MP Title: The Growth of Solar Concentrator Photovoltaic Markets in the Southwest US Author: Sean Connor Advisor: Lincoln Pratson Date: April, 2008 Abstract: Worldwide solar photovoltaic (PV) markets have grown at an average rate at 38% over the past ten years. While polysilicon flat panel PV modules have traditionally dominated the overall solar market, a range of different solar energy conversion technologies are starting to gain market share. One such class of solar technologies, concentrator photovoltaics (CPV), is in its commercial infancy but offers a module manufacturing paradigm to greatly lower the cost of solar electricity production. This paper examines the attributes of CPV and analyzes how it might compete within the overall solar market. The Southwest US is used as a case study to examine specific subsidies, regulations, and business models that will affect the success of CPV. In addition, a financial model was created to examine important factors influencing retail and wholesale PV and CPV project costs under various scenarios.

I. INTRODUCTION The global solar electricity market has experienced a second wave of 38 % average annual growth over the past 10 years (Maycock, 2007)spurred on by the lure of a self supporting industry providing clean electricity to the masses. For entrepreneurs, the race is to build cheaper solar systems and take advantage of the current subsidy-dependent demand growth while keeping their sights on making unsubsidized solar competitive with conventional fossil fuel electricity generation. This point of parity is expected to enable solar to take a significant percentage of the enormous market for new electricity generation. With the prize of enormous growth at stake, a multitude of companies have entered the market offering technological innovations and reformulated designs. One such technology is a concentrator photovoltaic (CPV) system whose inchoate development began in 1976 at Sandia National Laboratory. (Antonio Luque, 2007) The value of this design approach to photovoltaics (PV) has been touted because of CPV’s capability to reduce the use of semiconductor material while simultaneously enabling a more cost effective use of high efficiency semiconductors. While the working knowledge of CPV has been around for over 30 years, significant solar market penetration has remained elusive (Antonio Luque, 2007). However, a recent confluence of factors is projected to enable CPV grow from its current world solar market share of less than 1% (Figure 6) to 100 GW of a 1 TW annual global solar market in 2040 (Sharp Solar Systems Group, December 7, 2006). This report explores solar electricity markets with a focus on factors influencing the penetration of high-concentration PV (HCPV). Currently, solar markets are very dynamic and are being driven by an influx of venture capital, new market entrants, unstable government subsidies, high electricity prices, and the prospect of extensive US greenhouse gas regulations. In fact, one prominent industry icon, Jigar Shah described major market conditions as changing monthly. (Shah, 2007) In order to navigate through these market conditions and remain relevant as they change, this report identifies the underlying factors that will continue to drive solar markets and HCPV competitiveness amidst market fluctuations. While current factors will be identified, a focus will be placed on how market changes will affect the strength of solar in general and more specifically HCPV. This report contributes to the current solar literature by assessing the competitiveness of HCPV to traditional PV in the face of general solar market dynamics which should be of particular interest to HCPV solar module manufacturers, systems integrators, and anyone curious about HCPV. While opportunities for HCPV are opening up globally, this report will focus in depth on the behavior of solar markets in the SW US given its abundant direct solar radiation (insolation), rapidly growing electricity demand, and states with contrasting solar policies. Given solar systems’ high capital cost, it is clear that government policies and incentives have been a major driver of solar market growth worldwide. The US is no exception. State renewable portfolio standards (RPS), federal tax credits, state tax credits, and various other subsidies are designed to increase the percentage of clean energy generation while nurturing solar manufacturing competiveness to the point that subsidies are no longer required for significant growth. While the focus of the report is on HCPV, other electricity generating technologies will also be examined. On a general level, there are many economic substitutes for CPV and each substitute may exhibit comparative advantages. However, many government incentives are designed to promote specific technologies renewable technologies. HCPV generally receives the same federal and state government incentives as PV technologies. These PV technologies possess many of the same operating characteristics as CPV. In 2006, mono-crystalline silicon (Si) and poly-crystalline Si captured 84.6% of the overall PV market (Figure 6). Given the market dominance of Si PV and the similar operating attributes as HCPV, crystalline-Si PV technologies will be assumed the closest economic

substitutes of HCPV in this study. These PV technologies include, fixed PV, 1-axis tracking PV and 2-axis tracking PV which are compared to 2-axis tracking HCPV. Given that the objectives of this report are to identify the current and future competitiveness of HCPV and the overall market conditions that will drive its growth, the following methodologies are utilized: The technological characteristics of CPV are outlined; current solar market conditions are identified along with the framework in which HCPV will compete; factors affecting the costs of retail and wholesale solar installations are examined; the importance of geography and insolation are examined. Finally, a case study was constructed of hypothetical installation in AZ to examine the impact of subsidies and a range of factors entering into the financial health of typical solar installations. To aid in this analysis, an Excel spreadsheet model was constructed to examine the factors impacting overall solar project costs. Installation cost and operational cost data used in this report’s cost analyses are based on Department of Energy (DOE) surveys. (Department of Energy, 2005) This cost data along with technological assumptions and financial assumption are inputs into the financial model that calculates the levelized cost (LCOE)1, net present value (NPV), and internal rate of return (IRR) of hypothetical solar projects involving the relevant technologies. Subsidies, taxes and projected electricity rates are included to produce results that might reflect real typical installation metrics. It should be noted that the resultant project financial metrics can vary significantly based on input assumptions and that the model is not meant depict specific installations. As a result, the model was designed with the capability to easily determine the sensitivity of the resultant financial metrics to a wide variation of input parameters. This sensitivity analysis is used to reveal the important factors affecting overall project costs. The structure of the report is organized in a manner following the methodology: Part 1 discusses CPV technology, its current status, and its strengths. Part 2 outlines the current status of solar markets in the US focusing in on markets in which CPV will likely compete. Future projections of these markets are also examined. Part 3 details this study’s methodology which includes a discussion of the major factors entering into solar projects and a description of the financial models constructed to perform sensitivity analyses on the project factors. Part 4 identifies the LCOE of PV and HCPV in various SW US locations with varying levels of insolation. Part 5 discusses the current LCOE of PV and HCPV, discount rates, and what that means for their competitiveness. Part 6 explores the role of subsidies in AZ on solar projects. Part 7 estimates the future competitiveness of PV and HCPV. Lastly, a number of factors in the solar value chain are connected to paint a picture of HCPV’s prospects.

1 Levelized is cost is computed in $/kWh (Walter Short, 1995):

Where LCOE: levelized cost of energy ($/kWh) r: discount rate i: year PVprojectcosts: present value of project costs

I. TECHNOLOGY BACKGROUND Concentrator Photovoltaic Technology The general principle that characterizes CPV systems is that they use lenses or mirrors to concentrate sunlight onto a small solar cell (Figure1). There are a myriad of designs employing different configurations of concentrators but the overall concept of reducing semiconductor remains similar - the area of the semiconductor material is roughly reduced by the level of the concentration. Thus, the cost of the cell surface is replaced by the cost of the cheaper optics and other auxiliary components. This reduced cell size makes the use of expensive high-efficiency single-junction and multi-junction solar cells more cost effective. For example, a high-efficiency (~35%) multi-junction cell currently costs roughly 100X more than a single-junction Si cell, however, a 1000X optical concentrator reduces the required cell area by roughly 1000X. (Kinsey, 2007) Assuming that designers can adequately remove the added heat and series resistance issues stemming from HCPV, a module with a higher efficiency and lower cost ($/W) than traditional PV is possible. The term solar module basically refers to the package of solar cells mounted on a substrate with conductors combined with other auxiliary component which all together output DC electricity. At an installation site, solar modules are combined with a mounting structure (trackers are optional for traditional PV but necessary for HCPV), wiring, and inverters which together comprise the solar system. Most companies are pursuing designs using 400X to 1000X high concentration PV (HCPV) systems that require the use of two-axis trackers to maintain to optical alignment with direct insolation as the relative position of the sun changes throughout the day. Figure 2 shows typical optical acceptance angles of HCPV. As the optical misalignment (pointing error) with the direct solar radiation errs larger than the acceptance angle, the power output from the module quickly drops to zero. The necessity of 2-axis trackers adds significantly to the complexity and cost of HCPV installations and the cost of maintenance. In addition, the use of trackers limits the types of surfaces on which HCPV can be economically mounted. On the other hand, a number of manufacturers are pursing low concentration PV (LCPV) using single-axis trackers or no trackers which could be suitable for rooftop markets. Table 1 in the appendix summarizes the design features of current CPV manufacturers with market-ready products and pre-market-ready products. The high concentration designs have an average cell efficiency of 30% with Si cell efficiencies ranging from 22% - 28% and III-V cell efficiencies ranging from 30% - 37%.

Figure 1. Block Diagram of Traditional PV vs. CPV.

Figure 2. . Power Output vs. Optical Misalignment of Typical HCPV. (Palmer, 2007)

Most of the current PV module market is dominated by the use of single-junction Si cells. One disadvantage of this single-junction approach is a theoretical maximum cell efficiency of 40%. Multi-junction cells have a theoretical maximum efficiency of 86%. The primary market for multi-junction

cells has historically been the space industry which has employed III-V semiconductor materials manufactured in low volumes. However, burgeoning CPV applications are quickly overtaking space-based applications as the primary user of III-V multi-junction cells.2 One limitation of using silicon cells is that concentration levels can hardly go above 300 suns, however, multi-junction cells can operate very efficiently at over 1000 suns. High concentrations are only possible if the cells are very small, on the order of 1mm2, so that extraction of the current becomes easy. Si solar cells of this size are not possible due Si semiconductors’ high diffusion length, which makes the cells very sensitive to perimeter recombination. (Antonio Luque, 2007) High cell efficiency in CPV is important for several reasons. The optical concentrator has an efficiency of less than one and the concentrator can only collect direct insolation. Hence, fore example, if the direct normal/global normal radiation ratio is 80% and the optical efficiency is 80%, this means that only 64% of available insolation is cast onto the CPV cell. Assuming that conventional PV modules are 15% efficient, a concentrator cell would need to be at least 23.4% efficient for the CPV module to output the same power as a conventional PV module on a W/m2 basis. (Antonio Luque, 2007) In addition, Figure 3 shows the affect of solar cell cost and efficiency on the overall system cost for a CPV system and a traditional fixed (non-tracking) PV system. The steep slope of the fixed PV cost curves within the current range of cell costs show that a reduction in cell price leads to a large reduction in overall system costs. A reduction in CPV cell price (in the current range of cell prices) produces less of a reduction in the overall CPV system costs. In this case, increased efficiency has a pronounced effect on reducing overall system costs.

Figure 3. Cell Cost and Efficiency vs. Overall System Costs.

2 Boeing Spectrolab indicates a III-V cell production capacity of 200 MW for terrestrial applications and 600 kW for

space applications. (Boeing) Emcore indicated a III-V cell production capacity of 50 MW for terrestrial applications and 125 kW for space applications. (Emcore, 2006)

Given the premise that concentration can potentially reduce the overall system cost, the question then

becomes, what is the optimal level of concentration? This question is partly answered by Figure 4 which

shows the affect of three input variables, concentration level, cell price, and cell efficiency, on overall

levelized costs. The cell prices range from 2 Euro/cm2 to 10 Euro/cm2 and are representative of low to

high prices that might actually occur in the market over the next few years. For each cell price and the

range of cell efficiencies, the affect of concentration on the levelized costs is consistent - a higher level

of concentration produces a lower levelized cost. In fact, as the cell price increases, the levelized cost

differential between low and high concentration systems becomes increasingly amplified. This raises an

important point - cell prices are subject to fluctuations as evidenced by recent Si shortages that drove up

module prices. (Abboud, 2007) Although, high-efficiency HCPV cells do not have to use Si, supply and

demand imbalances resulting from rapid market growth could undulate III-V cell prices. A high-

concentration design would temper its levelelized cost against fluctuating cell prices more effectively

than a low concentration design.

Figure 4. Levelized Cost vs. Efficiency, Cell Price, and Concentration Level. (Luque, 2004)

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II. SOLAR MARKET OVERVIEW

High concentration photovoltaics compete in a market framework that is supported by government incentives but may ultimately need to be self-supporting without government incentives. The market-framework for HCPV can be outlined by identifying its likely submarkets and its closest competitors within those submarkets. This market framework is shown in Figure 5 going from general markets at the top to specific submarkets towards the bottom of the figure. HCPV has a place supplying energy to grid-connected and off-grid electricity markets. However, the grid-connected market is the larger of two markets and is the focus of the report (Figure 7). In retail and wholesale markets, the levelized project cost of a solar installation competes against the price of electricity. However, the broader class of renewables may be able to monetize environmental attributes which can produce a source of income in addition to the generated electricity. Currently, in the SW US (CA, NV, NM, AZ, CO, UT), Utah is the only state lacking a renewable portfolio standard. All the other states require that utilities supply a certain percentage of their electricity sold from renewable generation sources. Given the range of renewable generation options, utilities will generally choose the lowest cost generation first. Biomass, geothermal, wind, then solar often comprise the cheapest to most expensive components in supply curves. (Black and Veatch, June, 2007) (E3, 2008) However, the size of the renewable resource, transmission costs, and site considerations also factor into generation deployment decisions. Retail Markets The retail market is composed of solar systems that are net-metered. Under net-metering a solar system resides on the utility customer’s side of an electricity meter and that customer’s electricity use is offset by the electricity produced the solar system. The solar installation is generally sized so that it outputs only a fraction of the customer’s electricity use. This is due to the fact that many state laws don’t require utilities to compensate customers for solar energy output that exceeds their energy demand. Space constraints are also a factor constraining installation size. The commercial retail market has thus far been the largest US market. Within this market, roughly 28% of installations (sized in Watts) in 2006 were ground mounted. This fraction (28%) is used to estimate the HCPV commercial market potential projections in Figure 6. The 70% of the commercial market that consists of rooftop installations is assumed to be uncompetitive for HCPV due to the complexity and added cost of 2-axis tracking. Problematic issues arise concerning fire codes, load stresses on rooftops, and building sway which can cause the HCPV to frequently lose alignment with the sun. (Grunow, 2007)

Figure 5. Market Framework of HCPV.3

Figure 6. 2006 US PV Shipments by Technology Type. ( Energy Information Administration, 2006)

3 Building integrated PV (BIPV) is excluded from ground-installed solar.

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Figure 7. US Annual PV Market Demand by Market Segment (MW). Based on (Solar Buzz, June 2007)

Wholesale Markets Solar systems in wholesale market are generally owned by independent power producers that sell the electricity to utilities through power purchase agreements (PPAs). In addition, utilities may own their own generation. Since wholesale rates are significantly lower than retail rates, they currently generally produce a lower rate of return for investors. (Broido, 2008) However, lacking the size and net-metering constraints of retail installations, wholesale installations have the advantage of economies of scale. This then raises the question of how the competitive framework will change for HCPV as desired installation sizes grow. Concentrating solar thermal-electric (CST) systems have recently begun to show their cost advantage in large-scale installations. The first US CST to be built in 16 years, Nevada Solar One (64 MW), began

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operation in 2007. (Forbes, 2007) Since then, a number of utilities have signed long-term contracts with CST developers for future installations that will compete for the title of the largest solar installation in the world. (California Energy Commission, 2008) Within CA, utilities have contracted 1629 MW of CST to meet state RPS requirements. These various installations are set to begin construction in 2008 - 2011 with the largest on the order of 500 - 800 MW. Financial details of CST contracts remain uncertain as utilities are not required to promulgate the information. Nevertheless, the US market has demonstrated its preference for CST in large-scale installations (> 50MW). CST has the technological advantage over PV and CPV in that it can effectively store heat for roughly 9 hours economically and efficiently. (Goswami, 2007) This firming-up of the capacity increases the value of the system. In addition, as demonstrated by Nevada Solar One, CST can supplement solar heating with natural gas to firm up the power. However, CST utilizes a power block (turbine, condenser, and heat exchanger) which constrains the increments at which installations can be scaled-up. For example, a 50 MW power block with only 10 MW of would result in 40 MW of unused capacity. Large spare capacity results in less then optimal economics. On the other hand, HCPV and PV installations could be scaled up in smaller increments than CST. The power block is the largest CST cost component on the 1 – 5 MW scale but is also scalable. Figure 8 shows that the levelized cost of CST is greatly reduced as the installation size scales up. Comparing CST levelized costs to HCPV and low-concentration PV shows that HCPV would find its comparative advantage at 100kW – 50 MW whereas the economic s of CST would allow it to dominate the market for installations greater that 50 MW. (Renzetti, 2007) US CST installations are being proposed on lands managed by Federal Bureau of Land Management These lands are essentially being given away at close to a price of zero (Grunow, 2007) but may be isolated from centers of demand or high voltage transmission. In addition, transmission requirements add greatly to project costs and do not qualify for federal solar tax credits. (Martin, May 26, 2006) PV and CPV have the advantage of being installed on smaller swaths of land closer to existing transmission in a more distributed manner.

Figure 8. CST Levelized Cost vs. Installation Size. (Renzetti, 2007)

III. METHODOLOGY This section describes the main cost components of fixed PV, 1-axis tracking PV, 2-axis tracking PV, and 2-axis tracking HCPV installations. These cost components along with other relevant input variables are entered into two different models constructed for this report to examine the competitiveness of HCPV relative to the other PV and to examine the general importance of the cost factors. The first model demonstrates the impact of insolation on relative project levelized cost. The second model incorporates a wide range of input parameters that are varied to determine their effect on current hypothetical project costs. The results of the model are not intended to depict the costs of actual specific installations due to the fact that actual installation costs widely vary. In addition, the NPV of a project is quite sensitive to cost assumptions. The model provides value in that it can estimate financial metrics of representative installations, it can demonstrate the dynamic dependency of output financial metrics on input assumptions, and it can demonstrate the relative competitiveness of the relevant solar technologies. Major Factors in the Net Present Value of a Typical HCPV or PV Project:

o Capital costs (installed costs) Module Balance of system Installation Other/indirect

o Operating and maintenance o Insolation o Financing o Electricity price

Retail (net-metered) Wholesale

o RECs, environmental attributes o Subsidies

Federal investment tax credit Accelerated depreciation State subsidies

IV. INSOLATION AND ITS EFFECT ON THE LEVELIZED COST OF ENERGY OF VARIOUS PV TECHNOLOGIES

This section demonstrates the relationship between a technology’s access to insolation and its overall project costs. A set of closed-form equations are constructed to demonstrate the relationship in a simplified manner. The resultant model then utilizes insolation levels (NREL) at various locations in the SW US and NJ to calculate the installed cost (varying by PV type and location) necessary to achieve a levelized cost equivalent to that of a reference 2-axis PV system. The total intensity of insolation and the proportion of direct/indirect insolation vary geographically around the world. Figure 9 through Figure 11 in the appendix demonstrate the level of insolation available for three representative types of PV technologies in the US. A 2-axis tracking flat plate (2-X FP) system has access to the most insolation due to the fact that it can receive direct and indirect insolation, and it optimally tracks the sun as its relative position moves through sky. When comparing the insolation available to 2-X CPV vs. a fixed flat plate (FFP) system, the figures demonstrate that areas of high direct insolation in the SW US provide more insolation for 2-X CPV than FFP. However, in the NW, FFP has access to more insolation than CPV.

Closed Form Model How does a particular technology’s access to insolation affect the overall economics of a solar project? Solar installation prices are commonly quoted on a basis of $/W installed. This refers to the bulk of the capital cost of a solar installation and generally includes the cost of the modules, balance of system (BOS) components, trackers (when applicable), and labor. Using this installed cost, the annual energy production, the lifetime of the system, and a discount rate, a formula can be constructed to examine the relationship between the LCOE, the installed cost, and the energy produced.

An ordinary annuity formula can be used to find the LCOE: C = installed capital cost, ($/W) Om = Operating and maintenance costs, ($/kWh) L = LCOE, ($/kWh) E = annual energy production ratio, (kWh/(W-year)), these values are from NREL measured

insolation data. I = discount rate N = lifetime of project, (years)

A = ordinary annuity factor

, where

Simplifying the equations:

The LCOE can be used as a guide to rate the economics of a solar project. Since, a 2-X FP system has access to the most insolation, its LCOE can be used as a reference to compare other PV technologies.

If we assume for the moment that all PV technologies have the same installation cost, C, and that all PV installations using trackers have operating and maintenance costs 4 times as high as fixed flat plate PV

(Swanson, 2000) then it turns out that 2-X FP will have the lowest LCOE due to it having the highest energy production (kWh/kW). Using , the installed cost of other technologies necessary to achieve a LCOE equivalent to can be found. The application of this formula reveals that that all other PV technologies will have to have a lower installation cost in order to achieve the same LCOE as 2-X FP.

Thus when the LCOE for all the PV technologies are equal,

Equation 1

This equation assumes financing costs and system derate factors are equal for every PV technology. As discussed later in the paper, financiers may actually require a higher discount rate on technologies with a lack of historical performance data. While taxes and other factors were neglected in this simplified LCOE model, it can still be used to illustrate the relative strengths of the various PV technologies. A 2-axis FPV LCOE of $0.2/kWh is assumed (based on an estimate of its current unsubsidized LCOE). In addition, the model assumes A FFP Om cost of $0.002/kWh and that the other tracking technologies have an Om cost of $0.008/kWh. (Swanson, 2000). Figure 12 shows the results of cost comparison.

Figure 9. Installation Cost Relative to 2-X FP Necessary to Achieve LCOE Equivalent to 2-X FP LCOE.

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Discussion The results in Figure 12 show that if all the PV and CPV technologies have an equivalent installed cost ($/W), then the 2-axis PV would produce the lowest LCOE. Comparing the systems from a different perspective shows that a 1-axis PV would need an installed cost ($/W) roughly 4% lower than the 2-axis PV installed cost in order to produce a LCOE equivalent to the 2-axis PV. In areas where the ratio of direct to diffuse insolation is low (e.g. Newark, NJ), the requisite capital cost reduction is lowered. The requisite capital cost reduction for a CPV system fluctuates between 20% and 38%. Lastly on the graph, the requisite capital cost reduction for fixed PV fluctuates between 18 % and 26%. It is likely that an installation surface amenable to a 2-axis PV will likely also be amenable to a 1-axis PV, 1-axis CPV, and fixed PV. However, fixed-PV has a wider range of amenable installation surfaces (e.g. rooftops) than the tracking systems. Therefore, the results of Figure 12 should be assumed to model ground mounted installations. It is clear that 2-axis CPV faces its stiffest competition from the other tracking PV technologies. So, how can the CPV capital cost be reduced below the other tracking PV? For one, the HCPV design allows economical use of high efficiency solar cells which can result in a more efficient module than traditional PV in terms of W/m2. This means that it is possible for HCPV to have a higher W/tracker than PV. Assuming for example that the HCPV module efficiency is 25% and the PV module efficiency is 15% this means it is possible that the HCPV will use 10% less trackers all else being equal. However, 2-axis PV and 2-axis HCPV will not necessarily utilize the same type of tracker. Given that HCPV will need to maintain a more accurate tracker than 2-axis PV will likely result in a higher individual tracker cost for HCPV. (Antonio Luque, 2007) This leaves the module as the other prime area for capital cost reduction. A module cost reduction of 40% below PV module cost will be a formidable challenge for HCPV manufacturers. It will be important for HCPV proponents to verify the accuracy of direct insolation data in order to accurately predict project economics. Figure 13 shows the sensitivity of generic PV/CPV LCOE to changes in received insolation. This shows that as received insolation becomes lower, the slope of LCOE/insolation becomes larger. Figure 14 shows received insolation levels for the SW US and NJ. The 2-axis CPV median received insolation is roughly 5.8. Figure 13 shows that in this range a +-1 unit change in insolation leads to a -+ $.05/kWh change in LCOE. Whereas, for tracking PV, a +-1 unit change in insolation leads to only a -+ $.02/kWh change in LCOE. Figure 13 raises the point that when choosing an installation location, regional subsidies and electricity prices can vary widely in political regions whereas insolation varies geographically. Both are important considerations in determining project economics.

Figure 10. LCOE vs. Received Insolation for a Generic PV/CPV System.

Figure 11. Recceived Insolation in SW US. (NREL)

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COLLECTORS FACING SOUTH AT A FIXED-TILT (kWh/m2/day), Percentage Uncertainty = 9 Lat

SOLAR RADIATION FOR 1-AXIS TRACKING FLAT-PLATE COLLECTORS WITH A NORTH-SOUTH AXIS (kWh/m2/day), Percentage Uncertainty = 9 Lat

SOLAR RADIATION FOR 2-AXIS TRACKING FLAT-PLATE COLLECTORS (kWh/m2/day), Percentage Uncertainty = 9 2-Axis

DIRECT BEAM CONCENTRATING COLLECTORS (kWh/m2/day), Percentage Uncertainty = 8 2-X

V. FINANCIAL MODEL A spreadsheet model was created to analyze Pro Formas of hypothetical PV installations with costs reflective of today’s technology. The primary financial output metrics include a solar project’s NPV , internal rate of return (IRR), and unsubsidized LCOE. The structure of the model is flexible enough that it could be used to calculate metrics for retail commercial installations and projects by wholesale power producers. In addition, the model can be customized to analyze financials of installations utilizing various business models. It is important to note that the structure of project ownership significantly affects project financials. This section of the report details the inputs and calculations in a generic form of the model. To produce a Pro Forma that includes state and federal subsidies, the model requires a table of state incentives, federal incentives, and a slight customization to incorporate the incentives. The main components of the model include:

A table summarizing federal and state incentives available for PV commercial and wholesale installations and a set of financial input variables

A set of financial input variables

A set of input variables surrounding the PV technologies and the installation

A pro forma detailing cash flows during the project lifetime, the NPV of the cash flows, IRR, and unsubsidized LCOE.

Financial Inputs The input variables are highlighted in yellow. The variables are self-explanatory unless otherwise noted: After tax WMCC (weighted average marginal cost of capital) (Nominal) = [Cost of Equity (Nominal)] * [Equity Fraction] + [Debt Rate

(Nominal)] * [Debt Fraction] Cost of Equity (Real) = [1 + Cost of Equity (Nominal)] / [1 + Inflation] – 1 Debt Rate (Real) = [1+ Debt Rate (Nominal)] / [1 + Inflation ] – 1

Financial Assumptions Note Value

After tax WAMCC (Nominal) % 6.00%

Cost of Equity (Nominal) % 6.0%

Cost of Equity (Real) % 3.41%

Equity Fraction % 100.0%

Debt Fraction % 0.0%

Debt Term years 15

Debt Rate (Nominal) % 6.0%

Debt Rate (Real) % 3.41%

Inflation % 2.5%

Target Debt Service Coverage Ratio 1.2

Cash Reserve Interest rate % 2%

Federal Tax Rate % 35%

State Tax % 7%

Effective Tax Rate % 40%

REC price $/kWh 0.015

Technology Inputs Cost Data is based on data from a 2005 DOE study (Department of Energy, 2005). Data for technologies not explicitly available in the report were derived by extrapolating from the given report data. The commercial data was updated by the DOE and subsequently used in this report. (Cameron, January 2008) Units Fixed PV

Commercial 1-Axis PV Commercial

2-Axis PV Commercial

2-Axis CPV Commercial

Fixed PV Utility

1-Axis Flate Plate Utility

2-Axis Flate Plate Utility

2-Axis CPV Utility

Module Price $/Wdc 4.84 4.84 4.84 5.00 4.56 4.56 4.56 5.00

Inverter Price $/Wac 0.71 0.71 0.71 0.71 0.67 0.67 0.67 0.67

Inverter Life Years 10.00 10.00 10 10.00 10.00 10.00 10.00 10.00

Other BOS4 $/Wdc 0.00 0.00 0 0.00 0.00 0.97 1.07 0.70

Installation5 $/Wdc 0.00 0.00 0 0.00 0.00 0.27 0.27 0.55

Other/Indirect $/Wdc 1.68 1.85 2.03 1.85 1.58 0.55 0.55 0.11

Installed System Price

$/KWdc 7230 7400 7585 7560 6817 7023 7120 7029

O&M Cost (not including inverter replacement)

fraction of installed price

0.0045 0.0045 0.0090 0.0090 0.0010 0.0015 0.0085 0.0085

The input variables are highlighted in yellow. The variables are self-explanatory unless otherwise noted: First Year of Operation = [Construction Start Year] + [Construction Lead Time] Balance of System Efficiency is the percent of the DC electricity leaving the module that gets turned put into the grid as AC electricity. Project Capacity (Building Increment) (kWhac) = [Project Capacity (Building Increment)] * [Balance of System Efficiency] Performance degradation is the percent decline in AC output of the system. 1st Year of Operation Electricity Output = [Project Capacity (Building Increment) (kWhac)] * [Baseline Capacity Factor] * [365 days] * [24 h/day] Project Capital Costs (pre-tax) = [Installed Capital Cost (pre-tax)] * [Project Capacity (Building Increment)] Fixed O&M Expense is the annual operating and maintenance expense.

4 A cost of zero indicates that this cost is incorporated into Other/Indirect costs.

5 A cost of zero indicates that this cost is incorporated into Other/Indirect costs.

CPV 2-Axis Utility Units

First Year of Operation 2008

Construction Lead Time 0

Construction Start Year 2008

Project Life Yrs 30

Project End 2037

Project Capacity (Building Increment) kWdc 33

Total System Derate Factor % 77%

Project Capacity (Building Increment) kWac 25.41

Baseline Capacity Factor % 28%

Performance Degredation %/yr 1.0%

1st Year of Operation Electricity Output kWh/yr (ac) 62895

Installed Capital Cost (pre-tax, including inverter) $/kW (dc) 7029

Project Capital Costs (pre-tax, including inverter & inverter replacements) $

238,953

Inverter Costs $/Wac

0.67

Annual O&M (not including inverter replacement)

annual % installed price 0.85%

Insurance % of Installed Cost 0.0%

Property Tax % book value 0%

AZ Excise Tax % 0%

Capital Goods Excise Tax $ 0

Fixed O&M Excise Tax $/kW-yr 0

Cash Flows and Financial Calculations The model assumes that all qualified incentives are fully utilized. In practice it may be difficult for many commercial entities to fully utilize all available tax credits. However, this limitation is circumvented by business models that utilize third party financers/owners who can fully utilize all the available tax benefits. The AZ incentives utilized in this model are detailed later in the section, Arizona Case Study. In summary, they include 5-year MACRS depreciation, federal and state tax credits, and utility rebates. In addition, the PV system is exempt from state sales tax and property tax. Financial Calculations The project pro forma cash flows are divided into the following categories: Taxable Revenues Electricity Production (kWhac/yr) = [DC capacity] * [Balance of system efficiency] * [Capacity factor] * (1 – [Performance

degredation factor] ^n Where n is the nth year after the first year of operation.

Rebates ($): These may be offered by utilities or state governments. Each type of rebate generally carries with it a number of restrictions that restrict the total size of the rebate in some way. Rebates are assumed to be distributed when the solar system begins operation. Electricity Revenue ($) = [Electricity Production] * [Electricity Price] The electricity price could be a retail rate or an amount included as part of a power purchase agreement. The Electricity Price can vary in time and may be subject to uncertainty as in the case of retail electricity rates or a PPA price indexed to electricity rates. The Electricity Price may also be predetermined in the case of a PPA. Production Based Incentive ($): This is a dollar amount offered to a solar owner based on the amount of annual energy an installation produces. Under some state rules, the amount of energy produced at a given installation is estimated and an upfront payment based on the lifetime energy estimate is made to the installation owner. REC ($): This is a dollar amount based on the amount of energy produced by the installation. A REC is an environmental attribute with monetary value separate from the actual electricity produced by an installation. Capital Costs Equity Capital ($): This is the amount of equity invested in the installation cost. Operating Costs Operations and Maintenance ($): The annual amount required to operate and maintain the installation. Inverter Replacement ($): An Inverter’s lifetime is generally shorter than that of the solar modules and needs to be replaced during the lifetime of the whole installation. Insurance ($): An annual amount to protect against unexpected damage. Property Tax ($): Generally a local tax from which solar installations are often exempted. Depreciation (5-Year MACRS) (Martin, May 26, 2006) Taxable Basis for Depreciation ($): Federal law allows qualifying solar expenses to depreciate using the 5-year MACRS method. For tax purposes, if an owner of a system utilizes the federal tax credit and the 5-Year MACRS method, then amount used to calculate taxable depreciation is as follows: [Depreciation Basis] = [Qualified System Costs] * (1- 0.5* [Federal Tax Credit]) However, under certain ownership structures, such as a sale lease-back where a site host purchases the system then sells to another party only to lease the system, the lessor may allow the lessee to utilize the tax credit while the lessor can utilize the full system cost as the depreciation basis. In this arrangement the lessee would have to report half the federal tax credit it claims as taxable income over a five year period. Corporations will generally use the full system cost for the book value depreciation. Taxable Depreciation Charge ($): This is the amount of annual depreciation incorporated into tax calculations. The yearly depreciation schedule is as follows:

Year Taxable Depreciation Charge

1 20%

2 32%

3 19.2%

4 11.52%

5 11.52%

6 5.76%

Net Book Value (end of year) ($): This is the book value of the system after depreciation using the full system cost as the basis for depreciation. Debt Service Cash Available Before Debt ($) = [Total Taxable Revenues] – [Total Operating Costs] Cash Reserve Infusion ($) = [Debt Service Ratio] *[Total Debt Payment] – [Cash Available Before Debt] Cash Available Before Debt After Infusion ($) = [Cash Reserve Infusion] + [Cash Available Before Debt] Debt Principal Payment ($) = [Total Debt Payment] – [Interest Payment] Interest Payment ($) = [Total Principal Owed ]t-1 * [Debt Rate (Real)]

[Debt Payment ] =

Income Taxes Operating Income Tax ($) = ([Total Taxable Revenues] – [Total Operating]) * [Tax Rate] Cash Reserve Infusion Tax ($) = [Cash Reserve Infusion] * [Tax Rate] * [Cash Reserve Interest Rate] Depreciation Charge Tax = -[Taxable Depreciation Charge] * [Tax Rate] Debt Interest Payment Tax = -[Interest Payment] * [Tax Rate] Federal Tax Credit ($) = 30% * [Project Capital Costs (pre-tax)], If operation begins between January 2006 and December 31, 2008. Otherwise, Federal Tax Credit ($) = 10% * [Project Capital Costs (pre-tax)] Full utilization of the federal tax credit depends on many factors. In addition, the amount of the system expenses which the tax credit is applied depends on a number of federal requirements. For a summary of these requirements refer to (Martin, May 26, 2006). In both situations, the tax credit vests at 20%/yr for five years. So, if the owner of the system sells it within the first five years of operation, then the owner must count the unvested part of credit as taxable income in the year the credit is recaptured. The taxpayer can add back his tax depreciation basis half the recapture income reported in the recapture year. (Martin, May 26, 2006)

State Tax Credit ($) =[ State Tax Credit %] * [Project Capital Costs (pre-tax)]. It is assumed that this is counted as federally taxable income. After Tax Cash Flow ($) = [Total Taxable Revenue] – [Equity Capital] – [Total Operating Costs] – [Total Debt Payment] – [Total Tax Payments] NPV =

IRR = WAMCC (Nominal) at which the NPV equals zero

Unsubsidized Costs =Equity Capital + Operating Cost + Debt Payments + Cash Reserve Infusion Tax + Depreciation Charge Tax + Debt Interest Payment Tax

Unsubsidized LCOE:

Unsubsidized LCOE This section estimates the current LCOE of CPV and PV installations in Phoenix, AZ. The input cost data for each technology are found in the section, Financial Model. The systems are assumed to be 100% equity financed in order to demonstrate the effect of a variable discount rate on the LCOE. The results (Figure 15) show that 2-axis CPV is currently the most expensive technology for commercial and wholesale utility-scale installations whereas 1-axis PV is the least expensive. Currently, average retail commercial electricity rates throughout the US range from $0.06/kWh to $0.20/kWh. (Energy Information Administration) Although the plots were generated using AZ insolation data they still demonstrate that without subsides, the current technologies would be economical in very few markets with the highest retail electricity rates (i.e., HI, CA). Given, that average US wholesale prices range from $0.03/kWh to $0.07/kWh, all the unsubsidized technologies would not be able to compete without some other revenue stream.

Figure 12. Unsubsidized LCOE vs. Discount Rate.

Although, solar energy production is not subject to the risk of rising fuel prices like fossil fuels, it is subject to equipment failure and unreliable insolation data. Given that many subsidized installations produce a rate of return for investors under 10 % (Shah, 2007), the risk of overestimating insolation can reduce profit margins to dangerously low levels. While some types of investors avoid this risk by just investing in the solar tax credits and accelerated depreciation, these investors are still subject technology risk. Although investors can use the full federal solar tax credit when a system begins operation, the credit vests over a period of five years. So, if the project prematurely ceases operation within its first five years, then a prorated fraction of the credit is recaptured by the federal government. (Martin, May 26, 2006) Investors prefer reliable technologies that are backed by historical performance data. (Shah, 2007) Since HCPV is a technology new to the solar market and many manufacturers have yet to test their systems in the field, a major impediment for these manufacturers will be the technology risk their systems carry. Investors manage this risk by adding a risk factor to the discount rate. In Figure

0

0.1

0.2

0.3

0.4

0.5

0.6

Un

sub

sid

ize

d L

CO

E ($

/kW

h)

Discount Rate

CPV 2-Axis Commercial

PV Fixed Commercial

CPV 2-Axis Utility

PV Fixed Utility

PV 2-Axis Commercial

PV 1-Axis Commercial

PV 2-Axis Utility

PV 1-Axis Utility

15 the slope of the 2-axis CPV curve indicates that a 1% change in the discount rate leads to a $0.02 change in the LCOE. In the increasingly competitive solar market, HCPV manufacturers will need strong warranties or creative partnerships with systems integrators to overcome these technology risk factors. Another way to mitigate the risk factor is to reduce the HCPV installation size. As discussed later in the report, retail installations generally produce higher rate of return than wholesale installations. Since retail installation sizes are generally smaller, HCPV module manufacturers might find market entry paths selling to large commercial or industrial end customers (e.g. wastewater treatment plants). Another strategy would be to integrate a small patch of HCPV into a larger wholesale installation. Investors have a great deal of leverage over the technology used by systems integrators. (Shah, 2007) A small HCPV module company with limited resources could possibly find investors who play a dual role. That investor could have an equity stake in the HCPV module company and invest in installations. This type of situation would produce a positive feedback by incentivizing the investor to use his or own modules which would stimulate manufacturing production and bring down future module costs which would increase returns on future installations.

VI. ARIZONA CASE STUDY This section explores the impact of state and federal government subsidies on the economics of PV and CPV installations in AZ. Federal and state governments in the SW US classify technologies that are eligible for particular solar subsidies. (DSIREUSA) CPV falls into the broader class of photovoltaics and therefore receives the same US subsidies as the other PV technologies mentioned in this paper. The incentives for commercial (non-residential) photovoltaics include (DSIREUSA):

Federal tax credit of 30% of the capital cost. This includes standard equipment and labor costs up to the point of transmission (i.e. transmission expenses do not receive the credit). Utility owned equipment is not eligible for this tax credit.

State tax credit of 10 % of the capital cost. This is counted as federal taxable income. The credit is capped at $25,000 for installations at a single building or $50,000 total credit in one year.

5-year MACRS depreciation. The depreciable basis is calculated as the capital cost minus half the federal tax credit.

State property tax exemption.

State sales tax exemption. Retail installations receive the following utility subsidy:

A Rebate on the capital cost that varies by utility. For Arizona Public Service (APS), the largest utility in AZ, it is $2.50/Wdc with a maximum total rebate of $500,000. By accepting the rebate, the owner of the system gives APS ownership of all the RECs produced.

The following prices are used in the analysis:

2008 retail electricity rate: $0.09/kWh with a real growth of 1%/yr.

2008 wholesale electricity rate: $0.06/kWh with a real growth of 1%/yr.

REC price: $0.015/kWh

The IRR vs. capital cost of fully subsidized retail and wholesale installations is shown in Figure 166. IRR was chosen over NPV as a metric because it is assumed that the hypothetical investors’ required rate of return is unknown. The installations are assumed to be 100% equity financed. The rectangles surrounding portions of the retail and wholesale curves indicate ranges of capital costs that may be achievable today. Again, due to the uniformity of the subsidies, 2-axis CPV produces the lowest rate of return. The primary reasons why retail installations produce a much higher IRR than the wholesale installations are due to the utility rebate being unavailable to wholesale installations and the wholesale installations’ lower revenue stream (i.e. electricity + RECs).

Figure 13. Internal Rate of Return vs. Capital Cost.

Fully Subsidized System (Phoenix, AZ)

6 IRR and NPV are commonly used financial metrics depicting the value of a series of cash flows. Sometimes when

ranking different projects it is possible for IRR and NPV to produce conflicting results. For example: Project A: IRR = 50%. NPV = $1000 Project B: IRR = 40%. NPV = $1500 This discrepancy is possible when the series of cash flows change sign more than once (e.g. -, +, -). When ranking mutually exclusive projects NPV is often preferred when it is uncertain whether or not conflicting results will occur. In the AZ case study (Figure 16) the different technologies do not produce conflicting NPV and IRR results.

0%

5%

10%

15%

20%

25%

30%

35%

40%

50

00

54

67

59

33

64

00

68

67

73

33

78

00

82

67

87

33

92

00

96

67

10

13

3

10

60

0

11

06

7

11

53

3

12

00

0

Inte

rnal

Rat

e o

f R

etu

rn

Capital Cost ($/kWdc)

PV 1-Axis Commercial

PV 2-Axis Commercial

PV Fixed Commercial

CPV 2-Axis Commercial

PV 1-Axis Utility

PV 2-Axis Utility

PV Fixed Utility

CPV 2-Axis Utility

Retail Capital Costs Range

Wholesale Capital Cost Range

VII. HCPV FUTURE POTENTIAL The previous sections of this report highlight the need for HCPV capital costs to be lower than 1-axis PV and 2-axis PV in order to be competitive. It was also noted that HCPV currently supplies less than 1% of the US market and it is currently estimated to have a capital cost higher than other PV choices. However, due to the fact that most HCPV manufacturers do not have full scale production capabilities or they have not even begun commercial production, the HCPV space should be considered embryonic. (Sophisticated Optics, 2007) Technologies and their costs are dynamic and subject to evolution so the dynamics of the HCPV industry should be examined. One study by Gregory Nemet (Nemet, 2006) examined the learning curve for the world Si PV module industry from 1975 to 2001. A goal of the study was to de-aggregate the learning curve to understand the factors that have brought down module prices since the terrestrial PV market commenced in 1975. The two dominant factors bringing down prices were primarily economies of manufacturing scale (not necessarily heuristic learning) and secondarily, an increase in solar cell efficiency. Industry production increases were made possible by government subsidies which enabled a market for terrestrial modules. Cell efficiency gains were primarily a result of research in universities and government labs. In 1974 the market share of PV for terrestrial applications was 4% whereas satellites accounted for the remaining 96%. By 1979, terrestrial market share had grown to 64%. This embryonic terrestrial market period was characterized by a sharp factor of three module cost decline (3/4yrs). This cost reduction was accelerated by the fact that the satellite market had a lower elasticity of demand than terrestrial markets. In addition, terrestrial cells did not need to be as high quality as satellite cells. From 1979 – 2001 the learning curve brought costs down by only a factor of seven (7/21yrs). There is a parallel between the early period of the terrestrial PV market and the current HCPV market. Satellites have historically been the largest market for high-efficiency III-V cells but the terrestrial HCPV market is projected to dominate in terms of market share. (Boeing) Comparing a Si PV learning curve (Nemet, 2006) to a HCPV learning curve (Luque, 2004) reveals that HCPV modules could reach a price of approximately $0.5/W after 1 GW of cumulative production whereas PV modules could reach $1/W after 1.3 TW of cumulative production. The timing of these milestones is quite uncertain. Reaching the PV milestone by 2050 would require an annual industry growth rate of 11%. Using a CPV growth projection by Sharp (Sharp Solar Systems Group, December 7, 2006) reveals a rough estimate of the CPV milestone being met circa 2020. This HCPV scenario would result in an unsubsidized LCOE of roughly $0.07/kWh which would be competitive in retail and wholesale markets.

VIII. CONCLUSIONS A confluence of factors are converging that will allow CPV to make significant strides in SW US solar markets. Commercial III-V cells have reached 35%+ efficiency which is a major factor in lowering CPV module costs. The use of these cells is more economical in HCPV modules than LCPV modules. In addition, the HCPV design has potential to produce a lower LCOE which is tempered against fluctuating cell prices. For large-scale retail installations and wholesale installations the high-concentration design’s attributes make it a better competitor than LCPV. Despite the lower returns in wholesale markets, their growth will be spurred on by RPS mandates especially in locations where retail markets cannot meet the mandates and in locations where other cheaper non-solar renewable resources have been exhausted.

HCPV holds promise but must surmount formidable challenges in order to grow market share. HCPV project economics must be compared to fixed PV, 1-axis PV, and 2-axis PV. Out of these technologies, 1-axis PV is estimated to currently have the lowest LCOE and highest rate of return while HCPV has the highest LCOE and lowest rate of return. These results were calculated for hypothetical installations in Phoenix, AZ which has some of the highest direct insolation in the US. However, the economics of HCPV will be worse in lower direct-insolation areas. In order for HCPV to achieve the same LCOE as 2-axis PV and 1-axis PV it will need to achieve a reduced relative capital cost of approximately 25% - 35% and 21% - 31%, respectively in the SW US. In addition, HCPV manufacturers will also have the burden of proving the reliability of their systems in the field. Financiers will likely require a higher rate of return for risky technologies. Nevertheless, HCPV manufacturers should seek investment arrangements and partnerships where the other parties can benefit from the growth of HCPV manufacturing and from returns on HCPV installations. By 2020 it is estimated that cumulative global HCPV manufacturing capacity could reach 1 GW. Under this scenario HCPV’s LCOE is projected to reach $0.07/kWh which would be competitive without subsidies in many SW retail and wholesale markets. The initial phase of this learning curve will be especially accelerated as the terrestrial HCPV market for III-V cells overtakes the satellite market. During this period of growth HCPV will find its competitive niche with medium-scale installations on the order of 100 kW – 50 MW while CST’s storage capabilities and overall economics will give it an advantage in larger installations. Due to the increasingly competitive nature of the solar market, modules could become more of a commodity product. However, now world markets are fragmented, dynamic, and characterized by a large number of new entrants. It will important for start-up HCPV manufacturers to expedite the introduction of products while not sacrificing quality. The bar for market entry may be raised in the future as existing HCPV manufacturers establish quality brands and continual improvements are made to traditional PV.

Bibliography Energy Information Administration. (2006). Retrieved April 2008, from Table 2.21 Photovoltaic Cell and

Module Shipments by Type:

http://www.eia.doe.gov/cneaf/solar.renewables/page/solarphotv/solarpv.html

Abboud, L. (2007, September 21). The Silicon Shakeup. Retrieved April 2008, from Wall Street Journal:

http://online.wsj.com/article/SB119033317212334604.html?mod=googlewsj

Agency, I. I. (2007). Trends in Photovotaic Applications: Survey report of selected IEA countries between

1992 - 2006.

Antonio Luque, V. M. (2007). Concentrator Photovoltaics. Springer Series in Optical Sciences.

Black and Veatch. (June, 2007). Arizona Renewable Energy Assessment.

Boeing. (n.d.). Spectrolab. Retrieved April 22, 2008, from http://www.boeing.com/defense-

space/space/bss/factsheets/spectrolab/spectrolab.html

Broido, C. (2008, February). (S. Connor, Interviewer)

C. Baur, A. W. (Vol. 129. August, 2007). Triple-Junction Concentrator Solar Cells: Perspectives and

Challenges. Journal of Solar Engineering .

California Energy Commission. (2008, February). Database of Investor-Owned Utilities' Contracts for

Renewable Generation, Contracts Signed Towards Meeting the California Renewables Portfolio Standard

Target. Retrieved April 13, 2008, from http://www.energy.ca.gov/portfolio/contracts_database.html

Cameron, C. (January 2008). Multi-year Program Plan Commercial Benchmark. Sandia National

Laboratory.

Department of Energy. (2005). Solar Energy Technologies Progra: Multi-year Program Plan 2007-2011.

DSIREUSA. (n.d.). Database of State Incentives for Renewables and Efficiency. Retrieved April 2008, from

dsireusa.org

E3. (2008, April). CPUC Greenhouse Gas Modelling. Retrieved from Renewable Energy Supply Curves:

http://www.ethree.com/cpuc_ghg_model.html

Emcore. (2006, February). Piper Jaffray Opportunities in Solar and Clean Tech Symposium.

Energy Information Administration. (n.d.). Average Retail Price of Electricity to Ultimate Customers by

End-Use Sector, by State . Retrieved March 2008, from

http://www.eia.doe.gov/cneaf/electricity/epm/table5_6_a.html

Energy Information Administration. (n.d.). Wholesale Market Data. Retrieved 2008, from Wholesale

Averages: http://www.eia.doe.gov/cneaf/electricity/wholesale/wholesale.html

Forbes. (2007, September 9). Light And Heat. Retrieved April 13, 2008, from

http://www.forbes.com/forbes/2007/0903/082.html

Goswami, F. K. (2007). Handbook of Energy Efficiency and Renewable Energy.

Grunow, M. (2007, December). (S. Connor, Interviewer)

Kinsey, G. (2007). Concentrator Photovoltaics: Getting More from Less. Solar Power 2007. Long Beach,

CA.

Luque, A. M. (2004). Next Generation Photovoltaics: High Efficiency Through Full Spectrum Utilization.

IOP Publishing.

Martin, K. (May 26, 2006). Federal Tax Incentives for Solar Energy Version 1.2. Solar Energy Industries

Association.

Maycock, P. (2007). Photovoltaic Energy Conversion A Critical Analysis. PV Energy Systems.

Nemet, G. F. (2006). Beyond the Learning Curve: Factors Influencing Cost Reductions in Photovoltaics.

Energy Policy , 3218-3232.

NREL. (n.d.). Solar Radiation Data Manual for Flat-Plate and Concentrating Collectors. Retrieved

February 2008, from http://rredc.nrel.gov/solar/pubs/redbook/

Palmer, T. (2007). Concentrating PV: Getting More From Less. Solar Power 2007. Long Beach, CA.

Renzetti, F. R. (2007). Comparison Between Different Solar Concentrators as Regards to the Electric

Generation.

Shah, J. (2007). Power Purchase Agreements: The New Frontier. Solar Power 2007. Long Beach, CA.

Sharp Solar Systems Group. (December 7, 2006). Sharp Solar Presentation.

Sherif, R. A. (2005). The Path to 1 GW of Concentrator Photovoltaics Using Multijunction Solar Cells.

Proc. 31st IEEE PVSC. Orlando, FL.

Solar Buzz. (June 2007). US Grid Connect PV Market 2007.

Sophisticated Optics. (2007, 3). Sun & Wind Energy .

Swanson, R. (2000). The Promise of Concentrators. Progress in Photovoltaics: Research and Applications.

Walter Short, D. P. (1995). A Manual for the Economic Evaluation of Energy Efficiency and Renewable

Energy Technologies. NREL.

APPENDIX

Table 1. Summary of CPV Designs by Current Manufacturers. (Sophisticated Optics, 2007)

Company

Product

Name

System

size (kWp)

Optical System

Concentration Ratio

Cell Type

Cell Efficie

ncy

Cell Manufact

urer

Module

Efficiency

System

Efficiency

Cooling Type

Tracking

System

Tracking

Error (º)

Power Input

Warranty

(years)

Concentration

Amonix, Inc.

Mega Modul

e 25

Fresnel lens

500 Silicon 28% Ammonix 18% 16% Passive hydraulic

0.1 1% High

Concentrating Technologies, Inc.

Micro Dish

3 - 50 Dish

mirror 500

GaInP/GaAs/Ge

35% Spectrola

b 25% 23% Passive 0.1 7 W High

Concentrix Solar GmbH

CX 5000

5.75 Fresnel

lens 385

GaInP/GaAs/Ge

33% 25% 23% Passive 2-axis <0.05 <10 W High

Cool Earth Solar

SA-10 10 Inflated mirror

220 Silicon 22% Sunpower 17% 11% Active Tensil

e truss

0.6 <500 High

Daido Steel / Daido Metal

1 - 10 Fresnel

lens 550 - 1340

GaInP/GaAs/Ge

30-35%

Sharp 28% 20% Passive 6W/kWp

n/a high

Day4Energy

LCPV receiv

er

individual

Fresnel lens / mirror

<=10 Silicon 16.5% Schott Solar

16.5% Passive 1 or 2 axis

+/-2 20 Low

Emcore 25 Fresnel

lens 500

GaInP/GaAs/Ge

35% Emcore Passive 25 High

Entech, Inc.

Advanced SR

50 point focus

Fresnel 500

multi junction

35% various 30% 24% Active/Pa

ssive 2-axis .1 200 20 High

Entech, Inc.

Solar Row (SR)

25 linear

Fresnel lens

21 Silicon 20% various 17% 14% Active/Pa

ssive 2-axis .1 200 20 Low

Green and Gold Energy

Sun Cube

0.3 Fresnel

lens 1000+

GaInP/GaAs/Ge

35% Various 35% 35% Passive 2-axis +/-5 <=1 W High

GreenVolts

HCPV 3.5 Off-axis

dish 625

GaInP/GaAs/Ge

37% Spectrola

b Passive 2-axis High

Guascor Fotón

SIFAC 25K

25 Fresnel

lens 400 Silicon 28%

Guascor Fotón

Passive el

hydro 0.7 High

JX Crystals Inc.

3-sun panel

1,2,25 Mirrors 3 Silicon 20% Sunpower 15% 14% Passive 1-axis 1 0.1% 10 Low

Pyron Solar

Pyron Triad

90 Propriet

ary 500

GaInP/GaAs/Ge

36% Spectrola

b 30% 28% Passive 2-axis +/-2 5 W 10 High

Sharp Solar

Sharp CPV

2.9 Fresnel

lens 700

GaInP/GaAs/Ge

37% Sharp Passive Speci

al High

Silicon Valley Solar

Sol-X 0.2 /

module

TIR 2.2 Silicon 17% Ersol 15.3% Passive None 25 Low

NuEdison NExT modul

e

Assymetric

concentrator

2 - 4.5 Silicon 16 - 20%

16 - 20%

Passive None Low

Sol3g M40 1,4,

other

Fresnel +

secondary

476 GaInP/GaA

s/Ge 30 - 32%

Azur Space

21 - 25%

19 - 23%

Passive 2-axis 0.5 negligi

ble 2 High

SolarTec AG

Sol Con

2.16 Fresnel

lens 400

GaInP/GaAs/Ge

36% ENE

Spectrolab

28% Passive 2-axis 0.1 200 W <= 20 High

Solfocus Gen 1 Refl

optics, mirrors

500 GaInP/GaA

s/Ge 35% Various 17% 15% Passive 2-axis 0.5 High

Stellaris Corporation

Clear Power

1 - 100+

Non-imaging optics

3 CIGS, silicon

Passive none Low

Taihan Techren Co

MS-900

5 Fresnel

lens 9 Silicon

16 - 20%

14 - 18%

11 - 12%

Passive 2-axis 1 Low

Whitfield Solar

Sun Light

0.3 Fresnel

lens 40 Silicon 19% BP Solar 15% 13% Passive

tilt & roll

0.1 1 Low

Pacific Solar Tech

Micro PV

0.5 - 1000

Fresnel lens

2 - 10 Silicon 15% 13% Passive None 25 Low

Prism Solar Technologies

HPC Hologra

phic film

1.25 - 2 any cell

type None None 20 Low

Figure 14. (NREL)

(NREL)

Figure 15. (NREL)

(NREL)

Figure 16. (NREL)

(NREL)