sites evaluated, drilling
would occur at all 4 sites, and 3 of those sites would
result in commercial projects.
To assess how the different assumptions and
calculated values used impact the generation costs, a
sensitivity analysis was performed for ~60 variables
of the ~185 GETEM inputs. For each variable
selected for the analysis, a LCOE was determined
using a most likely or reference value, which
represented a hypothesis of what could be done at an
nth
project using current technology. Calculations
were then repeated for each variable using both a
conservative value and an optimistic value for the
selected variable. This analysis identified which
variables significantly impacted the LCOE, and
provided the GTO with an indication of the relative
importance of the different elements of a project‟s
phases on LCOE. It also assists in identifying where
technology improvements would contribute to
reaching LCOE target goals.
The sensitivity analysis reinforced that LCOE‟s are
sensitive to the drilling costs for the wells, which lead
to an effort to improve the model‟s characterization
of those costs. Prior to this effort, drilling costs were
derived from cost curves that were generated using
data from 2004 (Mansure, 2005), with those 2004
costs brought forward in time using a US Bureau of
Labor Statistics Producer Price Index (PPI) for oil
and gas wells. To improve the model‟s projections of
drilling costs, a series of well cost estimates were
generated by Sandia National Laboratory and
provided to the GTO team. These costs were used to
Table 1. EGS Resource Scenarios
Scenario
Case
Temperature
(°C) Depth (km)
Well Flow
Rate (kg/sec) Wells
Production:Injection Power Plant
Type Sales (MW)
A 100 2 40 2:1 Air Cooled
Binary 10
B 150 2.5 40 2:1 Air Cooled
Binary 15
C 175 3 40 2:1 Air Cooled
Binary 20
D 250 3.5 40 2:1 Flash 25
E 325 4 40 2:1 Flash 30
Table 2. EGS Project Phase Duration and Discount Rates
Phase Duration
Scenarios A,B & C Scenarios D & E
Discount
Rate
Permitting- Exploration & Confirmation 1 year 1 year 30%
Exploration 1 year 1 year 30%
Confirmation 1.5 year 1.5 year 30%
Utilization Permit – Field & Plant 1 year 1year 15%
Well Field Development/Completion 2 year 2 year 15%
Power Plant Construction 2 year 1.5 year 7%
Total Duration Pre-Operation Activities 5.5 year 5.5 year
Operations 20 year 20 year 7%
generate new cost curves that were recently
incorporated into the model.
The model was also modified to incorporate a
methodology for estimating power generation costs
that will tentatively be implemented in all of the
DOE‟s EERE programs. The EERE methodology
replicates a discounted cash flow analysis. It allows
both the discount rate and duration of different
project phases (including operation) to be varied, and
includes both a depreciation schedule and taxes.
Previously a Fixed Charge Rate (FCR) was used to
estimate the LCOE: this was consistent with the
approach used in the EIA Annual Energy Outlook
Report. While the model can utilize either
methodology for calculating the LCOE, the GTO
now uses the EERE methodology in its analysis
activities. The model also includes a separate, simple
discounted cash flow sheet, though those LCOE
estimates are not „reported‟ in the model output.
Other changes that were incorporated include
providing input needed to estimate the effects of
permitting, leasing, and taxes and insurance on
LCOE. Previously with the FCR approach the model
assumed a fixed plant/project life of 30 years; the
new EERE methodology allows project life to be
varied up to 40 years.
EGS SCENARIO COSTS
In addition to defining the temperature, depth, well
flow rate and power sales, a set of model inputs were
defined to establish a Reference Case LCOE for each
of the EGS scenarios. To the extent possible, the
input values used were based upon the interviews
performed with industry. Table 3 summarizes some
of the more important input parameters used to define
the EGS C scenario. Note that for all the EGS
scenarios evaluated, it is assumed that only the
injection wells are stimulated.
Table 3. Selected EGS C Scenario Model Inputs
Variable Reference Value Used Variable Reference Value Used
Resource EGS Reservoir
Temperature 175°C Well Flow 40 kg/s
Depth 3 km Temperature Drawdown 0.5% per year
Exploration Hydraulic Drawdown/Buildup 0.4 psi per 1,000 lb/hr
# of Exploration Sites Evaluated Prior to Drilling 1.33 (Productivity/Injectivity Index) 4.6 kg/s per bar
Cost per Site Evaluated $500K Subsurface Water Loss 5% of injection flow
# of Sites Drilled 1.33 Makeup Water Cost $2,000 acre/ft
Site Exploration Drilling Costs $3,000K per site Power Plant
Confirmation Conversion System air-cooled binary
# of Sites with Confirmation Drilling 1.33 Transmission Line Cost $0
Success Rate 100% Binary Plant Performance (brine effectiveness) produces LCOE minimum
# of Successful Wells Required 3 Economic Parameters
Cost per Well $9,363K Power Sales 20 MW
Stimulation Cost $2,500K Project Life 20 year
Well Field Development Contingency (applied to all capital expenditures) 15%
Success Rate 100% Royalties BLM schedule
Well Cost $7,802K Depreciation MACRS - 5 yr
Ratio Production to Injection Wells 2
Stimulation Cost per Injection Well $2,500K
Surface Equipment Cost $200K per well
Utilization Permit (Well Field and Plant) $1,000K
Table 4. EGS Scenario Estimated Costs
EGS Results Scenario A Scenario B Scenario C Scenario D Scenario E
Resource Temperature 100°C 150°C 175°C 250°C 325°C
Resource Depth 2 km 2.5 km 3 km 3.5 km 4 km
Plant type Air-Cooled BinaryAir-Cooled BinaryAir-Cooled Binary Flash Steam Flash Steam
# of Production Wells 21.5 7.6 7.9 6.4 4.3
Ratio of Production to Injection Wells 2:1 2:1 2:1 2:1 2:1
Production Well Cost - each $5,187K $6,965K $8,973K $8,237K $10,280K
Injection Well Cost - each $5,187K $6,965K $8,973K $11,210K $13,678K
Total Geothermal Flow 860 kg/s 303 kg/s 316 kg/s 256 kg/s 171 kg/s
Power Sales 10 MW 15 MW 20 MW 25 MW 30 MW
Geothermal Pumping Power 3,499 kW 738 kW 383 kW 997 kW 679 kW
Plant Output 13.50 MW 15.74 MW 20.38 MW 26 MW 30.68 MW
Generator Output 17.07 MW 20.34 MW 24.4 MW 27.42 MW 31.72 MW
Power Plant Cost $8,128/kW $4,668/kW $3,597/kW $2,091/kW $1,571/kW
Overnight Project Capital Cost (with contingency) $343,960K $187,291K $217,994K $176,620K $152,299K
Present Value of Project Capital Cost $396,252K $235,706K $276,042K $229,634K $211,177K
Exploration & Confirmation (₵ /kW-hr) 9.44 7.27 6.56 4.83 4.88
Well Field Completion - Including Stimulation (₵ /kW-hr) 32.46 7.47 7.24 4.56 2.53
Permitting (₵ /kW-hr) 0.37 0.23 0.17 0.13 0.11
Power Plant (₵ /kW-hr) 16.98 7.13 5.30 3.09 2.33
O&M (₵ /kW-hr) 17.22 5.65 4.74 4.78 3.53
Levelized Cost of Electricity - LCOE (₵ /kW-hr) 76.47 27.75 24.01 17.4 13.39
As shown in Table 4, there is considerable variation
in the generation cost estimates for these five EGS
scenarios. Not surprisingly the higher temperature
resource had lower LCOE even though its well costs
were 2 to 2-1/2 times that of the cooler, shallower
resources. While it is assumed that power sales
increase with the fluid temperature, the primary
reasons for the lower generation costs at the elevated
temperatures are the increased energy content of
these fluids. This is illustrated by the reduced number
of production wells needed to produce the indicated
level of power sales. It should be pointed out that for
all the scenarios the costs for the exploration and
confirmation phases are effectively the same, and that
successful confirmation wells are used to support
plant operation during the operational phase. As a
result, for those scenarios requiring fewer production
and injection wells, a larger fraction of the capital
costs associated with finding and creating the
reservoir and well field is attributed to exploration
and confirmation.
Note also that for these scenarios, the annual O&M
cost estimates are in part determined as being some
fixed fraction of the capital costs for the project.
Hence the O&M contribution to LCOE at the low
temperature scenario is high due to both the higher
annual cost associated with its higher project capital
costs and the lower power sales. For the higher
temperature scenarios it was assumed that a flash-
steam conversion system would be used. The model
estimates the amount of water that would be needed
for makeup to an evaporative heat rejection system,
and includes the cost of that makeup water in the
O&M contribution to the LCOE.
EGS SCENARIO COSTS
Because there a few EGS projects upon which to base
the different input variables that are used to define
the EGS scenarios, there is considerable uncertainty
associated with those inputs specific to EGS. The
sensitivity analysis that was performed identified
those having the greatest impact on the LCOE‟s. The
following discussion is specific to those parameters
that had the larger impacts on generation costs for
EGS Scenario C. This discussion focuses on those
variables that are specific to the EGS resources, and
does not address the uncertainty in the inputs
impacting the conversion system and pump costs and
performance.
Though perhaps not specific to the EGS resource,
two variables that impacted the LCOE estimates were
the level of power sales and the project life that was
used. For all scenarios, the period of operations was
20 years and for Scenario C the power sales was 20
MW. The impacts of different power sales and
project life on the LCOE for this scenario are shown
in Figure 1. While the model projects a continued
benefit from increasing plant size or longer project
life, the magnitude of this benefit diminishes with as
either parameter is increased.
Of the variables that are more specific to the
subsurface, drilling costs and production well flow
rate have large impacts on the LCOE. While there is
uncertainty as to the costs for stimulation (the model
assumes only the injection wells are stimulated), the
sensitivity analysis suggests variation in the cost for
Figure 1. Effect of Project Life and Power Sales on
the LCOE for Scenario C
stimulation has a relatively small impact on the
LCOE for the range over the range of costs
considered. Figure 2 shows the impact of variation in
the well cost and the stimulation cost on the LCOE.
Clearly the well drilling cost has a significant impact
on the LCOE. The interviews with industry indicated
that there is considerable variation in this cost.
Because of its importance, the GTO will continue its
efforts to gather data on these costs and update the
model as appropriate. Again there is little data
available on stimulation costs. Changing the
reference value stimulation cost of $2.5M per
injection well by +50% impacted the LCOE, but not
as much as the assumption that only the injection
wells are stimulated. If the production wells were
Figure 2. Effect of Well Drilling and Stimulation
Costs on LOCE for Scenario C
also stimulated, the estimated LCOE would increase
as indicated by the red square symbol in Figure 2.
Production well flow rate has a significant impact on
the LCOE. The number of production wells needed to
produce a given power sales decreases as the flow per
well increases; this reduces the cost to develop the
well field and reservoir. There are negative
consequences to increasing the production well flow
rate. The geothermal pumping power is a direct
function of both the hydraulic drawdown (inverse of
the Productivity Index) and the well flow rate. For a
given hydraulic drawdown, as the well flow rate is
increased, the required geothermal pumping power
increases. In order to maintain the same level of
power sales, a larger power plant and increased total
geothermal flow (i.e., wells) are required. In Figure 3
Figure 3. Effect of Reservoir Hydraulic Performance on LCOE for Scenario C
the effect of the values used for well flow rate and
hydraulic drawdown or Productivity/Injectivity Index
on the LCOE are shown. These estimates indicate
that for a given reservoir hydraulic performance there
is a flow rate that would produce a minimum LCOE,
and as expected as the reservoir Productivity/
Injectivity Index increase, the flow rate at which this
LCOE minimum occurs also increases.
The productivity of a geothermal resource is
dependent upon the well flow rate, temperature
decline with time, and productivity/injectivity of the
reservoir. In the case of hydrothermal resources, the
relationships between these parameters are inherent
to the reservoir. With EGS resources, these
relationships will depend upon the characteristics of
the reservoir that is created. Because the reservoir is
„engineered‟, it can be postulated that it will be
possible to create a reservoir that could maximize the
amount of heat extracted from a given reservoir
volume – at a cost. Hence for EGS, the cost to create
the reservoir becomes another parameter to be
included in the tradeoff between flow rate and both
thermal and hydraulic drawdown in optimizing the
LCOE for a given EGS scenario.
While GETEM can account for the effects of the
relationship between the well flow rate and the
hydraulic drawdown, as shown in Figure 3, it has no
methodology to relate the changes in the thermal
drawdown with flow rate. Nor does it have a
methodology of relating the cost and size of a
reservoir that is created to either the hydraulic or
thermal drawdown. It must rely on the model user to
provide input for these different parameters that
correctly depicts the consequences of increasing well
flow rate or decreasing either the hydraulic or
thermal drawdown.
FURTHER MODEL DEVELOPMENT
At present the LCOE analysis team is reviewing the
information obtained during the most recent
interviews with industry. This review will determine
whether inputs that define the EGS scenarios require
further revision, as well as whether further changes
are needed for the methodologies used to
characterize cost or performance. An effort is in
progress to determine whether a simplified well cost
method can be integrated into the model to allow the
GTO to assess how technologies specific to well
drilling can impact the generation costs. Once these
efforts are completed, an updated version of the
model will be available from the DOE GTO web site.
Further model development work will consider some
of GETEM‟s limitations. At present the model only
utilizes air-cooled binary or flash steam conversion
systems; other conversion system types may be
considered (dry steam, water-cooled binary, hybrid
flash/binary), as well as direct use or combined
power/direct use applications. Another area where
additional work may be done is updating the
properties of water that are used in order to improve
model estimates for high temperature resources
(~300°C). In the near term, there will be
consideration as to how to simplify the model‟s input
to make it use less arduous.
The GTO will continue to collect data and
information needed to validate and improve the
model‟s estimates for power generation costs. The
GTO continues to solicit comments relative to the
utility of the model, the validity of its methodology
and estimates, and any suggestions for improvements
or changes.
ACKNOWLEDGEMENT
The authors wish to acknowledge the other members
of the GTO Analysis team. Those members include
Chad Augustine (National Energy Renewable
Laboratory), Mark Paster (Consultant), and Ella
Thodall, Erin Camp and Steven Hanson (DOE
service contractors). We would also acknowledge
the technical support received from both Seungwook
Ma (DOE) in the integration of the EERE
methodology into GETEM and John Finger
(Consultant) in providing the well cost estimates used
to update the model‟s drilling cost projections. The
authors and other team members also would like to
thank those individuals from the geothermal industry
who provided the time and patience to participate in
the team‟s interviews.
This work was supported by the U.S. Department of
Energy, Assistant Secretary for Energy Efficiency
and Renewable Energy (EERE), under DOE-NE
Idaho Operations Office Contract DE AC07
05ID14517.
REFERENCES
Augustine, C. (2011), “Updated US Geothermal
Supply Characterization and Representation for
Market Penetration Model Input”, NREL/TP-
6A20-47459, 18-23, 32-36
Entingh, D. J. and Mines, G. L. (2006), “A
Framework for Evaluating Research to Improve
U.S. Geothermal Power Systems”, Geothermal
Resources Council Transactions, v. 30, 741-746.
Mansure, A. J., Bauer, S. J., and Livesay, B. J. (2005)
Geothermal Well Cost Analyses 2005,
Geothermal Resources Council Transactions, v.
29, p 515-519.