Economic Optimum Levels of Storage in a NEM-Connected Concentrating Solar Power Station

Warwick Johnston BEng (Hons)/BSc

Dissertation for Masters of Science (Renewable Energy)

Murdoch University School of Science, 2009

PEC624 – Student # 30440143

Abstract Many studies into the Life-cycle Cost of Energy (LCOE) from Concentrating Solar Power Stations (CSPS) have been performed, however most have taken place in markets in which a constant power price can be expected, whether through a power purchase agreement or some form of Feed-in Tariff. However, in the absence of an Australian Feed-in Tariff, a CSPS obtains its revenue dependent upon the Australian National Electricity Market (NEM) price at the time of power delivery. Hence in order to maximise revenue when the power price varies, the design objective becomes maximising Internal Rate of Return (IRR) rather than minimising LCOE.

This dissertation uses the Solar Advisor Model (SAM) software to investigate a 250 MW trough CSPS connected to the NEM. It answers the following 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? Given that energy storage costs money, what is the most cost-effective investment in energy storage?

It is found that the IRR-maximising CSPS plant configuration varies depending upon the electricity price profile. It is shown that the best location for solar power may not necessarily be that with the greatest solar resource. It is also demonstrated that inclusion of energy storage facilitates greatest revenue when electricity price-dependent energy dispatch methodologies are used, but that the overall impact upon IRR of including energy storage is marginal in most cases. Regardless of this, it is found that the Australian NEM price is too low and variable to justify NEM-connected CSPS in Australia without government contribution exceeding previously pledged amounts. A case is therefore made for increasing government funding and reducing project risk by creating a Feed-in Tariff.

Table of Contents Abstract ................................................................................................................................................... 2 Declaration and Acknowledgments ........................................................................................................ 4 1 Introduction .................................................................................................................................... 1 2 Concentrating Solar Thermal Power Stations ................................................................................. 3

2.1 Components ............................................................................................................................ 4 2.1.1 Solar Field ........................................................................................................................ 4 2.1.2 Thermal Energy Storage .................................................................................................. 6 2.1.3 Power Block ..................................................................................................................... 8

2.2 Design ...................................................................................................................................... 8 2.2.1 Solar Multiple and Energy Storage ................................................................................. 8

2.3 Operation .............................................................................................................................. 10 3 World Status ................................................................................................................................. 11

3.1 Australian Situation ............................................................................................................... 11 4 CSPS Economics ............................................................................................................................ 13

4.1 Current .................................................................................................................................. 14 4.2 Future .................................................................................................................................... 17 4.3 Component Cost and Optimisation ...................................................................................... 19

4.3.1 Optimum Level of Storage ............................................................................................ 19 4.4 Australian National Electricity Market Costs ........................................................................ 23

5 Research design ............................................................................................................................ 27 6 Input Data ..................................................................................................................................... 29

6.1 National Electricity Market ................................................................................................... 29 6.2 Weather and Solar Radiation ................................................................................................ 29 6.3 Solar Advisor Module Parameters ........................................................................................ 32

6.3.1 SAM technical inputs .................................................................................................... 32 6.3.2 SAM Financial inputs ..................................................................................................... 33

6.4 Other Financial Parameters .................................................................................................. 33 7 Preliminary Investigation: Greatest Delivered Value of Solar Energy .......................................... 35

7.1 Correlation between NEM price and Solar Radiation ........................................................... 35 8 Results ........................................................................................................................................... 40

8.1 Simulated Solar Power Station Operation ............................................................................ 40 8.1.1 Base Case: Longreach, Queensland, 2007 NEM DataSet, $350/m2 solar field, $40/kWhth

storage, $50/REC ......................................................................................................... 40 8.1.2 Sensitivity Analysis: NEM DataSet................................................................................. 42 8.1.3 Sensitivity Analysis: Cost of Storage ............................................................................. 44 8.1.4 Sensitivity Analysis: Cost of Collectors .......................................................................... 46 8.1.5 Sensitivity Analysis: REC price ....................................................................................... 47 8.1.6 Sensitivity Analysis: Electricity Price Increase ............................................................... 48 8.1.7 Sensitivity to Location ................................................................................................... 48 8.1.8 Variation with Solar Radiation Data .............................................................................. 51 8.1.9 Variation with energy dispatch methodology .............................................................. 54 8.1.10 Sensitivity to Government Contribution ....................................................................... 61

9 Interpretation of results ................................................................................................................ 62 9.1 Comparison of Results to Literature ..................................................................................... 62

9.1.1 Project Cost ................................................................................................................... 62 9.1.2 Project Cost Breakdown ................................................................................................ 63 9.1.3 LCOE .............................................................................................................................. 63 9.1.4 Configuration with Lowest LCOE ................................................................................... 63

9.2 Discussion .............................................................................................................................. 64 9.2.1 Correspondence of lowest LCOE with highest IRR ........................................................ 64 9.2.2 Benefits to IRR of incorporating storage ....................................................................... 64 9.2.3 Sensitivity to Component Cost of Configuration for Highest Financial Gain ................ 66 9.2.4 Sensitivity to REC Price .................................................................................................. 66 9.2.5 Sensitivity to Location ................................................................................................... 66 9.2.6 Other configurations ..................................................................................................... 66

9.3 Significance of results ........................................................................................................... 67 9.4 Limitations of results ............................................................................................................. 67 9.5 Aims....................................................................................................................................... 68

10 Conclusion, .................................................................................................................................... 69 10.1 Recommendations ................................................................................................................ 69 10.2 Opportunities for further study ............................................................................................ 70

11 References .................................................................................................................................... 71 12 Glossary ......................................................................................................................................... 73 13 Appendices .................................................................................................................................... 74

13.1 Solar Radiation Data ............................................................................................................. 74 13.2 Appendix 2: SAM economics inputs...................................................................................... 75

Declaration and Acknowledgments Except where other input sources have been referenced, this dissertation is my account of my own research.

I wish to gratefully acknowledge the contribution of the following people, with whom conversations were held that shaped research directions and confirmed my own findings:

• Sasha Giffard of SMEC

• Fiona O’Hehir of GreenBank Environmental

• Steve Hollis of Lloyd Energy Systems

• Peter Muers and Paul Ebert of Worley Parsons

• Tim Burrows of Climate Managers

I also wish to acknowledge the contribution of my project supervisor, Trevor Pryor, and Murdoch University Energy Economics Lecturer Adam McHugh

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1 Introduction The alignment of solar power systems’ output with network energy demand profile is a commonly stated justification for supporting increasing levels of distributed photovoltaic generation1

• Can solar power systems financial return on investment benefit from access to tariffs reflective of the time at which they generate electricity?

. During periods of peak demand, NEM wholesale energy prices soar above $300/MWh, events that often coincide with high solar radiation levels as they are commonly linked to air conditioner usage. This leads to two questions:

• If solar power systems can access these peak power price events, would their payback be shorter than if confined solely to retail tariffs?

There are a number of ways in which stakeholders in the solar power industry attempt to access higher tariffs, reflective of the coincidence of solar power systems output and energy demand:

• Feed-in tariffs (FiT) provide increased prices for solar power. Some studies2 have investigated the benefits of deferred infrastructure upgrades and additional generation capacity, along with other socio-environmental3 factors, in order to demonstrate that policy measures such as FiTs are justified. Supported by generous FiTs and other support mechanisms, the international solar power industry is increasingly moving towards large-scale systems that take advantage of the available economies of scale to deliver solar power with a lower Levelised Cost of Energy (LCOE) and development-friendly return on investment. Meanwhile, low Australian energy prices and FiT caps of 5, 10, and 30 kW4

• The size and non-dispatchability of small solar power systems inhibits their ability to participate in the wholesale electricity market, in which generators should be at least 5 MW

currently inhibit development of large-scale systems on our shores. As feed-in tariffs are a policy measure that lie beyond CSPS developer’s control, this dissertation focuses upon currently available incentives. In doing so, this dissertation quantifies opportunities for solar power stations under existing conditions, and may describe and quantify the level of support needed if Australia is to develop capacity in large-scale solar energy deployment.

5. The CSIRO is currently investigating the creation of ‘virtual power stations’ by aggregating nearby small renewable energy systems6

• Internationally, solar power stations of the megawatt range are growing increasingly more common, yet there are few large solar power stations in Australia. Above 5 MW, participation in the NEM becomes feasible, budgets become large, and - from a developer’s perspective - maximising revenue becomes more important than maximising efficiency. Energy storage might assist in obtaining maximum economic value from harvested solar energy, by dispatching energy when the NEM price is at its highest – a postulation assesses by this paper. This option is immediately available to developers such as Worley Parsons, who announced plans to investigate up to 34 $1b solar power stations of 250 MW capacity

. Incorporating telecommunications and some energy storage, the diversity of renewable energy sources and locations may increase aggregate system size and reliability to levels that allow smaller solar power systems to access the premium rates offered by the wholesale market. This dissertation may demonstrate the value of accessing the NEM through this mechanism, and the results concerning levels of energy storage will also be relevant to ‘virtual power stations’. For the moment, CSIRO’s research is not commercialised, meaning that solar system developers must rely on other mechanisms for accessing increased tariffs, such as the wholesale market, and the large but limited capacity off-grid diesel market.

7.

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With the above in mind, this dissertation shall investigate the level of energy storage that achieves the greatest financial value of delivered energy in a solar power station. Towards this outcome, this study shall assess optimal energy storage levels through a financial analysis that balances the purchase, operation, and maintenance of standard energy storage technologies with the value they create by delivering energy at a later moment in time. More explicitly, it uses the Solar Advisor Model (SAM) software to investigate a 250 MW trough CSPS connected to the NEM and answer the following 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? Given that energy storage costs money, what is the most cost-effective investment in energy storage?

The dissertation is broken into two parts. The first provides a background to Concentrating Solar Power (CSP), including a literature study. The second concentrates upon the metric study of CSPS optimisation.

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Part 1: Background and Literature Survey

2 Concentrating Solar Thermal Power Stations

Figure 1: Concentrating Solar Thermal Power Station Types8

As illustrated in

Figure 1, there are a number of different technologies that are classified as CSPS. These include:

• towers, in which an array of mirrors focus light onto a centralised receiver,

• dishes, in which a parabolic dish concentrates light onto a central focal point,

• troughs, in which a parabolic trough concentrates light into a linear receiver tubeA

Additionally, the CSPS receiver can be photovoltaic or thermal in nature

.

B

From a high level, CSPS consist of a collector field, energy storage, and power generation blocks, as depicted in

. This dissertation investigates only parabolic trough thermal CSPSs as they are considered proven technology.

Figure 2.

A A heliostat array of mirrors may achieve simular effect to a parabolic trough, concentrating light onto a centralised receiver tube; for example Ausra’s technology. See http://www.ausra.com.au/ B CSP cogeneration can use both PV receiver with thermal cooling water used as low-temperature process heat, but heat demands of the scale applicable to CSPSs are uncommon.

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Figure 2: CSPS overview9

2.1 Components

There are many technical variables involved when designing a CSPS. These include the relative size of the solar field to the power block, the amount of energy storage, the working fluid used in the receivers and storage vessels, and many others. Each has a significant impact upon the operation of the plant, its upfront and ongoing cost, and the cost of the electricity it produces.

The following sections explore in more depth the available technologies and the choices available in parabolic trough CSPSs.

2.1.1 Solar Field The solar field consists of a large number of reflective parabolic troughs, each with a receiver tube, a single-axis tracking mechanism, and associated services.

The parabolic trough collector is responsible for intercepting the solar radiation and concentrating it onto a linear receiver. Temperatures high enough to generate steam are achievable through concentrating the solar energy by a factor typically in the hundreds. The parabolic trough collector is a crucial part of the system, and various types of collector exist. Because this dissertation is focussed more upon storage and economics, variations in trough performance are beyond the scope of this dissertation, and thus receive no further mention.

A heat transfer fluid flows through the receiver tube, transporting the heat from the receiver to the power block, via heat exchangers and energy storage in most cases. Important properties of the heat transfer fluid include its ability to withstand the high temperatures involved, its corrosiveness, its freezing temperature, and its heat capacity. These are described in the following sections.

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2.1.1.1 Receiving Fluid The heat transfer medium transports the sun’s concentrated energy from the parabolic trough to the storage vessel and/or power generation block. The are a number of choices of receiver heat transfer fluid available, with the key properties being specific heat capacity, greatest operational temperature, freezing point, pressure, and corrosiveness.

Some of the more commonly used receiver fluids are Solar Salt, Caloria, Hitec XL, VP-1, Hitec, Dowtherm Q, Dowtherm Rp, and Therminol; the properties of some of which are presented in Figure 3.

Figure 3: Characteristics of some Heat Transfer Fluids10

Currently, most collector fields use a synthetic thermal fluid in the collectors. The main problem with synthetic oils is decomposition of the oil during operation, which requires hydrogen absorbers “getters” in the receivers. However, molten salt’s high heat capacity and maximum operable temperature is driving research towards its direct use in receivers. This can result in a reduction in pumping energy, as well as an increased plant efficiency through higher receiver temperature

11

Another research direction is towards the direct steam generation (DSG) in receivers, offering benefits of higher temperatures than thermal oil, cheaper working fluid, simpler plant configuration (as depicted in

. Anti-corrosive properties of receiver materials are required when using molten salts in the receiver.

Figure 4), and reduced environmental risk12. The pressures involved (60-100bar) increase the weight of the collector, and demands upon valves and fittings. Even so, a reduction of LCOE of 8% is expected13. The challenges involved in DSG include solar field control under radiation transients, and uneven heat transfer at the absorber pipe14.

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Figure 4: Direct Steam Generation's Simpler Configuration15

The choice of thermal fluid can have significant impact upon the cost and energy production of the CSPS, and there is an interplay between the choice of receiver and storage mediums. Using the same fluid in the solar receiver as in the thermal energy storage can avoid the expense (upfront and maintenance) and inefficiency of the heat exchangers, but the relative costs of fluids as well as the energy needed to keep them from freezing in the receivers overnight has impact upon the energy balance.

As many of these heat transfer fluids freeze at temperatures below 100°C, prevention of freezing is a key issue, as the forced thawing of such an occurrence can cost time and money at best, and damage the entire collector field at worst. For example, failure of a circulation pump can lead to freezing of HiTec XL within 35 minutes16

Freeze protection can be achieved in a number of ways: ‘cold’ salt can be circulated, the salt can be directly heated through resistance, and impedance heating or field draining can be used during maintenance. Obviously, all of the above methods use energy in some form; overnight freeze protection requirements for a number of proposed thermal fluids is presented in

.

Table 1. The method of freeze protection can thus impact upon the net energy available for export and consequently the project revenue.

Table 1: Supplementary thermal energy demand for overnight freeze protection17

Fluid

Annual supplementary heat demand (hours/year)

Temperature

Therminol 10 Sandia Salt 600 130°C Hitec/ Hitec XL 2300 175°C Binary salt 4200 250°C

2.1.2 Thermal Energy Storage Incorporation of thermal Energy Storage (TES) into CSPSs brings multiple benefits:

• Greater power station availability

• Electricity production later in the day (as depicted in Figure 5), often meaning generation during periods of higher electricity prices which generally occur after the midday peak in solar radiation

• Dispatchable power, a significant benefit to the network and power station operator

• Minimisation of energy losses during generator start-up and optimised generator operational efficiency.

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These benefits can mean improved project economics, but not necessarily so.

Figure 5: Effect of Incorporating storage into power production18

Just as there are a number of receiver heat transfer fluids, each with their own implications upon the CSPS, there are a number of thermal energy storage media, the choice of which is explored in

2.1.2.1. Interacting with the choice of energy storage medium is the energy storage tank configuration, detailed in Section 2.1.2.2

2.1.2.1 TES media A number of different means of storing thermal energy have been used in existing CSPSs or are under development. The thermal storage fluids options are essentially the same as those available in the receiver, as are the design criteria:

• “High energy density (per-unit mass or per-unit volume) in the storage material

• Good heat transfer between heat transfer fluid (HTF) and the storage medium

• Mechanical and chemical stability

• Compatibility between HTF, heat exchanger and/or storage medium

• Complete reversibility for a large number of charging/discharging cycles

• Thermal losses

• Ease of control” 19

Using the same fluid in the receiver and storage tank can eliminate heat exchangers that reduce efficiency and are expensive to purchase and maintain. This can therefore result in upfront and ongoing cost savings, but a balance must be struck between costs and performance, particularly as the receiver and storage tank have requirements of optimal material property.

2.1.2.2 TES configurations There are two commonly used tank configurations for TES: 2-tank and Thermocline. These are depicted in Figure 6, and explained in further detail in this section. A heat exchanger is always used in the steam creation loop unless the solar field is configured to directly create steam (which is a topic of current research). Although Figure 6 does not depict them, heat exchangers are required if differing media are used for heat transfer and storage and are noted in the following paragraph.

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2-tank (direct) TES schematic Thermocline (direct) TES schematic Figure 6: Energy Storage Tank Configurations20

The most energy efficient method of storing the thermal energy is in two tanks: one ‘hot’ and one ‘cold’. When energy is being transferred from the solar field to the energy storage, fluid flows from the cold tank to the hot tank via the receiver-to-storage heat exchanger. When energy is being transferred from the energy storage to the power block, fluid flows from the hot tank to the cold tank via the storage-to-generator heat exchanger. However, as the TES fluid is costly, alternative storage methods have been developed.

Thermocline TES use a single tank, in which a heat gradient is maintained. Cold fluid is drawn from the bottom of the tank, heated in the receiver-to-storage heat exchanger, and placed back into the top of the tank. To generate power, hot fluid is drawn from the top of the tank, delivers its heat in the storage-to-generator heat exchanger, and placed back into the bottom of the tank. The major benefit of thermocline systems is the ability to use fillers; thermal energy storage materials that sit within the tank and which are far less expensive than the typical thermal storage fluids. Quartzite rock with silica sand have been demonstrated to be suitable filler materials21

Concrete blocks and Phase Change Materials have also been identified as suitable energy storage materials

.

22

2.1.3 Power Block

.

In most CSPSs, the power block is a Rankine cycle turbine, in common use in the power generation industry. The choice of wet or dry cooling for heat rejection can significantly impact upon the efficiency of plant operation and LCOE. In the simulation, wet cooling has been used as it produces better performance and financial results. However, as the most favourable sites for CSP are those that are located in arid regions, wet cooling may be impractical – a situation compounded by Australia’s frequent water shortages.

2.2 Design This section details the parameters involved in CSPS system design, particularly those pertinent to the SAM software which is used for the modelling described later in the Dissertation.

2.2.1 Solar Multiple and Energy Storage The solar multiple (SM) is the ratio of the solar field peak output power to the rated generation capacity of the generation block. For a fixed power generation capacity, the solar multiple therefore establishes the size of the solar field.

If the solar field peak capacity is smaller than the generator capacity, then there is no need for energy storage (except to delay output if desired), as the generator can make use of all solar

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radiation delivered to it. When the solar field capacity is larger than the generator capacity, the excess energy can either be dumped and lost, or placed into storage for later use. Even when storage is not incorporated, it typically makes economic sense to size the generator slightly smaller than the solar field capacity, as significant capital costs can be saved by choosing a smaller generator, so long as the amount and value of dumped energy is not great. – there being an economic optimum ratio, as illustrated in Figure 7.

Figure 7: LCOE with respect to solar multiple and hours of TES – Source SAM version 2009.10.2

For systems that incorporate TES, when the solar field produces more power than the maximum generator output power, the excess solar energy can be stored for later despatch. Incorporation of energy storage makes the search for an economic optimum more complex, as:

• larger solar multiples become viable,

• energy storage size and technology strongly influence capital cost, and

• energy dispatch methodology influences the net value of delivered energy.

The energy flows that occur when TES is incorporated are illustrated in Figure 8.

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Figure 8: Energy Flows for CSPS that incorporate TES23

The size of the energy storage unit (hTES), typically expressed in hours of operating the generator at its rated capacity, is another fundamental design criterion, and there is a strong relationship between the economically optimum solar multiple and hours of energy storage, as depicted in

Figure 7. Having 12 hours of energy storage is not useful if the solar field never generates more power than the generator can handleC

The time at which peak power prices are available

; similarly, much energy will be lost if a solar multiple of 10 is used unless there is a large-capacity energy storage unit.

D also strongly establishes the financially-prudent energy storage amount, although achieving a minimum LCOE is another influence. The losses incurred in the storage vessel and heat exchangers can result in a reduced thermal efficiency of plant operation. However, the increase in usability of the energy within the power block can counteract the efficiency losses, whilst achieving significant increases in other highly-desirable power stations metrics, principally power station availability and value of energy deliveredE

The dispatch strategy for stored thermal energy storage can also influence the end value of the power delivered. Energy produced in the morning may be stored for afternoon dispatch if the price of electricity is anticipated to be greater later in the day; however, as the energy storage approaches its capacity, then some interplay between the weather forecast and the price forecast may dictate whether any forthcoming radiation is put directly towards generation, or stored for later use.

.

2.3 Operation A CSPS with storage that is operated with the objective of attaining the greatest energy efficiency or least LCOE typically would base the output power upon the amount of energy in the storage and the incident solar radiation at that time. In the morning, solar energy is typically stored, at least until sufficient buffer is available to ride out any intermittent clouds. In the afternoon, the storage is used to extend full-load generation once solar radiation levels decreaseF

C Unless the peak power price is at midnight – ie the TES achieves a time shift only

. In between, there is a high level of flexibility about how the energy can be stored, dispatched, or even bypass storage. While the

D In a strict sense, the relationship between peak power price and month of year influences optimal design of energy storage levels, as attempting to meet peaks that occur in the winter afternoon may result in an oversized solar field. E This demonstrates the value of searching for an economic optimum rather than solely optimum plant efficiency. F An energy dispatch methodology to attain minimum LCOE would still store solar radiation in order to operate the generator at its maximum efficiency. If the energy prices peak during the late afternoon, storage can also be used to increase revenue.

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optimum control algorithm for a CSPS is a presently unpublished topic, it appears that control decisions can have significant impact upon CSPS energy efficiency and revenue.

3 World Status Trough CSP is considered a proven technology because of its history of reliable operation. In response to the 1970s oil shocks, nine “Solar Energy Generating Systems” (SEGS) were commissioned in the USA over the period 1985-1991. representing a total of 354 MW, the largest individual SEGS plant was 80 MW24

In addition, a number of CSP trough projects are under construction (see

, and the 9 plants cumulatively represent 354 MW of time-proven generation. These were developed as commercially viable projects at the time, though investment in CSP subsequently stalled until recent years’ renaissance. The 64 MW Nevada Solar One project in Las Vegas, USA was commissioned in 2007. The 50 MW AndaSol 1 project in Granada, Spain was completed in November 2008.

Table 2), and many more are slated for development.

Table 2: trough CSP under developmentG

Name

Country Size Martin Next Generation Solar Energy Center USA 75 MW Andasol 2 solar power station Spain 50 MW Andasol 3 solar power station Spain 50 MW Alvarado/La Risca 1 solar power station Spain 50 MW Solnova 1 solar power station Spain 50 MW Solnova 3 solar power station Spain 50 MW Energia Solar De Puertollano SA Solar Plant Spain 50 MW Extresol 1 solar power station Spain 50 MW Kuraymat Plant Egypt 40 MW Hassi R'mel integrated solar combined cycle power station Algeria 20 MW Keahole Solar Power Hawaii 1 MW

3.1 Australian Situation At the time of writing, there was only one concentrating solar thermal power station operating in Australia. Developed by Ausra (then Solar Heat and Power), a 2 MW linear Fresnel lens provides pre-heated steam to an existing coal power station as a demonstration plant25

• Solar Systems deployment a number of concentrating PV dishes. Prior to Solar System’s collapse, a number of concentrating PV heliostat projects were in development.

. Other large-scale concentrating solar power systems include:

• Lloyd Energy’s 3 MW station in Lake Cargelligo, under construction.

• The ANU’s Big Dish test and demonstration installation.

• EnviroMission’s intended solar tower in Mildura26

A number of projects have also been announced. A trough CSP has been announced for Cloncurry, QLD, to be developed by SMEC and Lloyd Energy Systems. Worley Parsons announced plans to investigate up to 34 $1b solar power stations of 250 MW capacity

27

G Correct at time of writing,

, and is presently identifying the

http://en.wikipedia.org/wiki/List_of_solar_thermal_power_stations

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most appropriate sites and technologies. The Federal Government’s Solar Flagships program aims to facilitate the creation of four solar farms of varying technology, which should result in at least one Australian CSPS.

Proposed non-photovoltaic solar power stations are presented below:

Table 3: Proposed non-photovoltaic Solar Power Stations28

Name & state

Owned Technology Capacity (kW)

Mingenew WA Worley Parsons Solar Thermal 250000 Neds Corner Vic Enviromission Ltd Turbine 200000 Mildura Vic Solar Systems Generation Pty Ltd(80%)/

TRUenergy(20%) Solar Concentrator 154000

Buronga NSW Enviromission Ltd Turbine 50000 Point Patrson SA Acquasol Solar Concentrator 50000 Parkes NSW New Horizon Energy Parabolic Troughs 30000 Whyalla SA Solar Oasis Solar Dish 22200 Cloncurry Qld Ergon Energy Steam Turbine 10000 Stanwell Qld Austa Energy Corp Ltd/Stanwell Corp

Ltd Solar Concentrator 5000

Lake Cargelligo NSW

Lloyd Energy Systems Unknown 3000

Eraring NSW Pacific Power/ANUTECH/Transfield P/L Steam Turbine 2300 Mica Creek Qld CS Energy Ltd Steam Turbine 2000 Alice Springs NT Public Solar Concentrator 1300 Kalgoorlie WA City of Kalgoorlie-Boulder Photovoltaic and mirrors 1152 Coober Pedy SA Government Solar Dish 624 Alekarenge NT Alekarenge Community Photovoltaic and mirrors 576 Ti Tree NT Ti Tree Community Photovoltaic and mirrors 192 Kalkarindgi NT Kalkarindgi Community Photovoltaic and mirrors 192 Perth WA Verve Energy Photovoltaic and mirrors 20 Sydney NSW CSIRO Cogeneration 20

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4 CSPS Economics For CSP to be deployed at the level necessary to achieve significant reductions of our electricity supply’s carbon intensity, it must compete with other energy generation technologies, including traditional and ‘clean’ coal, gas, wind, hydro, photovoltaic, and nuclear power. The Levelised Cost of Energy, or total project lifetime costs divided by total net energy output, is one economic metric typically used to compare the attractiveness of different generation technologies. However, investors make decisions for each individual project based upon its Net Present Value (NPV), or Internal Rate of Return (IRR). Of course, there is an interaction between LCOE and IRR, for if the cost of producing solar energy is greater than the price the market will pay for it, then the project will not be profitable and will thus not proceed.

The revenue for a power station that operates in the NEM depends upon the price of electricity at the moment of generation, itself significantly dependent upon the amount of electricity demand at that moment. As seen in Figure 9, the NEM price for Queensland has a somewhat consistent profile, inasmuch that electricity prices tend to be higher in the late afternoon than they are in the middle of the day. This late-afternoon price peak may justify the inclusion of storage, depending upon the relative cost of storage and the cost advantage of delayed output

Figure 9: Average QLD Price by Hour of Day for 2000-2007 (Source NEMMCO)

Wind and photovoltaic power stations generally don’t incorporate energy storage, as the cost of electricity storage typical outweighs any financial benefit achieved29. Such power stations have low operational costs and rely upon intermittent fuel availability, meaning they will generate power whenever the resource permits, and are thus considered as price-takers. Fully dispatchable power stations that use fuels such as natural gas operate only when the price of electricity is greater than the costs of fuel and operational costs. As storing thermal energy is less expensive than storing electrical energy30, and considering the other financial benefits of TES within a CSPS, solar power stations can attempt to gain maximum revenue from that day’s solar resource. In doing so, they are

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still constrained by the price of electricity that is available to them on that day. As such, CSPSs that incorporate TES are still price-takers, albeit with some ability to achieve greater revenue through output control.

The fundamental question in the minds of energy industry leaders is: even given the governmental financial support for Solar Flagships and availability of renewable energy and carbon pollution markets, will CSP compete with other forms of electricity supply, now or in the future? If so, under what input assumptions (e.g. REC price)? If not, what is needed to make CSP competitive? Can the advantages of more easily storable thermal energy provide financial benefits because of dispatchability? This dissertation intends on answering some of these pertinent questions. To this end, the IRR of CSPSs in Australia is investigated using available tools. In particular, the effect of the NEM price upon the IRR is investigated, in order to categorise the profitability of CSP in the absence of a Feed-in Tariff.

Although IRR is the metric by which an individual project’s financial viability can be measured, LCOE is a metric that is more commonly reported in international literature. There is a close relationship between LCOE and IRR; the LCOE must at least be lower than the electricity sale price for a power station to be financially viable. The tendency to report LCOE may occur because the most favourable markets for CSPS have Feed-in Tariffs, or because Power Purchase Agreements may also establish a fixed price of energy. In both cases a profit can be made so long as the LCOE is less than the FiT or agreed PPA price, the aim therefore becomes to minimise LCOE. However, due to the variable NEM price that an Australian CSP receives, the project that seeks maximum financial return must design the power station to obtain the highest IRR. Under these terms, it may be worthwhile investing in more storage if it gains access to significantly greater revenues at marginal extra cost. Bearing in mind that minimum LCOE may not mean maximum IRR in Australia, the following sections summarise available literature on CSP economics.

4.1 Current Not surprisingly, the largest cost in a CSPS is the cost of the collector itself. Figure 10 shows that the cost of 6 hours of TES represents about 15% of estimated total project costs, with the major other cost being that of the turbine itself. In 2007, the price of CSPS projects was found to between US$2,400- $3,000/kW, with corresponding LCOE in the range of 10-12.6 c/kWh (USD)31

4.3. Component

costs are explored in more depth in Section .

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Figure 10: Cost breakdown of 100 WM CSPS with 6 hours storage, US$200732

There are many factors that affect a CSPS’s LCOE, some of which will be explored in greater depth in this dissertation. Chief amongst these variables is the (direct normal) solar radiation resource.

Figure 11 demonstrates that a 13-23% reduction in LCOE is available at sites with annual direct normal incidence solar radiation exceeding 2300 kWh/m2a. Assuming that a similar situation applies to Australia, Figure 12 demonstrates that there are a number of Australian sites that meet this criteria.

Figure 11: Dependency of solar LCOE on DNI resource (free load, 6h storage)33

Solar Field 59%

Thermal Energy Storage

15%

Power Block 10%

Contingency 8%

Balance of Plant 6%

HTF System 2%

Site Work and Infrastructure

0%100 MW CSP Cost Breakdown

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Figure 12: DNI radiation at key Australian Sites34

A quoted CSP LCOE of 10-12.6 c/kWh compares favourably with LCOE at many foreign Simple-Cycle gas turbines (18.7 c/kWh) and Combined Cycle gas LCOE (11.9 c/kWh)

35

Figure 13

. However, hampering the economics of Australian CSP development is the cheap price of electricity. One Australian uranium mining report put the LCOE from a coal power station at 4 c/kWh (AUD) and 5 c/kWh from a combined-cycle gas turbine, as seen in . Unfortunately, even a 23% improvement in LCOE due to high insolation levels is not going to overcome this difference. However, such an LCOE might be able to compete with coal or natural gas if their externalities associated with pollution (shown in Figure 14) were internalised, depending upon the external costs of CSP.

Figure 13: Lifecycle economic costs of electricity generation36

[17]

Figure 14: Lifecycle costs including Environmental Externalities37

4.2 Future

Technology experience curves predict that CSP LCOE will reduce as the cumulative installation capacity increases with time. There are many factors that contribute to this price reduction: as experience with CSPS grows, risks are reduced and investors’ appetite grows for larger-scale systems, driving down costs through economies of scale. Specific improvements to contributing technologies can also deliver better performance with a smaller price-tag. Figure 15 shows a predicted experience curve for CSP, based upon an investigative study into likely price reductions from contributing factors. LCOE reductions of between 13 and 29% are demonstrated in Figure 16 for a CSP with 3 hours of TES, and are calculated from expected small advances in a wide range of contributing technologies. Further technology-driven improvements to LCOE are explored in Section 4.3. S&L and SunLab predict LCOE of 6.2 and 4.3 c/kWh respectively by 202038

. If these predictions prove to be correct, then CSP can clearly provide cost-competitive renewable energy in future, if its deployment is supported today.

[18]

Figure 15: CSPS Experience Curve39

Figure 16: Technology-driven reductions in LCOE40

[19]

4.3 Component Cost and Optimisation There is a myriad of combinations of component technology and overall plant configuration, each with their own impact upon the overall cost and performance of the power station. Some of the options include:

• As mentioned in Section 2.1, using the same working fluid in the receivers and the storage vessel can save the (upfront and ongoing maintenance) expense of the heat exchangers, and improve storage.

• Use of a thermocline system with appropriate low-cost filler material can reduce LCOE by 0.4c/kWh (see Figure 17)

• Using salt as the receiver heat transfer medium can allow increased receiver temperatures, increasing plant operational temperature and thus generator efficiency, reducing LCOE by 1.4-1.7 c/kWh, depending on the operational temperature achieved – see Figure 17.

Figure 17: LEC gains with use of salt as a receiver41

4.3.1 Optimum Level of Storage

Even though there are costs involved in energy storage equipment, and energy losses associated with transfer and storage of heat, incorporating storage can increase project revenues by shifting generation to times with higher energy prices, whilst also reducing LCOE by increasing power station availability and overall plant operational efficiency. Platts42

Figure 18

claims that on a 100 MW power station, incorporating 4 hours TES can increase annual revenues by $2.5M when compared to a plant without storage, raising the average delivered revenue from $51/MWh to $60/MWh (see ).

[20]

Figure 18: Economic Benefits of TES43

The question then becomes what amount (and type) of energy storage maximises the economic attractiveness of the solar power station, whether defined by LCOE or NPV/IRR – a question that forms the primary investigation direction of this dissertation. In

Figure 19, Flagsol places specific costs of storage at $25-$135/kWh, depending on the energy storage medium used; the least expensive being thermocline liquid salt.

Figure 19: Specific Costs of Different TES Concepts44

Due to economies of scale and material efficiencies, larger storage capacities have lower cost and fewer losses per unit of energy storage, as shown in

Figure 20.

[21]

Figure 20: Specific Storage Cost45

Larger storage capacity reduces the amount of energy that is dumped when storage vessels are full. However, as shown in

Figure 21, there are diminishing returns associated with increasing the amount of energy storage; beyond a certain point LCOE can actually increase with greater amounts storage.

Figure 21: Selection of Storage Size46

Brosseau

47

Figure 19 demonstrates that the advantages of TES are dependent not only on the materials used

in the storage (see ), but also on the configuration and operating temperature. Figure 22 demonstrates very little benefit to LCOE from incorporating two-tank TES when receiver operational temperatures are 391°C; in comparison Figure 23 shows that significantly reduced LCOE may result from including storage when thermocline configuration is used with 500°C molten salt in the receivers.

[22]

Figure 22: Indirect Two-tank TES at 391°C48

Figure 23: Direct Thermocline TES at 500°C49

Ecostar demonstrates that price of electricity can affect the LCOE

H

Figure 24, and thereby influence the

optimum design. The search for an optimal solar field size and TES capacity depicted in yields plants with large solar fields and large storage capacity when electricity rates are fixed (typically by government renewable energy incentive schemes), but smaller solar field and storage size when electricity rates vary during the day.

H Presumably this is because even though the ‘fuel’ is free in a CSPS, the night-time parasitic load is a cost within the project – if no revenue can be achieved in night-time operation, then less storage is warranted.

[23]

Figure 24: Comparison of LCOE with fixed and variable electricity sales prices50

In a similar way, this dissertation investigates the optimum CSPS design within the NEM’s varying electricity price. Whilst minimising LCOE is a worthy goal, optimising a project for greatest possible IRR is a more likely outcome of project design, which drives projects towards obtaining maximum revenue from minimum investment.

4.4 Australian National Electricity Market Costs The Australian National Electricity Market (NEM) is the electricity transmission and distribution network that connects the eastern and southern states of Australia (see Figure 25). The NEM operator facilitates network balancing, forecasts demand and prices into the future, and provides the market mechanism for the transfer of electricity and payments between generators and retailers.

Figure 25: NEM extents51

The NEM wholesale price of electricity is updated in 5 minute blocks, which varies in a non-linear manner with electricity demand. Based upon the forecast price of electricity and the generators marginal cost of production, each generator bids their willingness to generate a certain amount of energy, and is called upon to do so if the electricity price exceeds their bid. The wholesale price is then aggregated into half-hour purchasing intervals, which are paid to the generators by the purchasers of electricity.

[24]

The NEM price varies with demand, according to market principles of supply and demand (see Figure 26). However, Figure 27 demonstrates that the relationship is non-linear, with significant peaks in NEM prices occurring. Price peaks occur in particular when supply is constrained – particularly by environmental events such as bushfires and drought damaging power lines or reducing hydro-power generation capacity – or when demand is particularly high – which often occurs due to air conditioner use on particularly hot days (see Figure 28). However, although such price peaks can strongly influence datasets of average price, they are infrequent occurrences (see Figure 29), with most days following a more typical hourly profile, albeit one that varies from month to month (see Figure 30).

Figure 26: NEM Average RRP by Demand and Hour, QLD 2005-06 (Data Source: NEMMCO)

Figure 27: NEM Price vs Demand - QLD 2007 (Data Source: NEMMCO)

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Figure 28: NEM Price Peaks Explained52

Figure 29: NEM Price Distribution Curve (Data Source: NEMMCO)

[26]

Figure 30: Average NSW Price by Hour of Day for Each Month (Data Source: NEMMCO)

Of greatest interest for CSPSs is maximising the value of the power generated. Although Figure 31 demonstrates that power price is generally higher during daylight hours and that there is thus some correlation between solar radiation and power price, it is clear that the peaks in solar radiation and wholesale electricity price do not coincide. This fact provides the value for energy storage, investigated in detail in Part II.

Figure 31: NEM Price & Solar Radiation vs Time of Day, NSW (Data Source: NEMMCO and EnergyPlus)

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Part II: Investigation

5 Research design The dissertation makes use of CSP simulation software to model the input and operational costs of various CSP configurations. A financial spreadsheet that incorporates NEM electricity pricing data is built around the 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 challenge faced by CSPS operating on the NEM is the variable price of electricity in the absence of a feed-in tariff. This variance has a daily NEM-price profile that suggests that storage of solar energy for delivery later in the day might improve the financial performance of a NEM-connected CSPS. The modelling took a selected year’s hourly NEM price and assumed it applied in the following thirty years of operation. However, the NEM-price variance also spans years, meaning investment decisions must make predictions of future years’ NEM price. 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. This also allows investigation of the relationship between financially optimum levels of storage and the NEM price profile.

In order to ascertain whether incorporation of storage into a NEM-connected CSPS would deliver greater income stream, a preliminary investigation was conducted prior to commencing the dissertation. This investigation used a simple dot-product multiplication between hourly solar radiation and NEM price to approximate the magnitude of revenue that might be expected. Storage was modelled by slipping the NEM price by the associated number of hours, and the approximated revenue metric used to compare the outcome. This investigation is presented in Section 7.

Whilst the preliminary investigation established that there was a possible case for incorporating storage, it had limited ability to determine the financially-optimum level of energy storage to include. Determining the optimum amount of energy storage requires a financial model that takes into account upfront and ongoing costs, revenue streams from sale of electricity and Renewable Energy Certificates, taxation and depreciation, as well as a sophisticated CSP model.

The more detailed investigation made use of analysis software developed by the National Renewable Energy Laboratory (NREL) in the USA. Known as Solar Advisor Model (SAMI), it incorporates sophisticated models of CSP components and their costs, runs detailed thermal energy analysis using TRNSYS, and provides economic analysis53

An investigation of the variance of IRR with respect to inputs such as solar multiple, hours of thermal energy storage NEM price, and location was performed. To model the CSPS proposed under the solar flagships program, a 250 MW CSPS was chosen. LCOE was investigated for the purpose of comparison to international literature. To enable greater comparison between LCOE results and

. However, SAM has limited ability to incorporate hourly pricing data. As such, a financial spreadsheet was built around the output of SAM, thereby allowing sensitivity analysis to be performed upon input variables such as the price of collectors and cost of storage.

I SAM version 2009.10.12 was used

[28]

values from international literature, the LCOE was not subsidised in any way. A currency exchange rate of 1USD=0.8AUD was used.

IRR was investigated as the basis by which various plant configurations could be compared. NPV can accurately calculate poor financial returns, but is not useful as a comparison metric between differing investment amounts, as the value of NPV varies with the amount of investment. In contrast, IRR can be used to fairly compare the financial return on differing investment amounts. However, there are limitations when using IRR as a reference metric. Scenarios with poor financial performance may not be profitable even if a 0% (or less) discount rate is used – hence IRR is only an appropriate metric for situations in which a profitable outcome is possible.

For this reason, an optimistic scenario was chosen as the base case. It was found that a 33% government contribution to initial capital cost (as proposed by the announced 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. The sunniest NEM-connected location in Australia was chosen as the base case site of the power station, with a sensitivity analysis performed upon location.

[29]

6 Input Data

6.1 National Electricity Market Half-hourly electricity price and demand data from 1999 to present day is readily available from http://www.nemmco.com.au/data/market_data.htm. As SAM produces hourly output data, the half-hourly NEM price was averaged to create an hourly dataset for each state in the years 2004-2007.

6.2 Weather and Solar Radiation There are a number of data sources of Australian weather and solar radiation. Ultimately, the SAM requires site latitude and longitude, as well as hourly input data for solar azimuth, direct solar radiation, dry and wet-bulb temperature, and windspeed. Such information is available for ‘typical years’ at a large number of NEM-connected locations. However, accurately measured half-hourly data is needed to investigate whether a correlation between NEM price and solar radiation exists. This real-time data is only available at a limited number of sites in Australia. In contrast, satellite-inferred data is available for most meteorological stations in Australia; however, the data is less accurate and only available as a daily total – it is thus useful for comparative purposes only. The sites of interest with available data are presented in Table 4 below.

Highly accurate (bankable) solar radiation data is necessary to obtain the investors needed for a CSP project to proceed. However, for this dissertation, the lack of available measured half-hourly data at likely CSP sites meant that TMY data was used instead. It is recognised that decoupling the NEM price and the true solar radiation is a significant step that carries weighty implications should a strong correlation between NEM price and solar radiation exist. Thus an investigation into correlation between NEM price and solar radiation data at the sites with half-hourly solar radiation measurements was performed. It found that there was no discernable correlation between the two variables. The daily sum of global and DNI radiation is plotted against the daily sum of NEM price for Rockhampton 2005 in Figure 32 – with subfigures a through to c showing more detailed insight into the area where most data points are clustered.

This shows that although there is a correlation between the average hourly values of radiation and price, as shown previously in Figure 31, there is very little correlation between the daily solar radiation and NEM price. Investigations also showed a lack of correlation between the half-hourly measurements of these variables. However, there is a high correlation between the TMY data and averages of the measured values. Thus, use of TMY data for the purposes of this investigation should not significantly impact upon its findings, and significantly expand the set of available locations. The impact of this decoupling was further assessed with a sensitivity analysis into weather dataset (Section 8.1.8).

[30]

Figure 32 (a, b, c): Correlation between 2005 Daily Radiation at Rockhampton and QLD NEM Price (Datasource NEMMCO and Bureau of Meterology) – Half hourly measured data used.

Table 4 shows that the accurate half-hourly measured data at NEM-connected sites only exists in Adelaide, Mt Gambier, Rockhampton, Wagga Wagga, Melbourne, and Mildura. In contrast, TMY data shown in Figure 33 shows that these sites do not have the greatest solar resource, and that CSPS are therefore unlikely to be sited at these locations unless the power price (and profile) in those states is sufficiently high to overcome the performance penalty.

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Frequency Measurements of interest to study

Measured Locations of interest to study

Half-hourly Direct Solar Radiation, Global Solar Radiation, wind speed, dry-bulb temperature, wet-bulb temperature

Bureau of Meteorology Ground-based station measurements

SA: Adelaide, Mt Gambier QLD: Rockhampton NSW: Wagga Wagga Vic: Melbourne, Mildura

Daily Global Solar Radiation Bureau of Meteorology satellite-inferred data

Vic: Mildura NSW: Jindabyne, Tharwa, Sydney, Parkes, Bathurst, Morisset, Broken Hill QLD: Brisbane, Rockhampton, Mt Isa, Cloncurry SA: Adelaide, Port Augusta, Whyalla, Leigh Creek, Woomera

Typical Mean Year

Temperature (dry&wet bulb), direct normal solar radiation, global solar radiation, wind speed

Australian Greenhouse Office, via EnergyPlus websiteJ

NSW: Armidale, Coffs Harbour, Dubbo, Mascot, Moree, Nowra, Orange, Richmond, Sydney, Thredbo, Wagga Wagga,

Williamtown QLD: Gladstone, Longreach, Mackay, Mt Isa, Oakey, Rockhampton, Townsville SA: Adelaide, Ceduna, Mt Gambier, Mt Lofty, Woomera Vic: Ballarat, Cape Otway, East Sale, Melbourne, Mildura, Moorabbin, Warrnambool

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Temperature (dry&wet bulb), wind speed

Energy Plus Weather Search FacilityK

VIC: Avalon, Melbourne SA: Adelaide, Woomera,

QLD: Townsville, Rockhampton, Mt Isa, Coolangatta, Cairns NSW: Sydney, Dubbo, Richmond, Tamworth, Wagga Wagga ACT: Canberra

Table 4: Real-time Historical Weather Measurements in NEM-connected Locations

J http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=5_southwest_pacific_wmo_region_5/country=AUS/cname=Australia K http://apps1.eere.energy.gov/buildings/energyplus/cfm/weatherdata/weather_request_search.cfm?sortKey=country&opt=1

[32]

Figure 33: Annual Direct Normal Incidence Radiation (source: EnergyPlus)

6.3 Solar Advisor Module Parameters

6.3.1 SAM technical inputs The simulation used SAM version 2009.10.2.

The 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

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275 MW

Power Cycle As per Library: SEGS 80MWe Turbine

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Two-tank Only available Option

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SCE (A Californian utility pricing structure included in SAM)

Default

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Linked to hTES as per SAM user guide Table 2454

Parasitics SEGS VIII Reference Default

[33]

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.

6.3.2 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 inflation

0% Sensitivity analysis performed

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 value O&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 consultants55

6.4 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.

[34]

• 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 cashflow (NPV) to zero

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

[35]

7 Preliminary Investigation: Greatest Delivered Value of Solar Energy

As part of the preliminary dissertation study, a brief investigation into whether certain sites in Australia may be better for CSP was undertaken - not just because of high solar radiation but perhaps owing to a greater coincidence of NEM price with solar radiation that may lead to greater value of delivered power with less cost invested in storage. Notwithstanding the limitations of the study, it demonstrates that energy storage does provide a higher overall revenue stream, and suggested that there was an optimum level of storage.

7.1 Correlation between NEM price and Solar Radiation The relationship between solar radiation and NEM price was first introduced in Figure 31, and is repeated below. It is clear that the peak power price and peak solar radiation do not coincide, thus suggesting that incorporating energy storage may increase the value of the delivered solar electricity by delaying generation output until electricity prices are higher.

Figure 34: NEM Price & Solar Radiation vs Time of Day, NSW

Notionally, the revenue generated by a CSPS can be considered to be approximately proportional to the product of solar radiation and wholesale electricity price. The effect of incorporating storage into a CSPS can be simplistically modelled by applying a time-shift delay to the solar radiation, implying power generation occurred at a later time. The metric’s limitations include ignoring the effects of:

• energy loss in storage heat exchangers

• energy loss in storage vessel

• energy dumping when storage is full

• varying plant efficiencies at differing radiation and ambient temperatures

• scheduling dispatch of energy to obtain the greatest achievable value

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An accurate assessment of the value of delayed dispatch through ideal energy storage must maintain the correlation between NEM price and solar radiation, as there is some influence of the latter on the former, particularly when electric heaters and air conditioners are involved. This limits the number of locations that can be investigated to those with half-hourly solar radiation data available, detailed in Table 4.

Figure 35 presents the product of direct normal incident solar radiation at Wagga Wagga with NSW NEM price at a delay of 0-8 hours for various years. It demonstrates that delaying the dispatch of solar energy from its time of creation could increase the amount of revenue available by 25%. The value of delivered energy is maximised by a delay of energy dispatch between 3.5-5 hours, mostly depending upon the NEM price volatility of that year. However, the NEM price variation from year to year has a greater influence on the outcome than the amount of energy storage. This, in line with the infrequence of price peaks illustrated in Figure 29, suggests that although CSPS may utilize and benefit from price peaks, designing around the more common daily price trend is a less risky investment; so too forecasting the NEM price is sensible prior to investing in building a CSPS.

Figure 35: NSW Value of Energy with Delayed Output

The variance of revenue with location and hours of TES is presented in Figure 36. It illustrates that the theoretical optimum amount of storage (ignoring both fluctuations in NEM price trends and CSPS thermal characteristics) varies with location. It also demonstrates that some sites are more favourable for revenue generation than others, regardless of the amount of storage involved. For 2004, a CSPS in Rockhampton performed best and 2.5 hours of TES was optimum; Adelaide (2 hours TES) and Wagga Wagga (4 hours TES) were next best; with Mt Gambier (5 hours TES), Mildura (3.5 hours), and Melbourne (4 hours) being the least favourable sites of the sample data set. Graphs of the direct solar radiation and electricity price for each hour, averaged over 2004, are presented in the following figures. Regardless of the limitations of the model, this investigation shows that revenue generation at some sites is more sensitive to the amount of TES than at others.

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Figure 36: Notional Value of Delivered Energy vs Hours' Storage - 2004 Real Data

Figure 37: SA Direct Solar Radiation and NEM price hourly profile - 2004 average

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Figure 38: Qld Direct Solar Radiation and NEM price hourly profile - 2004 average

Figure 39: Vic Direct Solar Radiation and NEM price hourly profile - 2004 average

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Figure 40: NSW Direct Solar Radiation and NEM price hourly profile - 2004 average

Notwithstanding the limitations of the analysis, the metric does serve to illustrate that:

• there may be financial benefits to incorporating energy storage • there is some optimum level of storage needed to obtain maximum value of delivered

electricity • some sites are more valuable, not only because of their greater solar radiation, but possibly

also because of closer alignment between solar radiation and energy price.

More detailed further analysis into the optimum level of energy storage at a variety of Australian sites using the Solar Advisor Model is presented in Section 8. The sophisticated simulation model removes the limitations of the previous analysis, but there are some limitations to SAM which influence its analysis:

• the dispatch of energy cannot be influenced by the real-time NEM price, (although an allegory can be investigated by a static time-of-use model)

• SAM uses Typical Reference Year solar data rather than historical real-time data, which increases the number of sites that can be investigated at the expense of losing the correlation between the solar radiation and NEM price datasets. However, as shown by Figure 32, the correlation between radiation and NEM price isn’t strong, so losing the correlation between the two should not be too great a loss – a supposition investigated in Section 9.2.5.

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• Because SAM’s economic analysis uses a fixed price of power rather than half-hourly variable price as is the case in the NEM, the economic analysis is limited. This has been countered by dot-multiplying the output power from the power station with the power price at that time.

These limitations restrict the level of depth of analysis into the optimum level of energy storage that can be entered into within SAM.

8 Results

8.1 Simulated Solar Power Station Operation

8.1.1 Base Case: Longreach, Queensland, 2007 NEM DataSet, $350/m2 solar field, $40/kWhth storage, $50/REC

Figure 41 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. SAM produces absolute results that are less relevant to Australian conditions, but the relative results also point to a minimum LCOE for a SM of 2 with 6 hours of storage (See Figure 42).

Figure 41: Real LCOE for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and no government contribution

[41]

Figure 42: SAM output of US$ LCOE from 250 MW Longreach CSPS with discount rate 10%

Although the LCOE may have been least for a SM of 2 and 6 hours of TES, the IRR from the project is greatest for a SM of 2.4 and 6 hours of storage – for the 2007 QLD NEM price data set. Even the significantly higher LCOE that results from increasing the SM to 3 (with 6 hours of storage) has a comparably favourable IRR.

Figure 43: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2007 QLD NEM dataset

[42]

In absolute terms, even with a 50% government contributionL

8.1.2 Sensitivity Analysis: NEM DataSet

, these IRRs would be considered to be fairly low by investors wary of such a large project, the first of its kind in Australia.

The following section investigates the IRR that can be achieved with varying NEM-price datasets. Figure 44 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.

Figure 44: Average Queensland NEM Price Hourly Profile for 2004-2007

8.1.2.1 2006 In contrast to using the 2007 NEM dataset, the IRRs obtained using a 2006 NEM dataset are very poor (See Figure 45). This is likely because the power price for that year was far lower on average. The incorporation of larger amounts of storage is rewarded, likely due to the late-afternoon peak in power prices for that year.

L Recall that an unsubsidised LCOE is presented throughout this document for comparison purposes to international literature. A 50% subsidy of the upfront cost is used in the IRR analysis in order to produce positive IRRs across a range of situations, so that optimum storage levels can be investigated.

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Figure 45: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2006 QLD NEM dataset

8.1.2.2 2005 In contrast to using the 2007 NEM dataset, the IRRs obtained using a 2005 NEM dataset are very poor (See Figure 46). This is likely because the power price for that year was far lower on average. Less storage is needed to obtain IRRs exceeding 1%, probably as the peak power price in that year was at 4pm.

Figure 46: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2005 QLD NEM dataset

8.1.2.3 2004 In contrast to using the 2007 NEM dataset, the IRRs obtained using a 2004 NEM dataset are not exceptionally good, but are better than in 2005 and 2006. The maximum IRR plateaus at 3.5% for both smaller plants with less storage and larger plants with more storage. This is likely due to the rounded shape of 2004’s NEM-price profile.

[44]

Figure 47: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2004 QLD NEM dataset

8.1.3 Sensitivity Analysis: Cost of Storage Simulations were re-run with cost of storage at $20/kWth and $60/kWth (the original calculation used $40), for both the 2007 and 2004 NEM data sets.

Figure 48: Real LCOE for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and no government contribution. Left: Storage = $20/kWhth; Right: Storage = $60/kWhth

As to be expected the LCOE is less when storage is cheaper and more when storage is more expensive. Cheaper storage swings the minimum LCOE towards 6 hours of storage for higher solar multiples, but comparable LCOEs are produced by 2 and 4 hours of storage at Solar Multiples of 2 and 1.66.

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Figure 49: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2007 QLD NEM Dataset. Left: Storage = $60/kWhth; Right: Storage = $20/kWhth

Figure 50: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2004 QLD NEM Dataset. Left: Storage = $60/kWhth; Right: Storage = $20/kWhth

Also as expected, cheaper storage results in slightly higher IRRs (for configurations that include storage). The peak in the IRR for the 2007 NEM-price dataset swung further in favour of larger storage amounts for low storage costs; however the evening price peak meant that large amounts of storage were still warranted even when the storage cost increased by 50%. The plateau of comparable IRRs remained when the 2004 NEM price-dataset was used, though configurations with greater amounts of storage moved up or down relative to one another.

A 50% reduction in storage cost impacts IRR by less than 1%, but changes the relative amount of optimum storage for a particular solar multiple.

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8.1.4 Sensitivity Analysis: Cost of Collectors A 20% and 40% reduction in the cost of collectors was modelled.

Figure 51: Real LCOE for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and no government contribution. Left: Collector = $210/m2; Right: Collector = $280/m2

Figure 52: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2007 QLD NEM Dataset. Left: Collector = $210/m2; Right: Collector = $280/m2

Figure 53: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2004 QLD NEM Dataset. Left: Collector = $210/m2; Right: Collector = $280/m2

As expected, reducing the price of the collector results in a reduction in LCOE and an increase in IRR. The relative positions of LCOE and IRR for the selected amounts of storage remain unchanged. A 20% reduction in collector cost can reduce IRR by a bit less than 1%, but makes higher solar multiples slightly more favourable.

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8.1.5 Sensitivity Analysis: REC price A 30% decrease and 30% increase in REC price was modelled. The project 1st year revenues and expenses (using the 2007 QLD NEM dataset and the base-case REC price of $50) are presented below. They show that REC revenue makes up 35-40% of overall revenue at $50/REC.

Figure 54: Project 1st year Revenues and Expenses, 2007 QLD NEM dataset, $50/REC

A 30% change in REC price results in a 0.7% change in IRR. This is not a significant impact, because although RECs represent 40% of revenue, depreciation has a taxation impact that is three times greater than RECs.

Figure 55: Sensitivity to REC price of a 250 MW CSPS with SM=2.4, hTES=6, Longreach

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Figure 56: IRR for a 250 MW CSPS in Longreach with a 10 % discount rate, 1USD=0.8AUD, and 50% government contribution. 2007 QLD NEM Dataset. Right: REC price = $38; Right: REC price = $65

As a point of interest, the REC price is presently $38 (as of 7/10/09), not $50 as assumed in the analysis.

8.1.6 Sensitivity Analysis: Electricity Price Increase The base case assumes that electricity rises at the same rate as inflation. However, a number of factors (one of which is the Emissions Trading Scheme) suggest that electricity is likely to increase in price faster than inflation. Figure 57 shows that a 1% compounding increase in electricity prices can increase the IRR by 1%.

Figure 57: Sensitivity to Electricity Price Increase for a 250 MW CSPS with SM=2.4, hTES=6, Longreach and 50% govt subsidy

8.1.7 Sensitivity to Location As shown in Figure 58, 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, Figure 58 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.

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Figure 58: Annual Direct Normal Incidence?? Solar Radiation (source: EnergyPlus)

Figure 59: Average NEM Price 2004-07

Figure 60 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. Figure 61 repeats these values for a 2004 NEM dataset. 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.

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Figure 60: LCOE and IRR for best locations in each state, 250MW CSPS, $50 REC, SM=2.4, 6hTES, 10% discount rate. 2007 NEM data

Figure 61: 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, whilst Longreach would obtain the best IRR if the prices were equal to those of 2007, Moree would eclipse Longreach if the prices were equal to those of 2004. Interestingly, it might be expected that Woomera - which receives only 6% less DNI than Longreach but had a 22% higher average price in 2004 – would have a better IRR than Longreach for the 2004 price dataset. In contrast, Longreach’s

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IRR is 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.

However, this may also have occurred because Woomera’s peak power prices were not able to be captured by the CSP, at least in its SM=2.4 hTES=6 configuration – a typical configuration used around the world and the optimum configuration for Rockhampton, but perhaps not the best for Woomera. Contrasting the late afternoon 2004 NEM price peaks in Queensland (Figure 38) and NSW (Figure 40) with the early afternoon peak in South Australia (Figure 37), Woomera’s IRR may be improved by incorporating less storage. Furthermore, had Woomera dispatched its power earlier, it might have significantly increased its revenue, as will be shown in Section 8.1.9.

8.1.8 Variation with Solar Radiation Data In Section 6.2 it was demonstrated that there was little correlation between solar radiation and NEM price. Thus, TMY solar data was used for the analysis, principally because it allowed a greater number of sunny locations to be investigated. This Section investigates the impact of the decoupling of solar radiation from NEM price.

The upper half of Figure 62 shows the average monthly direct solar radiation for 2004, 2005, and TMY, along with the average NEM price for that month. The lower half shows the average product of solar radiation with NEM price. Comparing the yellow line (2004 NEM price x TMY radiation) with the brown line (2004 NEM price x Measured 2004 radiation at Wagga Wagga NSW), it is clear that in last quarter of the year, use of TMY data typically produces significantly greater monthly revenue. In contrast, comparing the pink line (2005 NEM price x TMY radiation) with the grey line (2005 NEM price x Measured 2005 radiation at Wagga Wagga NSW), a more balanced outcome is observed – with use of measured data producing an slightly better revenue overall.

Figure 62: Solar Radiation, NEM price, and their product for 2004 and 2005

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Figure 63 depicts why this discrepancy exists. On January 15, 2005 the NEM price spiked. A simulation based on TMY data profited more from this occurrence, whereas clouds in Wagga on that day would have denied a real CSP much revenue on that day.

Figure 63: Solar Radiation, NEM price, and their product for January 14, 2004 and 2005

Figure 64 depicts a contrasting 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).

Figure 64: Solar Radiation, NEM price, and their product for February 14, 2004 and 2005

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Overall, 2004 measured values at Wagga Wagga were 2.6% higher than TMY 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 Figure 65. 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.

Figure 65: Influence of Peak Power Price Capitalisation on Revenue

All in all, these occurrences balance out, principally due to the low correlation between NEM price and solar radiation at the investigated sites. The correlation between radiation and power prices may be higher in cities due to air conditioning demand, however a low correlation at the investigated sites might be explainable by the distances between Australia’s coastal population centres and the inland locations with high DNI. The lack of sunny NEM-connected sites with measured data prevented further investigation into whether other sunny sites may have had a stronger correlation with NEM price, or whether NEM price frequently spiked on cloudy days.

Whilst this analysis does not examine the benefits that storage can bring by delaying solar output, it does show that variablility between the NEM price, the solar radiation resource, and their product can have significant impact upon revenue. Storage would not have changed the situation depicted in Figure 65: there was little radiation in Wagga on that day to store when the power price exceeded $9000/MWh (see Figure 66). A 24% lower revenue that could have resulted from poor alignment between true solar radiation and power price profile would have massive negative impacts upon IRR; impacts that may have partially been overcome through use of storage. 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.

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Figure 66: December 2, 2004 Peak Power Price

8.1.9 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. Although the NEM price has a daily average profile, the NEM price profile varies considerably from month to month and from year to year, as shown in Figure 67. In order to maximise profits, a CSPS could base its energy dispatch methodology upon the forecast power price and the forecast weather. For instance, a price spike in the middle of the day may mean that storage is bypassed altogether. If the storage is nearing its capacity but the afternoon will be cloudy, the stored energy may be utilised later rather than if the afternoon was forecast to be sunny. Such a dispatch methodology may offer significantly increased revenues when compared to operating the power plant in a fixed manner.

Figure 67: Annual Average of QLD NEM Price Daily Profile

SAM schedules the dispatch of power based upon the time of day, month of year, and type of day, and the minimum proportion of stored energy required to start the turbine, as depicted in Figure 68. Whilst this allows some forward scheduling that can be set according to the average power price profile, it stops short of full price-responsive scheduling.

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Figure 68: SAM Screenshot: Energy Dispatch Schedule

The benefit of storage is shown in Figure 70 and Figure 71, 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 powerblock (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.

Figure 70 shows that the initial storage of the morning’s energy benefits the day’s revenue by being able to deliver energy from storage later in the overcast afternoon when the NEM price is higher. However, had the energy been stored for delivery even later when the NEM price was higher still, even more revenue could have been generated.

Figure 71 shows another day with a limited amount of sunshine. However, on this day the power price was higher in the early morning than through 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.

The NEM price is forecast for the forthcoming day with a reasonable degree of accuracy, as shown by Figure 54. Solar radiation can also be forecast. While inaccuracies inherent in these forecasts, when combined with other financially limiting factors such as turbine start-up and shut-down, may prevent a CSPS from obtaining the maximum possible revenue, it is clear that greater revenue can be obtained by a price-adaptive energy dispatch scheduling than SAM’s deterministic algorithm.

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Figure 69: Actual vs Predicted NEM Price, NSW Jan 2009

Figure 70: Energy Flows and Revenue - Jan 26

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Figure 71: Energy Flows and Revenue – Aug 16

The method used in the preliminary investigation of Section 7 has been expanded upon in order to investigate the amount of additional revenue that may be gained by a more price-responsive scheduling algorithm. In this method, the output power from the investigated CSP has been dot multiplied with the NEM price delayed by a nominated amount, for the previously investigated scenarios without storage for a solar multiples of 2. The difference between this analysis and that of Section 7 is that Section 7 used solar radiation data dot multiplied by NEM price, whereas this section uses modelled CSPS output.

This method is extended in this Section to assess the maximum amount of revenue it was possible to create each day by delaying the output a fixed number of hours for that day. Figure 72 shows the revenue streams from a Rockhampton CSP without storage for the first six days of 2007, were the output power to be delayed by 0 to 6 hours. It demonstrates that on day six, a delay of 2 hours would result in maximum revenue, whereas on day four a delay of 6 hours would create maximum revenue.

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Figure 72: Revenue Curves for Delaying Each Day's output by a fixed number of hours for the first 6 days in January (one day per line), 2007 QLD Longreach 250MW 2.4x 6hTES. Each line represents the day of the year.

When taken over the course of the year, the revenue generated by this fixed delay is presented in the bars of Figure 73. However, a greater amount of revenue is possible by fixing each day’s delayed output by the optimal number of hours for maximising revenue on that day. The right-most bar presents the sum of each of the ‘optimal’ delays, while the line graph presents the number of days for which each of 0,1,2…,6 hours produced the maximum revenue for that day. It can be seen that the maximum revenue is at least 10% higher than the best of the fixed delay dispatch amounts, and up to 44% higher than if there was no delay. It is also apparent that zero and three hour delays between output of power and NEM price produced maximum revenue for that day almost as frequently as 6 hours of storage.

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Figure 73: Revenue Maximising through Strategic Dispatch of Stored Solar Energy – 2007 QLD Longreach

This metric is a gross simplification – on the one hand it does not take into account energy losses in storage, and it assumes price fore-knowledge (which the NEM price forecast provides, as shown previously in Figure 69). Like the previous analysis of Section 7, it assumes no losses in storage and does not consider the cost of storage and consequent impact upon LCOE and IRR. Some errors are also introduced in the metric by the shift in delay that occurs at midnight each night, double counting some NEM prices and skipping others. On the other hand, a dispatch delay that is fixed for a single day is not reflective of real-time optimisation that might result in even greater maximum revenue. Unfortunately, these limitations are not able to be resolved within the current SAM release. However, in spite of its limitations, this metric demonstrates that about 10% greater revenue can be earned by price-dependent dispatch of energy. If revenue was 10% greater than SAM’s non-adaptive calculations due to electricity price-determined energy dispatch, the IRR would increase by 0.8%, as shown in Figure 74.

Figure 74: Potential Impact upon IRR of Price-Determined Energy Dispatch

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As has been alluded to previously, the characteristics of the NEM price profile impacts upon the amount of delay of energy output (approximating energy storage hours) that maximises revenue. Figure 75 is a repeat of Figure 72 but for 2004 instead of 2007. Similarly, Figure 76 is a 2004 equivalent of Figure 73. These graphs show that in 2004, lower amounts of storage delivered good results, though the ability to make use of 6 hours of storage was frequently beneficial.

Figure 75: Revenue Curves for Delaying Each Day's output by a fixed number of hours, 2004 QLD Longreach 250MW 2.4x 6hTES. Each line represents the day of the year.

Figure 76: Revenue Maximising through Strategic Dispatch of Stored Solar Energy – 2004 QLD Longreach

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8.1.10 Sensitivity to Government Contribution As mentioned in Section 5, the research design assumed a government contribution of 50% in order to produce positive IRRs for the majority of investigated configurations. However, the initial Solar Flagships program announcement suggested a government contribution of only 33%. As shown in Figure 77, this has significant implications for the best case IRR, reducing it by 3% to 4.73%. If the government were not to fund the project at all, the best case IRR found in this study would be only 1.28%.

Figure 77: Sensitivity of IRR to Government Contribution to Upfront Costs

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9 Interpretation of results

9.1 Comparison of Results to Literature

9.1.1 Project Cost Literature suggests56

Table 5

that the price of CSPS projects was US$2,400-$3,000/kW. This contrasts strongly with the costs that result from SAM’s default costing assumptions which had a cost range between US$3,500 and $9,900/kW as shown in . To obtain this lower price range would require SAM to produce values that were approximately 60% less than the 250 MW CSPS studied. As SAM’s default costs are based upon quotations and a study by Worley Parsons57

Table 5: Price of Systems

, these are more likely to be valid.

250MW Configuration

Total Capital Cost

US$/kW US$/kW Post 50% govt Contribution

US$/kW Post 33% govt Contribution

1.0x0h $1,103,209,345 $3,530 $1,765 $2,354

1.6x0h $1,594,742,263 $5,103 $2,552 $3,402

1.6x2h $1,760,960,964 $5,635 $2,818 $3,757

1.6x4h $1,927,179,664 $6,167 $3,083 $4,111

2.0x0h $1,847,956,191 $5,913 $2,957 $3,942

2.0x2h $2,014,174,891 $6,445 $3,223 $4,297

2.0x4h $2,180,393,591 $6,977 $3,489 $4,652

2.0x6h $2,346,612,292 $7,509 $3,755 $5,006

2.4x2h $2,312,073,629 $7,399 $3,699 $4,932

2.4x4h $2,478,292,329 $7,931 $3,965 $5,287

2.4x6h $2,644,511,030 $8,462 $4,231 $5,642

3.0x2h $2,758,921,736 $8,829 $4,414 $5,886

3.0x4h $2,925,140,437 $9,360 $4,680 $6,240

3.0x6h $3,091,359,137 $9,892 $4,946 $6,595

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9.1.2 Project Cost Breakdown The cost breakdown of the 250 MW CSPS project with 6 hours of storage and a solar multiple of 2.4x which was the base case for this analysis is presented in Figure 78. This compares reasonably well with the breakdown suggested by literature (as presented earlier in Figure 10) as follows:

• the solar field is 8% less than in literature

• the heat transfer system accounts for a much higher proportion of cost than in literature (7% vs 2%)

• the energy storage cost is slightly higher (17% vs 15%)

• the power block cost is slightly higher (13% vs 10 %)

• the power plant and contingency is the same

Figure 78: Cost Breakdown 250MW 2.4xSM 6hTES

9.1.3 LCOE The Levelised Cost of Energy of A$0.20-$0.28 calculated within this document are significantly higher than those quoted by one USA source (US$0.10 - $0.126), and the Australian reported value of A$0.08-$0.10, even with Longreach’s high solar radiation values. The aforequoted US$2,400-$3,000 capital cost would create a minimum LCOE of about 8c/kWh in Longreach, but this seems an unlikely outcome that requires 60% lower cost than is calculated by SAM. This suggests that the LCOE calculated in this project might be higher than would likely be expected.

9.1.4 Configuration with Lowest LCOE Although our simulated CSPS configurations demonstrated that at a solar multiple of 1.6, 2 hours of storage produced the lowest LCOE, the more detailed exploration of the space shown in Figure 42 demonstrated a minimum LCOE with zero storage and a SM of 1.5. This seems to be an outcome determined by the relative cost of energy storage and solar array, as the difference between the LCOEs amongst a wide range of variables is not hugely significant. Minimum LCOE is quite sensitive to the cost of storage, with a 20% reduction in storage costs swinging the least LCOE in favour of more storage – as shown in Figure 79, similar to figure Figure 48’s sensitivity analysis. The value of incorporating storage on LCOE thus depends upon its costs and configuration, as attested to earlier by Figure 22 and Figure 23.

Site Cost3%

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Cost Breakdown - 250MW 2.4x 6hTES

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Figure 79: Minimum LCOE for 250 MW CSPS with solar field costs of $350/m2 and Storage costs of $32/kWhth

9.2 Discussion

9.2.1 Correspondence of lowest LCOE with highest IRR It was found that in the absence of a fixed price for generated electricity, the minimum LCOE does not necessarily correspond with the greatest IRR, the latter being significantly affected by the NEM price variations. The analysis using SAM showed that in years in which NEM price peaks were closer to solar radiation peaks, lower amounts of storage produced more favourable outcomes. However, the daily variation in the energy price profile presents a valuable opportunity to increase revenue streams by dispatching energy from the CSPS according to the forecast NEM price.

9.2.2 Benefits to IRR of incorporating storage The question, “Does incorporating storage benefit IRR significantly?” can be answered by comparing the IRR for zero-storage systems with that of configurations that included storage. In the original suite of configurations, a solar multiple of 1.6 frequently had an IRR that was within 1% of the greatest achievable for each selected variable. Such a small gain in IRR may not be worth the additional risks associated with storage. However, when combined with the ability to dispatch energy and achieve higher prices (as explained in Section 8.1.8), up to 2% improvement on IRR may be available. Because configurations without storage exhibited a strong peak in IRR & LCOE around a solar multiple of 1.6, other solar multiples were investigated in order to determine whether slight change in solar multiple would significantly change the IRR, and thus swing the favour back towards storage-less options.

Figure 80 shows that a smaller LCOE can be achieved with a solar multiple of 1.4 and 1.5 than the previously investigated solar multiple of 1.66. However, Figure 81 and Figure 82 show that the difference that this brings to IRR is insignificant. This implies that storage is only truly valuable if it is low-risk and if it can be used to obtain 10% greater revenues than an inflexible dispatch methodology.

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Figure 80: Longreach Storage-less Configurations LCOE

Figure 81: Longreach IRR for Storage-less Configurations, 2007 QLD NEM dataset

Figure 82: Longreach IRR for Storage-less Configurations, 2004 QLD NEM dataset

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9.2.3 Sensitivity to Component Cost of Configuration for Highest Financial Gain Naturally, as the largest cost component, a reduction in the cost of collectors can significantly reduce LCOE and increase IRR. It can also shift optimum configuration towards higher solar multiples, as seen in the results in Section 8.1.4.

Section 8.1.3 demonstrated that a reduction in the cost of storage can slightly reduce LCOE and increase IRR. Lower cost of storage also means that configurations with more storage have reasonably superior LCOE and IRR.

9.2.4 Sensitivity to REC Price Although RECs produce 40% of revenue (See Figure 54), the financial outcomes are not highly sensitive to REC price. In theory, as RECs are production-based incentives, higher REC prices should influence projects towards minimising LCOE rather than maximising the available NEM revenue. However, there is little observed difference in IRR between the scenarios, and little evidence of an influence towards configurations with minimum LCOE. This may be because the value to project cashflow of the RECs is proportionally quite small compared to other revenue. Some factors that impact upon IRR are not dependent upon output at all – e.g. depreciation – and therefore further subdue the impact of a varying REC price. This explains why the IRR is fairly insensitive to REC price.

Of course, if electricity price increases faster than inflation, then better outcomes are achieved for CSP. Countering this trend is the likelihood of a REC price drop as the power price increases – the theory being that as electricity price increases more wind farms become viable and thus produce more RECs. Regardless, significantly higher electricity prices are needed for Australian CSPSs to be viable under these input costing assumptions.

9.2.5 Sensitivity to Location Location is a significant input variable, and also one with considerable political and social interest. The choice of location has strong impact upon the solar radiation resource, and consequently the LCOE. However, a far larger impact upon the project financial return is seen by the differing electricity price in each state of Australia. This demonstrates that the best location is driven not only by solar resource but also by projected electricity forecasts. The proximity to a network with an ability to receive such significant quantities of energy is also likely to have a strong impact upon project financials and therefore limit suitable locations. The cost of network extension would be very high, even network connection is likely to be costly, and therefore have a significant impact upon the outcome.

9.2.6 Other configurations The IRR of the project might be improved if it was operated as a hybrid solar-natural gas power station, because the power block represents 10% of the project costs but is not operated at full capacity the entire day – with six hours of storage, there are at least seven idle hours in summer and nine in winter. An investment that sits idle is underutilised, so the IRR might be improved if natural gas was used to create power at night (depending upon the natural gas price and the NEM price overnight). The incorporation of natural gas would likely lead to no requirement for storage. However, a hybrid plant would require coincidence of a natural gas resource with the solar radiation and electricity transmission infrastructure, thereby further constraining suitable locations. Other technologies such as PV and tower CSP have their own merits, some being more suited to particular locations than others.

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9.3 Significance of results The results clearly show that a 33% government contribution is insufficient to achieve return on investment large enough to attract investment in a project that carries such risk. The government should look to increase its contribution to such project if it wishes to achieve its aims.

The results also show that regardless of government contribution, a significant reduction in upfront cost is necessary in order to secure good IRRs. While the LCOE is projected to fall by 40% over the coming years, this may not result in good enough outcomes to warrant a NEM-connected CSPS (assuming SAM’s prices are correct).

The dissertation shows that in the absence of a feed-in tariff, the risk associated with variable power price also weighs strongly against a NEM-connected project. This may suggest that off-grid projects are more likely to proceed in the near future. As the IRR hurdle rate varies with the investment’s perceived risk, the introduction of a feed-in tariff may result in more frequent development of Australian CSPSs.

If high input prices and government support were addressed, then this dissertation demonstrates that a NEM-connected CSPS with variable pricing will not benefit greatly by incorporating storage, unless the peak energy price consistently occurs in the late afternoon. The benefits can be maximised by dispatching according to NEM price predictions, but the 10% increase in revenue may only result in a 0.8% increase in IRR. This may not be sufficient incentive to include energy storage, with its associated risks.

9.4 Limitations of results The major limiting factor of the results appears to be the input costs. Other inputs, such as REC prices and energy dispatch scheduling, have lesser impact upon project financial outcomes.

The claim that greater revenue might be achieved by using a price-determined energy dispatch methodology is based upon a simplistic analysis of the energy prices and CSPS power output. However, while the quantum of increase in revenue might be debatable, closer investigation of some sample days’ power output and energy price clearly demonstrated that an increase in revenue above those predicted by SAM was achievable. The impact of this increase is noteworthy, though REC price is of equal importance. Beyond the upfront costs, the project financial outcome is more highly impacted by the electricity price itself and its variation by year, hour, and state.

The financial model itself is somewhat simplified, though it is strongly based upon the SAM financial model, adapted for Australian taxation conditions. The use of a ‘cash’ financing option does not reflect the reality that debt and equity will be needed to develop a CSPS. The introduction of debt and equity into the mix increases the complexity of analysis required to demonstrate a winning outcome.

The analysis also assumed 100% availability and 0% degradation. In reality, whilst some maintenance can be performed overnight and on cloudy days, periodic power station shutdowns are inevitable. Add to this the significant task of cleaning the collectors, and the availability is certain to be less than 100%. The effect upon the IRR would depend upon when the maintenance occurred, though as a 10% increase in revenue results in a 0.8% greater IRR, then a reduction in availability might have similar impact.

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Although the assumptions mentioned in the previous paragraph may lead to overestimated revenue, other assumptions do not reflect recent advances in technology that should deliver higher performance than estimated. The use of a thermocline energy storage vessel with filler should reduce storage costs, and the use of higher HTF temperatures should increase turbine performance and reduce input costs. These factors suggest that the assumptions made in this analysis are a balance between optimistic and conservative in nature.

9.5 Aims This dissertation has demonstrated that CSPS financial return on investment is not greatly benefitted by accessing NEM prices that vary in real-time. In fact, the LCOE from a CSPS has been shown to be a value that is not significantly less than that from a PV array. Given that small PV arrays can access Feed-in Tariffs, such systems appear to be more lucrative investments that CSP.

This suggests that NEM price spikes occur so infrequently that they represent only a small portion of total revenue. Indeed, the risk associated with NEM price variations may increase the IRR hurdle rate for interested investors, thereby denying CSP the opportunity to be developed. For these reasons, the benefits of accessing the instantaneous higher prices that are available on the NEM seem outweighed by the frequent low price, and a feed-in tariff seems the logical solution to lower the investment hurdle rate. For similar reasons, existing feed-in tariffs may also produce better outcomes for small-scale PV than are achievable by the CSIRO’s creation of NEM-playing virtual power stations.

At the commencement of this research, it was desired to investigate whether the solar radiation profile closely matched the power price profile in any of the proposed locations for CSPS. However, the limited number of historically measured half-hourly data locations severely limited this analysis. Furthermore, as it became clear that the NEM price profile varied so much from year to year, investigation of a coincidentally optimal site seemed pointless, as the correlation may cease to exist the following year.

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10 Conclusion, This 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’s proposed 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.

The poor financial outcomes demonstrated may be due to the input costs used, which although based upon sound methodology, seem to be significantly higher than other claims made in literature. A reduction of 60% in input costs is required to bring these projects into line with estimations of upfront costs; the true cost may lie somewhere in between. A 40% reduction in LCOE is expected in future as the industry grows; however, Australia can ill-afford to delay action.

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, which averages to receive about 10c/kWh from the NEM and 5c/kWh from RECs. 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. Because the IRR is highly dependent upon the REC price, projects are exposed to significant risk.

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, though the IRR is typically only slightly less than configurations with storage. However, the benefits of a 1% increase in IRR may be outweighed by an increase in perceived risk that results in a higher investment hurdle rate. Even a slight reduction in storage costs (which might be achieved by using thermocline storage with inexpensive filler) may swing the most favourable outcome towards incorporating of storage. The ability to dispatch power based upon NEM price may also justify storage’s inclusion.

The optimum location for a NEM-connected CSPS is not necessarily the location with greatest direct-normal solar radiation. Location of high-capacity transmission lines constrains viable locations. The variation between states in NEM power price and daily profile can have dramatic impact upon achievable IRRs. Without certainty of the future NEM power price, economic considerations may prevail over energy efficiency sensibilities. However, with such risk associated with NEM prices, developments may simply not proceed.

10.1 Recommendations For these reasons, the government should consider the following means of supporting the development of CSPS in order to achieve their Solar Flagships policy objectives:

• Establishment of a Feed-in Tariff to remove price risk and ensure optimal energy-efficient outcome

• Relaxation of the requirement of storage, letting the market decide if it is justified

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• Increase in funding contribution on a per-project basis, whether upfront or through the Feed-in Tariff

• Increase in overall funding if four 250 MW CSPS are desired, or alternatively allowing smaller power stations that carry less risk

In the meanwhile, off-grid locations may present excellent opportunities for CSPS in the range of 1-100 MW. In such case, depending upon the penetration into the existing diesel mini-grid, at least a small amount of storage might be warranted for power balancing in the presence of clouds. The industry might also like to consider solar-gas hybrid systems to maximise the utilisation of the power block, which represents 10% of their upfront cost. However, the additional constraints imposed by the availability of natural gas may mean that solar collectors acting as pre-heaters to existing power stations may be a more viable option.

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 would greatly facilitate the deployment of CSP in Australia.

10.2 Opportunities for further study A more sophisticated energy dispatch methodology might more accurately demonstrate the case for storage. This would need to take into account energy transformations within the CSPS, and balance the need for minimising power block transients which might cause undue wear and tear.

A more thorough comparison between trough CSP, tower CSP, and tracking PV at various Australian locations may assist in identifying the scale and locations for which each is suited. For instance, trough CSP requires flat ground, whereas tower CSP is more flexible in site; CSP requires high DNI, whereas PV can make use of indirect solar radiation, and might thus be more suited to the coast where the population lies.

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11 References Added Values of Photovoltaic Power Systems. Report IEA - PVPS T1 - 09 : 2001

Analysis of PV System’s Values beyond Energy - by Country and Stakeholder. IEA PVPS Task 10, Activity 1.1 Report IEA-PVPS T10-02:2008 March 2008

Antoni Gil, Marc Medrano, Ingrid Martorell, Ana La´zaro, Pablo Dolado, Bele´n Zalba,

Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts. NREL. October 2003

Brosseau, D., Hlava, P., Kelly, M. Testing Thermocline Filler Materials and Molten Salt Heat Transfer Fluids for Thermal Energy Storage Systems Use in Parabolic Trough Solar Power Plants, Sandia National Laboratories, July 2004

Brosseau, D., Kolb, G., Bradshaw, B. 2007. Sandia Thermal Storage Activities. Trough Workshop, NREL Colorado. www.nrel.gov/csp/troughnet/.../brosseau_sandia_molten_salt_tes.pdf (accessed 20/9/09)

CSIRO’s ‘sustainable cities’ on show. CSIRO. 18/9/08. http://www.csiro.au/news/SustainableBuildingConf08.html (accessed 5/11/09)

Dennis Anderson, Matthew Leach. Harvesting and redistributing renewable energy: on the role of gas and electricity grids to overcome intermittency through the generation and storage of hydrogen. Energy Policy 32 (2004) 1603–1614

Eck, M., Hirsch, T. Direct Steam Generation in Parabolic Troughs – Simulation of Dynamic Behaviour.

Ecostar, European Concentrating Solar Thermal Road Mapping, Roadmap Document, DLR, November 2004

Energy markets – Renewable Power Stations. DEWHA. http://www.ga.gov.au/renewable/gmaps/proposed.html (accessed 5/11/09)

Energy Plus Weather Data. http://apps1.eere.energy.gov/buildings/energyplus/cfm/Weather_data.cfm (accessed 5/10/09)

Feed-in Tariffs in Australia. Wikipedia. http://en.wikipedia.org/wiki/Feed-in_tariffs_in_Australia (accessed 15/3/09)

Generator Registration Guide. NEMMCO. 2008. http://www.nemmco.com.au/registration/110-0725.pdf (accessed 5/11/09)

GreenPeace, ESTIA, SolarPACES. Concentrated Solar Thermal Power – Now!. September 2005. http://www.greenpeace.org/raw/content/international/press/reports/Concentrated-Solar-Thermal-Power.pdf (accessed 5/11/09)

Herrman, U., Nava, P. Thermal Storage Concept for a 50 MW Trough Power Plant in Spain. www.nrel.gov/csp/troughnet/pdfs/nava_andasol_storage_system.pdf (accessed 20/9/09)

Kearney, D., Kelly, B., Cable, R., Potrovitza, N., Herrmann, U., Nava, P., Mahoney, R., Pacheco, J., Blake, D., Price, H. Overview on Use of Molten Salt HTF in a Trough Solar Field, NREL Parabolic Trough Thermal Energy Storage Workshop, 2003. www.nrel.gov/docs/fy03osti/40028.pdf (accessed 20/9/09)

Kelly, B., Barth, D., Brosseau, D., Konig, S., Fabrizi, F. Nitrate and Nitrite/Nitrate Salt Heat Transport Fluids. Parabolic Trough Technology Workshop. March 2007

Kolb, G., Proposed bench-scale tests to investigate recovery from salt freeze-up events in trough fields, Sandia

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National Laboratories, February 2006. http://www.nrel.gov/csp/troughnet/pdfs/kolb_proposed_hce_freeze_test.pdf (accessed 5/10/09)

Luisa Cabeza. State of the art on high temperature thermal energy storage for power generation. Renewable and Sustainable Energy Reviews 14 (2010) 31–55

Luisa F. Cabeza, State of the art on high temperature thermal energy storage for power generation”. Renewable and Sustainable Energy Reviews 14 (2010) 31–55

Maryam Jahanshahi. Lidell Thermal Power Station – greening coal-fired power. EcoGeneration July/August 2008

Nava, P., Herrman, U. Trough Thermal Storage – Status Spring 2007. NREL/DLR Trough Workshop – Denver. March 2007.

Owens, B. The Value of Thermal Storage. Workshop on Thermal Storage for Trough Power Systems. February 2003

Pilkington Solar International GmbH. Survey of Thermal Storage for Parabolic Trough Power Plants, NREL, September 2000

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

Report to Congress on Assessment of Potential Impact for Concentrating Solar Power for Electricity Generation. US Department of Energy Solar Energy Technologies Program. February 2007

S. Jalal Kazempour, M. Parsa Moghaddam, M.R. Haghifam, G.R. Yousefi. Electric energy storage systems in a market-based economy: Comparison of emerging and traditional technologies. Renewable energy [0960-1481] Kazempour yr:2009 vol:34 iss:12 pg:2630 -2639

Solar Advisor Model – Draft CSP Reference Manual. December 2008. https://www.nrel.gov/analysis/sam/pdfs/sam_csp_reference_manual_2.5.pdf (accessed 9/10/09)

Solar Advisor Model (SAM). https://www.nrel.gov/analysis/sam/ (accessed 5/11/09)

Solar Advisor Model User Guide for SAM 2009.10.12.

Solar Energy Generating Systems. Wikipedia. http://en.wikipedia.org/wiki/SEGS (accessed 5/11/09)

State of the energy market 2008. Australian Energy Regulator

Stoddard, L., Abiecunas, J., O’Connell, R. Economic, Energy, and Environmental Benefits of Concentrating Solar Power in California. NREL. April 2006

Voss, A. LCA and external costs in comparative assessment of electricity chains. Externalities and Energy Policy: the LCA Approach Workshop Proceedings. France 2001

Watt, M., Partlin, S., Oliphant, M., Outhred, H., McGill, I., Spooner, T. The Value of PV in Summer Peaks. www.ergo.ee.unsw.edu.au/value%20of%20PV%20in%20summer%20peaks.pdf (accessed 20/9/09)

WorleyParsons' billion-dollar solar plan. Sydney Morning Herald. 12/08/2008. http://business.smh.com.au/business/worleyparsons-billiondollar-solar-plan-20080812-3u3u.html (accessed 5/11/09)

Zarza, E. Overview on Direct Steam Generation and Experience at Plataforma Solar de Almeria. www.nrel.gov/csp/troughnet/pdfs/2007/zarza_dsg_overview.pdf (accessed 20/9/09)

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12 Glossary Acronym Meaning CSP Concentrating Solar Power CSPS Concentrating Solar Power Station DSG Direct Steam Generation DNI Direct Normal Incident FiT Feed-in Tariff hTES Hours of Thermal Energy Storage HTF Heat Transfer Fluid IRR Internal Rate of Return LCOE Levelised Cost of Electricity NPV Net Present Value SM Solar Multiple, the ratio between the solar field output power and the turbine power TES Thermal Energy Storage

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13 Appendices

13.1 Solar Radiation Data http://gis.ncdc.noaa.gov/website/ims-cdo/ish/viewer.htm

http://eosweb.larc.nasa.gov/cgi-bin/sse/grid.cgi?email=wbj%40tpg.com.au&step=2&lat=-35&lon=139&num=320056&p=grid_id&p=swv_dwn&p=ret_tlt0&veg=17&hgt=+100&submit=Submit – monthly only

http://gis.ncdc.noaa.gov/website/ims-cdo/ish/viewer.htm

RMY: http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=5_southwest_pacific_wmo_region_5/country=AUS/cname=Australia

Realtime weather data: http://apps1.eere.energy.gov/buildings/energyplus/cfm/weatherdata/x-weather_request.cfm

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13.2 Appendix 2: SAM economics inputs The following is an extract of a paper58

“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

which details the input cost assumptions that are covered in SAM.

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.

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.

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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.“

1 Watt, M., Partlin, S., Oliphant, M., Outhred, H., McGill, I., Spooner, T. The Value of PV in Summer Peaks. www.ergo.ee.unsw.edu.au/value%20of%20PV%20in%20summer%20peaks.pdf (accessed 20/9/09) 2 Added Values of Photovoltaic Power Systems. Report IEA - PVPS T1 - 09 : 2001 3 Analysis of PV System’s Values beyond Energy - by Country and Stakeholder. IEA PVPS Task 10, Activity 1.1 Report IEA-PVPS T10-02:2008 March 2008 4 Feed-in Tariffs in Australia. Wikipedia. http://en.wikipedia.org/wiki/Feed-in_tariffs_in_Australia (accessed 15/3/09) 5 Generator Registration Guide. NEMMCO. 2008. http://www.nemmco.com.au/registration/110-0725.pdf (accessed 5/11/09) 6 CSIRO’s ‘sustainable cities’ on show. CSIRO. 18/9/08. http://www.csiro.au/news/SustainableBuildingConf08.html (accessed 5/11/09) 7 WorleyParsons' billion-dollar solar plan. Sydney Morning Herald. 12/08/2008.

http://business.smh.com.au/business/worleyparsons-billiondollar-solar-plan-20080812-3u3u.html (accessed 5/11/09)

8 GreenPeace, ESTIA, SolarPACES. Concentrated Solar Thermal Power – Now!. September 2005. http://www.greenpeace.org/raw/content/international/press/reports/Concentrated-Solar-Thermal-Power.pdf (accessed 5/11/09) 9 Antoni Gil, Marc Medrano, Ingrid Martorell, Ana La´zaro, Pablo Dolado, Bele´n Zalba, Luisa F. Cabeza, State of the art on high temperature thermal energy storage for power generation”. Renewable and Sustainable Energy Reviews 14 (2010) 31–55 10 Kearney, D., Kelly, B., Cable, R., Potrovitza, N., Herrmann, U., Nava, P., Mahoney, R., Pacheco, J., Blake, D., Price, H. Overview on Use of Molten Salt HTF in a Trough Solar Field, NREL Parabolic Trough Thermal Energy Storage Workshop, 2003. www.nrel.gov/docs/fy03osti/40028.pdf (accessed 20/9/09) 11 Kearney, D., Kelly, B., Cable, R., Potrovitza, N., Herrmann, U., Nava, P., Mahoney, R., Pacheco, J., Blake, D., Price, H. Overview on Use of Molten Salt HTF in a Trough Solar Field, NREL Parabolic Trough Thermal Energy Storage Workshop, 2003. www.nrel.gov/docs/fy03osti/40028.pdf (accessed 20/9/09) 12 Zarza, E. Overview on Direct Steam Generation and Experience at Plataforma Solar de Almeria. www.nrel.gov/csp/troughnet/pdfs/2007/zarza_dsg_overview.pdf (accessed 20/9/09) 13 Eck, M., Hirsch, T. Direct Steam Generation in Parabolic Troughs – Simulation of Dynamic Behaviour. 14 Zarza, E. Overview on Direct Steam Generation and Experience at Plataforma Solar de Almeria. www.nrel.gov/csp/troughnet/pdfs/2007/zarza_dsg_overview.pdf (accessed 20/9/09) 15 Ecostar, European Concentrating Solar Thermal Road Mapping, Roadmap Document, DLR, November 2004

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16 Kolb, G., Proposed bench-scale tests to investigate recovery from salt freeze-up events in trough fields, Sandia National Laboratories, February 2006. http://www.nrel.gov/csp/troughnet/pdfs/kolb_proposed_hce_freeze_test.pdf (accessed 5/10/09) 17 Kelly, B., Barth, D., Brosseau, D., Konig, S., Fabrizi, F. Nitrate and Nitrite/Nitrate Salt Heat Transport Fluids. Parabolic Trough Technology Workshop. March 2007 18 Nava, P., Herrman, U., Trough Thermal Storage – Status Spring 2007. NREL/DLR Trough Workshop – Denver. March 2007. 19 Pilkington Solar International GmbH. Survey of Thermal Storage for Parabolic Trough Power Plants, NREL, September 2000 20 Brosseau, D., Hlava, P., Kelly, M. Testing Thermocline Filler Materials and Molten Salt Heat Transfer Fluids for Thermal Energy Storage Systems Use in Parabolic Trough Solar Power Plants, Sandia National Laboratories, July 2004 21 Brosseau, D., Hlava, P., Kelly, M. Testing Thermocline Filler Materials and Molten Salt Heat Transfer Fluids for Thermal Energy Storage Systems Use in Parabolic Trough Solar Power Plants, Sandia National Laboratories, July 2004 22 Antoni Gil, Marc Medrano, Ingrid Martorell, Ana La´zaro, Pablo Dolado, Bele´n Zalba, Luisa Cabeza. State of the art on high temperature thermal energy storage for power generation. Renewable and Sustainable Energy Reviews 14 (2010) 31–55 23 Solar Advisor Model – Draft CSP Reference Manual. December 2008. https://www.nrel.gov/analysis/sam/pdfs/sam_csp_reference_manual_2.5.pdf (accessed 9/10/09) 24 Solar Energy Generating Systems. Wikipedia. http://en.wikipedia.org/wiki/SEGS (accessed 5/11/09) 25 Maryam Jahanshahi. Lidell Thermal Power Station – greening coal-fired power. EcoGeneration July/August 2008 26 Energy markets – Renewable Power Stations. DEWHA. http://www.ga.gov.au/renewable/gmaps/proposed.html (accessed 5/11/09) 27 WorleyParsons' billion-dollar solar plan. Sydney Morning Herald. 12/08/2008.

http://business.smh.com.au/business/worleyparsons-billiondollar-solar-plan-20080812-3u3u.html (accessed 5/11/09)

28 Energy markets – Renewable Power Stations. DEWHA. http://www.ga.gov.au/renewable/gmaps/proposed.html (accessed 5/11/09) 29 Dennis Anderson, Matthew Leach. Harvesting and redistributing renewable energy: on the role of gas and

electricity grids to overcome intermittency through the generation and storage of hydrogen. Energy Policy 32 (2004) 1603–1614 30 S. Jalal Kazempour, M. Parsa Moghaddam, M.R. Haghifam, G.R. Yousefi. Electric energy storage systems in a market-based economy: Comparison of emerging and traditional technologies. Renewable energy [0960-1481] Kazempour yr:2009 vol:34 iss:12 pg:2630 -2639 31 Report to Congress on Assessment of Potential Impact for Concentrating Solar Power for Electricity Generation. US Department of Energy Solar Energy Technologies Program. February 2007 32 Stoddard, L., Abiecunas, J., O’Connell, R. Economic, Energy, and Environmental Benefits of Concentrating Solar Power in California. NREL. April 2006 33 Ecostar. European Concentrating Solar Thermal Road Mapping, Roadmap Document. DLR. November 2004 34 Energy Plus Weather Data. http://apps1.eere.energy.gov/buildings/energyplus/cfm/Weather_data.cfm (accessed 5/10/09) 35 Stoddard, L., Abiecunas, J., O’Connell, R. Economic, Energy, and Environmental Benefits of Concentrating Solar Power in California. NREL. April 2006 36 State of the energy market 2008. Australian Energy Regulator 37 Voss, A. LCA and external costs in comparative assessment of electricity chains. Externalities and Energy Policy: the LCA Approach Workshop Proceedings. France 2001 38 Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts. NREL. October 2003 39 Report to Congress on Assessment of Potential Impact for Concentrating Solar Power for Electricity Generation. US Department of Energy Solar Energy Technologies Program. February 2007 40 Ecostar, European Concentrating Solar Thermal Road Mapping, Roadmap Document. DLR. November 2004

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41 Kearney, D., Kelly, B., Cable, R., Potrovitza, N., Herrmann, U., Nava, P., Mahoney, R., Pacheco, J., Blake, D., Price, H. Overview on Use of Molten Salt HTF in a Trough Solar Field, NREL Parabolic Trough Thermal Energy Storage Workshop, 2003. www.nrel.gov/docs/fy03osti/40028.pdf (accessed 20/9/09) 42 Owens, B. The Value of Thermal Storage. Workshop on Thermal Storage for Trough Power Systems. February 2003 43 Owens, B. The Value of Thermal Storage. Workshop on Thermal Storage for Trough Power Systems. February 2003 44 Nava, P., Herrman, U. Trough Thermal Storage – Status Spring 2007. NREL/DLR Trough Workshop – Denver. March 2007. 45 Herrman, U., Nava, P. Thermal Storage Concept for a 50 MW Trough Power Plant in Spain. www.nrel.gov/csp/troughnet/pdfs/nava_andasol_storage_system.pdf (accessed 20/9/09) 46 Herrman, U., Nava, P. Thermal Storage Concept for a 50 MW Trough Power Plant in Spain. www.nrel.gov/csp/troughnet/pdfs/nava_andasol_storage_system.pdf (accessed 20/9/09) 47 Nava, P., Herrman, U. Trough Thermal Storage – Status Spring 2007. NREL/DLR Trough Workshop – Denver. March 2007. 48 Brosseau, D., Kolb, G., Bradshaw, B. 2007. Sandia Thermal Storage Activities. Trough Workshop, NREL Colorado. www.nrel.gov/csp/troughnet/.../brosseau_sandia_molten_salt_tes.pdf (accessed 20/9/09) 49 Brosseau, D., Kolb, G., Bradshaw, B. 2007. Sandia Thermal Storage Activities. Trough Workshop, NREL Colorado. www.nrel.gov/csp/troughnet/.../brosseau_sandia_molten_salt_tes.pdf (accessed 20/9/09) 50 Ecostar. European Concentrating Solar Thermal Road Mapping, Roadmap Document. DLR. November 2004 51 State of the energy market 2008. Australian Energy Regulator 52 State of the energy market 2008. Australian Energy Regulator 53 Solar Advisor Model (SAM). https://www.nrel.gov/analysis/sam/ (accessed 5/11/09) 54 Solar Advisor Model User Guide for SAM 2009.10.12. 55 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 56 Report to Congress on Assessment of Potential Impact for Concentrating Solar Power for Electricity Generation. US Department of Energy Solar Energy Technologies Program. February 2007 57 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 58 Price, H. A Parabolic Trough Solar Power Plant Simulation Model. ISES 2003. March 2003