storage in nem-connected csp: synopsis

8/14/2019 Storage in NEM-Connected CSP: Synopsis

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[1]

Economics of Storage in a NEM-

Connected Concentrating Solar Power

Station

Author: Warwick Johnston - Manager, SunWiz - BEng (Hons)/BSc

Extract of Dissertation for Masters of Science (Renewable Energy)

Please contact: [email protected] or 0413361534 for further information

IntroductionThis is a summary of a dissertation undertaken by Warwick Johnston into economically-optimum

levels of energy storage in a Concentrating Solar Power Station (CSPS) connected to the National

Electricity Market (NEM).

It answers the questions:

In the context of operation within the Australian wholesale electricity market, is there value

in using energy storage in a solar power station? Does this vary by site, dependent on solar

radiation characteristics and wholesale price fluctuation?

What amount of energy storage generates the greatest revenue from a solar power station?

What is the most cost-effective investment in energy storage?

It shows:

Low NEM prices mean IRRs from CSP are unlikely to be sufficiently high to attract

investment, even with 50% government funding of upfront costs.

Incorporating storage leads to higher LCOE but improves IRR, even when price-insensitive

energy dispatch methodologies are employed.

Use of price-sensitive energy dispatch methodology can lead to 10-20% greater revenue

Generation during peak power prices can increase annual revenue by 25%.

Use of measured solar radiation data is critical, as differences between measured andTypical Mean Year solar data can lead to revenue miscalculations

Higher NSW NEM prices can lead to more favourable investments than those in sunnier

Queensland, but NEM price variability can easily cause this situation to reverse.



[2]

Premise:

The NEM electricity price tends to peak in the late afternoon, long after the peak in solar radiation,

as shown in the following graph. A CSPS that was able to delay its output to coincide with this price

peak should be able to generate more revenue.

However, loss of energy occurs whenever storage is used; and although thermal storage may be lessexpensive than battery storage, its costs must still be covered by increased revenue. Thus there may

be an optimal level of storage, in which revenue per unit of storage is maximised; an optimal level

that is dependent upon the cost of storage and the time-value of electricity.

The graph above suggests that storage can reduce Levelised Cost of Energy (LCOE). LCOE is an

appropriate measure by which to financially compare CSPSs if revenue is fixed by a Feed-in Tariff, as

producing lower cost energy should maximise return if revenue is only based upon output energy.

However, when the electricity price varies with time, as occurs in the NEM, optimal configurationsare those that maximise revenue per invested dollar (return on investment).

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1 2 3 4 5 6 7 8 9 1011121314151617181920212223 0

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NSW Ave NEM price & Solar Radiation vs

Time of Day

2004 2005

2006 2007

Direct Solar Radiation Global Solar Radiation



[3]

This leads to the questions:

What amount of energy storage maximises Internal Rate of Return (IRR)?

Does this amount vary by location?

How do NEM price and solar radiation characteristics influence this outcome?

NEM PriceThe challenge for CSP developers is that the NEM price varies by state, year, month, day, hour, and

minute. The annual variation between states is shown in the graph below; the subsequent graph

shows monthly variation for NSW in 2004.

These graphs show that a tendency toward afternoon price peaks exists, but the price profile

changes significantly as the profile is only an average of a half-hourly varying price. As the annual

revenue is the sum of the instantaneous product of NEM price and CSPS output, dispatching energy

$0

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$50

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Average NEM Price 2004-07

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Average Electricity Price vs hour of day -

2004 NSW January

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during varying times of peak energy price is critical for revenue maximisation. CSPS design should

therefore incorporate a sufficient amount of storage to cost-effectively capture higher electricity

prices.

Research designSolar Analysis Model (SAM) CSP simulation software produced by the National Renewable Energy

Laboratory (NREL) was used to model the input and operational costs of various CSP configurations.

A financial spreadsheet that incorporates NEM electricity pricing data was built around the SAM

simulator output. This allows calculation of LCOE and IRR for a number of input variables across a

variety of locations and NEM electricity price datasets.

The modelling took a selected year’s hourly NEM price and assumed it applied in the following thirty

years of operation. To provide a sensitivity analysis to NEM price, each simulation was run four

times, once for each of the four chosen years of hourly NEM price data (2004-2007). This also allows

investigation of the relationship between financially optimum levels of storage and the NEM price

profile.

An investigation of the variance of IRR with respect to inputs such as solar multiple (SM; the solar

field capacity divided by the generator output capacity), hours of thermal energy storage (hTES),

NEM price, and location was performed. To model the CSPS initially proposed under the solar

flagships program, a 250 MW CSPS was chosen. LCOE was investigated for the purpose of

comparison to international literature, and was not subsidised in order to enable direct comparison.

A currency exchange rate of 1USD=0.8AUD was used.

It was found that a 33% government contribution to initial capital cost (as proposed by theannounced Solar Flagships Program) did not guarantee good outcomes, and negative IRRs were

found in certain years. Instead a 50% government contribution was modelled for IRR calculations, so

that comparisons between from multiple datasets with positive IRR could be drawn. The sunniest

NEM-connected location in Australia (Longreach, QLD) was chosen as the base case site of the power

station, with a sensitivity analysis performed upon location.



[5]

The Importance of Weather DataThe study found publically-available half-hourly DNI solar radiation data was only available at six

NEM connected sites, as shown in the table below. In contrast, Typical-Mean Year half-hourly data is

available at a large number of sites.

Frequency Data Source Locations of interest to study

Half-hourlyMeasurements

Bureau of Meteorology

Ground-based station

measurements

SA: Adelaide, Mt Gambier

QLD: Rockhampton

NSW: Wagga Wagga

Vic: Melbourne, Mildura

Typical Mean Year Australian Greenhouse

Office, via EnergyPlus

websiteA

NSW: Armidale, Coffs Harbour, Dubbo,

Mascot, Moree, Nowra, Orange, Richmond,

Sydney, Thredbo, Wagga Wagga,

Williamtown

QLD: Gladstone, Longreach, Mackay, Mt Isa,

Oakey, Rockhampton, TownsvilleSA: Adelaide, Ceduna, Mt Gambier, Mt Lofty,

Woomera

Vic: Ballarat, Cape Otway, East Sale,

Melbourne, Mildura, Moorabbin,

Warrnambool

Even though a profile for solar radiation and NEM price shows some inter-relationship on average,

there is no strong correlation between the two at the half-hourly and daily level (seen below). In

order to investigate multiple sites at the sunniest locations, TMY data was used for this study, with a

sensitivity analysis performed that compares use of measured and TMY data.

Correlation between 2005 Daily Radiation at Rockhampton and QLD NEM Price - Half hourly measured data used.

A

http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=5_southwest_pacific_wm

o_region_5/country=AUS/cname=Australia



[6]

Results

Base Case: Longreach, Queensland, 2007 NEM Dataset, $350/m2 solar field,

$40/kWhth storage, $50/REC

The graph below shows that the LCOE from a 250 MW CSPS in Australia is greater than A$0.20/kWh.

It has a minimum for a Solar Multiple of 1.66 with 2 hours of thermal storage, although comparable

LCOEs can be achieved with a SM of 2 and 4 or 6 hTES.

Although the LCOE may be least for a SM of 2 and 6 hTES, the IRR for the 2007 QLD NEM price data

set is greatest for a SM of 2.4 and 6 hTES. Even the significantly higher LCOE that results from

increasing the SM to 3 (with 6 hTES) still results in a comparably favourable IRR.



[7]

Sensitivity Analysis: NEM Dataset

The following section investigates the IRR that can be achieved with varying NEM-price datasets. The

figure below shows the average hourly profile for each of the annual datasets, and gives insights into

why particular configurations may be more favourable in some years than others.

Average Queensland NEM Price Hourly Profile for 2004-2007

In summary, the configuration that produces greatest IRR varies depending on the NEM price

dataset used. Extremely poor IRRs are obtained for 2005 and 2006 NEM price datasets. 4-6 hours of

storage generally produces best results.

2006

IRR for a 250 MW CSPS in Longreach, 1USD=0.8AUD, and 50% government contribution. 2006 QLD NEM dataset

$0

$50

$100

$150

$200

$250

1 3 5 7 9 11 13 15 17 19 21 23

$ / M W h

Hour of Day

Average Qld NEM Price Hourly Profile

2004

2005

2006

2007



[8]

2005


2004




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Sensitivity to Location

As shown below, Longreach has the highest average DNI solar radiation resource in NEM connected

locations with available TMY data. Moree has 12% less DNI than Longreach, Woomera 6%, and

Mildura 16%. However, the subsequent graph demonstrates that the electricity power price in other

states was significantly higher than that in Queensland in some years – for example, in 2004 NSW

had an average price that was 35% higher than Queensland’s; SA’s average NEM price was 22%

higher than Queensland. Thus, a sensitivity analysis was performed across the sunniest locations in

each state.

Annual Direct Normal Incidence Solar Radiation

$0

$10

$20

$30

$40

$50

$60

$70

$80

2004 2005 2006 2007

$ / M W h

Average NEM Price 2004-07

QLD

SA.

NSW

VIC



[10]

The graph below presents the LCOE and the IRR for the locations of Moree (NSW), Woomera (SA),

Mildura (Vic) and Longreach (Qld), using the base case inputs and a 2007 NEM price dataset and the

best plant configuration identified at Longreach. The following graph repeats these values for a 2004

NEM dataset. Note that just as optimal plant configuration in Longreach varied depending on which

year’s NEM price dataset was used, the investigated plant configuration may be not be optimal for

each of these locations.

LCOE and IRR for best locations in each state, 250MW CSPS, $50 REC, SM=2.4, 6hTES, 10% discount rate. 2007 NEM data

LCOE and IRR for best locations in each state, 250MW CSPS, $50 REC, SM=2.4, 6hTES, 10% discount rate. 2004 NEM data

Naturally, the LCOE remains independent of the price of the delivered power. However, Longreach

obtains the best IRR under 2007 NEM price scenarios, though Moree would have eclipsed Longreach

in 2004. Although Woomera receives only 6% less DNI than Longreach but had a 22% higher average

price in 2004, Longreach’s 2004 IRR was better than Woomera’s. This may highlight that it is not the

average power price that is important, but the alignment of prices with the ability to deliver stored

solar radiation.



[11]

Variation with Solar Radiation Data

As no ground-based hourly solar radiation data measurements are available at the sunniest NEM-

connected location (Longreach), Typical Mean Year (TMY) solar radiation data was used. Although

there is no strong correlation between measured solar radiation data and NEM price, a sensitivity

analysis was performed in order to determine the scale of impact of using TMY rather than

measured solar radiation data. To do so, a location was selected for which both data was available,

that of Wagga Wagga (NSW). Rather than re-run the entire simulation, the sensitivity analysis

mimicked output from a storage-less CSP by summing the instantaneous multiplication of solar

radiation data with NEM price.

The graphs below depicts a situation on February 15, 2004. On this day the NEM price spiked (red

line of upper graph) whilst the true measured solar radiation (orange line of upper graph) was far

higher than the TMY (blue line of upper graph). This would have resulted in greater revenue from a

true CSP (brown line of lower graph) than the simulation predicted (yellow line of lower graph).

Solar Radiation, NEM price, and their product for February 14, 2004

Overall, 2004 measured solar radiation at Wagga Wagga were 2.6% higher than TMY radiation for

that year. However, use of measured data would have resulted in 24% less revenue for a storage-

less CSP than the TMY based simulation, chiefly due to missed opportunities (low radiation at time of

peak power prices) – as shown in the following graph. In 2005 the situation changed so that a 3.8%

greater measured DNI occurred than TMY, resulting in a 9.6% greater revenue than would have been

simulated.



[12]

Influence of Peak Power Price Capitalisation on Revenue

The impact of peak NEM prices upon revenue is shown in the figure below, switching back to

Longreach with base case configuration. The slopes of the 2004, 2005, and 2006 cumulative

revenues are almost the same, but peak price spikes increase revenue from $30m to $50m, or 66%.

The slope of the 2007 cumulative revenue is higher, indicating a generally higher electricity price,

but the revenue is still strongly influenced by peak power prices. Given the infrequent occasion on

which these price spikes occur, and the uncertainty with which they coincide with solar radiation, it

could be worthwhile to remove such peak power prices from the analysis and consider peak powerprice events as windfall profit.

Figure 1: Cumulative Revenue from SM2.4 6hTES Longreach CSPS with TMY Solar Radiation for Various NEM Years



[13]

Whilst this analysis does not examine the benefits that storage can bring by delaying solar output, it

does show that variability between the NEM price, the solar radiation resource, and their product

can have significant impact upon revenue. Storage would not have changed the peak power price

situation that occurred in 2004 (depicted below): there was little radiation in Wagga Wagga on that

day to store when the power price exceeded $9000/MWh. Ultimately, this clearly demonstrates the

need for reliable (“bankable”) hourly direct measurements of DNI in order to convince investors so

that the project may proceed, but that an unavoidable risk of missing peak revenue remains.

December 2, 2004 Peak Power Price

Variation with energy dispatch methodology

Storing energy brings with it the ability to dispatch the energy at a chosen later time, and the

opportunity to maximise revenue by scheduling power delivery based upon NEM price. The SAM

modelling software dispatches energy based upon stored and incident solar radiation, with only

limited ability to dispatch based upon time of day, week, and month. In order to maximise profits, a

CSPS could base its energy dispatch methodology upon the forecast power price and the forecastweather. Such a dispatch methodology may offer significantly increased revenues when compared

to operating the power plant in the fixed manner which produced the aforementioned results.

The benefit of storage to revenue is shown in the figure below, which demonstrate the energy

received by the solar collector field, the part of which is sent to TES, drawn from TES, and sent to the

power block (both directly and from the TES), with the corresponding net output of energy for a

particular 24 hours period. On the right axis, the figures also show the NEM price and the amount of

revenue earned by that power delivery at that time.

The graph shows a day with a limited amount of sunshine. On this day the power price was higher inthe early morning than during the middle of the day, and it peaked in the early evening. However,

SAM’s scheduled power delivery algorithm stored the energy during the morning price peak, and

delivered it when the price was lower, also missing out on the evening’s price peak.



[14]

Energy Flows and Revenue – Aug 16 2007

In order to investigate the amount of additional revenue that may be gained by a more price-

responsive scheduling algorithm, the output power from a storage-less CSP has been dot multiplied

with the NEM price delayed by a nominated amount. The bars in the graph below indicate the

amount of revenue that would be created from delaying the entire year’s production by zero to six

hours. The seventh bar mimics a price-sensitive dispatch mechanism by summing each day’s

maximum amount of revenue from zero to six hours of delay. The line graph depicts the number of

days in 2004 for which zero, one… to six hours of delay produced the greatest revenue.

Revenue Maximising through Strategic Dispatch of Stored Solar Energy – 2004 QLD Longreach

The graph above demonstrates that on most days of the year, six hours delay produced greatest

revenue. However, simply bypassing storage on 90 days would have produced the greatest possible

revenue for each of those days. Naturally, this is a simplistic model that does not account for storage

losses or the financial costs of storage. However, it does show that the resultant revenue could be

up to 15% greater than was calculated by SAM’s price-insensitive dispatch mechanism,

demonstrating that incorporating storage could provide additional benefits to those stated.



[15]

ConclusionThis dissertation has demonstrated that the IRR achievable from a NEM-connected CSPS is too low

to lead to significant development of CSPS around Australia without far greater government support

than has been offered. Contributions of 50% of the initial capital cost are needed to obtain

reasonable IRR in the sunniest location for half of the years studied; the Solar Flagships program’sproposed 33% government contribution is insufficient in the light of these results. The $1.1b-$3.1b

required to build a 250 MW CSPS would quickly exhaust the government’s earmarked funding of

$1.5b. Therefore, greater support is needed if the government is to achieve its policy objectives.

Although the input costs are based upon sound methodology (described in Appendix 2), improved

financial outcomes should come through expected 40% learning-curve LCOE price reductions.

Characteristics of the NEM price also contribute strongly to poor financial outcomes. The peak

power prices of $10,000/MWh occur too infrequently to benefit CSPS. The variability of the NEM

price from year to year creates strong risk when choosing CSPS location, and the variability in the

NEM price daily profile inhibits the optimisation of energy storage value.

Indeed, there seems to be little value in incorporating energy storage into NEM-connected CSPS,

unless storage costs drop significantly. The LCOE is least for storage-less configurations at the

assumed storage cost of $40/kWhth. Although storage can lead to a 1% increase in IRR, such benefits

may be outweighed by an increase in perceived risk. Countering this is the ability to dispatch power

based upon NEM price, which leads to flexible revenue maximisation opportunities now and in

future.

The optimum location for a NEM-connected CSPS is not necessarily the location with greatest direct-

normal solar radiation. The variation between states in NEM power price and daily profile can havedramatic impact upon achievable IRRs.

It is clearly apparent that accurate, “bankable” solar radiation data is required in order to accurately

assess likely project returns. As only a few solar radiation measurement sites exist that are NEM

connected (of which only a couple are very sunny), government investment in solar resource

measurement may greatly facilitate the deployment of CSP in Australia.

Sensitivity analyses to costs of storage and collectors, REC price, government funding contribution,

and electricity price increase were also performed, and can be read in the complete dissertation.



[16]

Appendix 1: Input DataThe following inputs and configuration was used for SAM:

Item Setting Parameters

System Degradation 0%

Availability 100%

Heat Transfer Fluid Hitec XL Other Parameters Default

Solar Collector SolarGenix (as used in Nevada Solar

1)

Default

Power Block Rated Turbine

Net Capacity

250 MW

Power Block Design Turbine

Gross Output

275 MW

Power Cycle As per Library: SEGS 80MWe

Turbine

Default, Wet-bulb Temperature

correction mode

Thermal StorageConfiguration

Two-tank Only available Option

Thermal Storage Fluid Type Hitec XL

Thermal Storage Dispatch

Control

SCE (A Californian utility pricing

structure included in SAM)

Default

Thermal Energy Storage

Losses

Linked to hTES as per SAM user

guide Table 241

Parasitics SEGS VIII Reference Default

The heat transfer fluid and the energy storage fluid were chosen to be Hitec XL, in order to remove

the cost and inefficiency of heat exchangers.

SAM Financial inputs

Item Setting Remarks

Analysis Period 30 years

Inflation Rate 2.5%

Real Discount Rate 10%

Federal Tax (Business Tax

Rate)

30%

State Tax 0% Land taxes etc covered elsewhere

Sales Tax 0%Insurance 0.5% Default value

Depreciation 6.66% Custom depreciation values used to reflect

standard Australian depreciation curve

Tax Credit Incentives None 33%/50% government contribution only factors in

separate spreadsheet

Payment Incentives A$0.05/kWh Reflective of REC price of $50, taxable income

Site Improvements US$20/m2 Default value

Solar Field US$350/m2 Default value

HTF System US$50/m2 Default value

Storage US$40/kWhth Non default value reflective of literature

Power Plant US$880/kWe Default value

Electricity Price above 0% Sensitivity analysis performed



[17]

inflation

Indirect Costs: EPC 15% Default value

Indirect Costs: Product, Land,

Management

3.5% Default value

O&M: Fixed Annual Costs $0/year Default value

O&M: Fixed Cost by Capacity $80/kW/year Default valueO&M: Variable Cost by

Generation

$3/MWh Default value

See the appendices for a more detailed description of the default costs used in SAM, which are

based on quotations and a study commissioned by NREL and undertaken by expert consultants2.

Other Financial Parameters

The economic model used the following assumptions:

The upfront cost is placed entirely in year zero. Any government contribution also occurs in

year zero and is not taxed.

Annual Revenue is the sum of electricity generation revenue - the constant annual net

electricity output multiplied by reference year NEM price dataset multiplied by (inflation

plus electricity increase) - plus RECs Revenue - constant REC price multiplied by net

electricity output. Inflation is not applied to the REC price in order to reflect expected REC

price decrease over life of system.

The annual expenses comprise of insurance, plus Operation and Maintenance.

The annual after-tax cash-flow is Tax Savings (accounting for depreciation, and tax on RECs

and expenses) plus REC creation minus expenses plus taxed electricity generation The LCOE is the sum of the discounted future after-tax costs divided by the sum of the

discounted future electricity generation

The IRR is the discount rate that sets the sum of the discounted after tax cash flow (NPV) to

zero

No cost of upgrading or extending the electricity transmission infrastructure is assumed.



[18]

Appendix 2: SAM economics inputsThe following is an extract of a paper3 which details the input cost assumptions that are covered in

SAM.

“The optimum design must consider the capital cost, operations and maintenance cost, annual generation,

financial requirements, and time-of-use value of the power generated

NREL has developed a detailed cost model for parabolic trough solar power plants. The model is a based

largely on input from FSI, which supplied the mirrors for all of the Luz plants, and has been actively working to

promote parabolic trough plants since Luz’s bankruptcy in 1991 [2]. FSI has developed a detailed cost model

based initially on the cost data from the Luz SEGS X project and later updated with more recent vendor quotes

[7]. FSI provided cost data to NREL as part of its participation in the 1998 Parabolic Trough Road-Mapping

Workshop [8] and updated the solar field costs under contract

The FSI cost model is very detailed and uses reference quotes for each cost element. Land: A parabolic trough

field uses approximately one hectare per 3,000 m2

of collector area, or a coverage of factor of about 0.3 m2

of

collector for every 1.0 m2

of land area.

Site Works and Infrastructure: The site works and infrastructure includes general land preparation, roads,

fences, and site infrastructures, such as firewater system, warehouse, and control building. The cost model

assumptions are based on the FSI input. This category scales based on the size of the solar field.

Solar Field: The solar-field cost estimates are based on an updated cost assessment produced by FSI [9]. The

cost estimate is based on the LS-3 collector design. Several adjustments are made to the collector cost to

account for a specific collector design used:

The number of receiver tubes, flex hoses, drives, sensors, and local controllers are adjusted per unit area

of collector.

The drive costs are adjusted to account for the collector size.

The mirror, steel structure, pylons, header piping, and civil work costs are assumed to be the same on a

per-square-meter basis for different collectors. Heat Transfer Fluid (HTF) System: The HTF system includes

the HTF pumps, solar heat exchangers, HTF expansion vessel, piping, valves, and instrumentation. HTF

system costs scale based on the power-plant size, except for the HTF pumps, which scale based on solar-

field size. The HTF costs are based on the FSI roadmap data. The later data was only appropriate for an

ISCCS-type plant.

Thermal Energy Storage (TES): The thermal storage costs are based on the detailed design study performed by

Nexant for a two-tank, molten-salt storage system [10]. Thermal storage tanks and costs are based on detailed

data from Solar Two and Solar Tres. The heat exchanger costs are based on manufacturer quotes. Storage

costs were broken into mechanical equipment (pumps and heat exchangers), tanks, nitrate salt, piping,

instrumentation and electrical, and civil and structural. The mechanical equipment and piping,

instrumentation, and electrical costs were scaled by power-plant size. The tank, salt, and civil costs were scaled

by storage volume. All storage costs assume a scaling factor of 1.0, so a storage system twice as big costs twice

as much. Thermal storage tank and salt costs are consistent between the trough and tower designs. The

trough thermal storage system must be approximately three times as big as the tower storage system (both in

tank size and volume of salt required) to store as much energy because of the much lower temperature

difference between the fluid in the hot and cold tanks in the trough plant.



[19]

Power Cycle: The power cycle includes the steam turbine and generator and all condensate and steam cycle

equipment including pumps, heat exchangers, piping, valves, instrumentation, and controls. The FSI studies [2]

have the most recent Rankine steam-cycle cost data for the systems used in trough designs.

Balance of Plant: The BOP includes other power plant systems, such as cooling towers, water treatment and

storage, electrical, and control systems.

Contingencies: Contingencies of 10% are included for all costs, except the solar field (5%), structures and

improvements (20%), and thermal storage. The cost of the solar field is very well understood at this point. The

larger contingency for structures and improvements is included to account for potential differences in site

preparation. Nexant included cost contingencies separately in the thermal storage.

Indirect Costs: Indirect costs include services, project costs, and management reserve. The indirect cost

assumptions were based on input from Nexant. Service costs include project management, project

engineering, and construction management services. Project costs include permits and licenses, utility

connections, and telecommunication links. No interest during construction is included; this is accounted for in

the financial model.

The primary advantage of the NREL trough simulation model is that it integrates the capital cost, O&M cost,

performance and financial constraints into a single model. This allows detailed design or project optimizations

to be carried out where all interactions between cost and performance can be accounted.“

1Solar Advisor Model User Guide for SAM 2009.10.12.

2 Solar Advisor Model – Parabolic Trough Default Cost Values. October 5, 2009. Refers to a study undertaken by

WorleyParsons commissioned by NREL under contract KAXL‐9‐99205‐00 3

Price, H. A Parabolic Trough Solar Power Plant Simulation Model. ISES 2003. March 2003

storage in nem-connected csp: synopsis

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