TODAY’S STUDY: WHERE THE COSTS ARE IN SOLAR

Benchmarking Non-Hardware Balance of System (Soft) Costs for U.S. Photovoltaic Systems Using a Data-Driven Analysis from PV Installer Survey Results

Kristen Ardani, Galen Barbose, Robert Margolis, Ryan Wiser. David Feldman, and Sean Ong, November 2012 (National Renewable Energy Laboratory and Lawrence Berkeley National Laboratory)

Executive Summary

This report presents results from the first U.S. Department of Energy (DOE) sponsored, bottomup data-collection and analysis of non-hardware balance-of-system costs—often referred to as “business process” or “soft” costs—for residential and commercial photovoltaic (PV) systems. Annual expenditure and labor-hour-productivity data are analyzed to benchmark 2010 soft costs related to the DOE priority areas of (1) customer acquisition; (2) permitting, inspection, and interconnection; (3) installation labor; and (4) installer labor for arranging third-party financing. Annual expenditure and labor-hour data were collected from 87 PV installers. After eliminating outliers, the survey sample consists of 75 installers, representing approximately 13% of all residential PV installations and 4% of all commercial installations added in 2010.

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Including assumed permitting fees, in 2010 the average soft costs benchmarked in this analysis total $1.50/W for residential systems (ranging from $0.66/W to $1.66/W between the 20th and 80th percentiles). For commercial systems, the median 2010 benchmarked soft costs including assumed permitting fees) are $0.99/W for systems smaller than 250 kW (ranging from 0.51/W to $1.45/W between the 20th and 80th percentiles) and $0.25/W for systems larger than 250 kW (ranging from $0.17/W to $0.78/W between the 20th and 80th percentiles). Additional soft costs not benchmarked in the present analysis (e.g., installer profit, overhead, financing, and contracting) are significant and would add to these figures. The survey results provide a benchmark for measuring—and helping to accelerate—progress over the next decade toward achieving the DOE SunShot Initiative’s soft-cost-reduction targets.

We conclude that the selected non-hardware business processes add considerable cost to U.S. PV systems, constituting 23% of residential PV system price, 17% of small commercial system price, and 5% of large commercial system price (in 2010). These processes present significant opportunities for further cost reductions and labor-productivity gains.

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Introduction

The global average wholesale price for photovoltaic (PV) modules fell from $4.04 per watt (W) in 2005 to $2.40/W in 2010, while the capacity-weighted average of residential and commercial U.S. PV system prices declined from $7.90/W to $6.20/W over the same period (Barbose et al. 2011). Thus, the reduction in module price accounted for the vast majority of the total decline in average installed PV system price from 2005 to 2010. Consequently, non-module hardware and non-hardware costs have accounted for a significant, and increasing, portion of average installed PV system prices in the United States (Barbose et al. 2011). To track and analyze the rapidly evolving price structures of PV systems, a thorough understanding of non-module cost components is needed.

To date, a number of analyses have examined non-module PV system hardware costs, including the costs of power electronics and other balance-of-system (BOS) hardware elements. Several other analysts have examined non-hardware BOS costs—often referred to as “business process” or “soft” costs—which include permitting and commissioning, profit, overhead, installation labor, customer acquisition, and financing. Goodrich et al. (2012), for example, estimate that total soft costs constituted, on average, 47% of U.S. installed residential PV system price and 33% of installed commercial system price in 2010, with variation around this average based on system size, location, and other factors. Some analysts have published details about individual soft-cost elements, and others have produced results that are available by subscription only.

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A survey data-driven, bottom-up examination of soft costs for residential and commercial PV systems, with granularity into multiple individual cost components, has not been published to date. The purpose of this analysis, therefore, is to provide further granularity to total soft-cost estimates and quantify specific and previously unmeasured soft costs for residential and commercial PV systems. Unlike PV hardware costs, which can be readily benchmarked with data collected from equipment manufacturers and purchasers (and for which a variety of available indexes already exist), quantifying soft costs requires detailed tracking of the time and resources required to complete the various stages of a PV system sale and installation. To quantify key soft costs, we fielded a survey of U.S. PV installers that collected data on the labor hours required, per installation, to complete discrete stages of the PV business process, along with data on annual expenditures for customer acquisition. Similar to Goodrich et al. (2012), we translate labor-hour requirements per installation into dollars per watt using system size, labor class and composition assumptions, and fully burdened wages.

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Our survey data and analysis focus on soft costs related to the U.S. Department of Energy (DOE) priority areas of (1) customer acquisition; (2) permitting, inspection, and interconnection (PII); (3) installation labor; and (4) installer labor for arranging third-party financing. Other soft costs and end-consumer price components not benchmarked by this analysis—including installer profit, overhead, financing, and contracting—contribute significantly to system prices and represent areas for further study.

The average 2010 soft costs benchmarked in this analysis total $1.50/W for residential systems (ranging from $0.66/W to $1.66/W between the 20th and 80th percentiles). For commercial systems, the median 2010 benchmarked soft costs are $0.99/W for systems smaller than 250 kW (ranging from $0.51/W to $1.45/W between the 20th and 80th percentiles) and $0.25/W for systems larger than 250 kW (ranging from $0.17/W to 50.78/W between the 20th and 80th percentiles).

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The DOE SunShot Initiative aims to reduce the installed-system price contribution of all soft costs to approximately $0.65/W for residential systems and $0.44/W for commercial systems by 2020 (DOE 2012). Our results provide a partial benchmark for measuring progress over the next decade toward achieving these total soft-cost targets—and inform strategies for accelerating softcost reductions.

The remainder of this report is structured as follows. Section 2 briefly describes the existing softcost literature. Section 3 describes the survey and analysis methodology we used. Section 4 describes the residential PV system data collection and results, and Section 5 does the same for commercial PV systems. Section 6 discusses the study’s limitations. Section 7 summarizes the results, and Section 8 draws conclusions and outlines areas for potential future work. Appendix A contains our installer survey instrument.

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Summary of Survey Results

The results of the residential installer survey suggest that the surveyed soft costs constitute a significant portion of total residential PV soft costs (Figure 13); including assumed permitting fees, the surveyed costs total $1.50/W, equivalent to 45% of total system soft costs ($3.32/W) and 23% of total system price in 2010 ($6.60/W). Based on a 2010 installed PV system price of $6.60/W, the difference in total soft costs and soft costs captured by the survey totals $1.82/W. This residual cost of $1.82/W is the subject of future analysis aimed at refining the granularity of PV system price benchmarks. Customer-acquisition ($0.67/W) and installation-labor costs ($0.59/W) are the largest of the soft costs benchmarked in this analysis, suggesting considerable cost efficiency gains can be made in these areas. However, streamlining PII requirements ($0.13/W for select PII costs in this analysis) is also an important cost-reduction opportunity. PII costs account for an estimated 25%–35% of the price difference between U.S. and German residential PV prices. Finally, while the benchmarked installer labor costs for arranging thirdparty financing are negligible ($0.02/W), additional data on financing costs are needed to depict the cost of financing and contracting PV systems more completely and accurately.

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As shown in Figure 13, the surveyed soft costs also constitute a significant portion of total commercial PV soft costs, although their impact depends significantly on system size. For small commercial systems (smaller than 250 kW), the surveyed costs (including assumed permitting fees of $0.35/W) total $0.99/W, equivalent to roughly 37% of all soft costs ($2.64/W) and roughly 17% of the total average system price in 2010 for commercial systems in that size range ($5.96/W). In contrast, surveyed soft costs for large systems (larger than 250 kW) are just $0.25/W (including $0.03/W for assumed permitting fees), or 12% of all soft costs ($2.16/W) and 5% of the total average system price in 2010 ($5.33/W).

Of the various commercial PV labor-related soft costs, installation labor is by far the most significant: $0.42/W for small systems and $0.18/W for large systems. This suggests that efforts to reduce commercial PV system costs ought to focus on this category. Customer acquisition adds $0.19/W to the cost of small commercial systems but only $0.03/W for large systems because large systems benefit from economies of scale and the ability to spread those (relatively) fixed costs over a larger number of installed watts. Labor costs associated with PII and arranging third-party financing are generally negligible ($0.02/W or less) for the surveyed commercial PV installers. Table 5 summarizes the soft costs considered in this analysis for residential and commercial systems.

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Conclusions and Future Work

As PV system prices continue to decline owing to module and hardware cost reductions, accurately quantifying soft costs is increasingly important for explaining PV system price dynamics across various U.S. and international markets. This report presents results from a bottom-up, survey-data-driven analysis of soft costs in the DOE priority areas of customer acquisition, financing, PII, and installation. This work establishes benchmark soft costs for residential and commercial PV with the objectives of tracking costs over time, identifying opportunities for cost reductions, and informing the development of policies and practices aimed at reducing cost inefficiencies.

The soft costs collected and analyzed in this report constitute a significant portion of total installed PV system prices. As Table 5 shows, including assumed permitting fees, the total surveyed soft costs are 23% of the total residential system price, 17% of the small commercial system price, and 5% of the large commercial system price (in 2010). Clearly, economies of scale help reduce these soft costs, particularly installation-labor and permitting costs, for large commercial systems compared with residential and small commercial systems. Among the individual surveyed soft-cost categories, customer acquisition and installation labor are the dominant contributors, while PII and labor for arranging third-party financing contribute relatively little cost. Thus, among the select costs analyzed in this report, customer acquisition and installation labor present the greatest potential for cost reductions for residential and commercial PV systems.

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The SunShot Initiative aims to reduce the installed-system price contribution of all soft costs to approximately $0.65/W for residential systems and $0.44/W for commercial systems by 2020 (DOE 2012). Our surveyed soft costs alone (including assumed permitting fees) contribute $1.50/W for residential systems, $0.99/W for small commercial systems, and $0.25/W for large commercial systems. Additional soft costs we did not survey (e.g., installer profit, overhead, financing, and contracting) are significant and would add to these figures. Thus, our survey results provide a partial benchmark for measuring progress over the next decade toward achieving the SunShot soft-cost-reduction targets.

Our ongoing and future work will expand on both the comprehensiveness and accuracy of PV soft-cost analysis, thus enabling a more complete understanding of soft costs and providing dataderived metrics in support of private and public soft-cost-reduction efforts. For example, PV system financing is a poorly understood soft cost. As discussed in Sections 4.2.4 and 5.2.4, residential and commercial PV financing is complex and vital to enabling large-scale PV deployment. We are working to enable a better understanding of PV-financing strategies and their impacts on PV system prices and to develop benchmarks for tracking reductions in financing costs.

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As another example, customer-acquisition costs are increased by potential PV customers’ lack of access to credible, standardized PV performance data and by installers’ need to visit potential PV sites to develop preliminary system designs and prepare bids. Future work in these areas could include benchmarking the specific cost contributions of these barriers and estimating the costreduction potential of solutions such as web-based dissemination of third-party-verified PV consumer data and remote PV site assessment.

As Germany’s experience has shown, streamlining the PII process for PV systems is also an important cost-reduction strategy. Analyzing the cost impacts of various PII reforms in U.S. jurisdictions—and disseminating information about effective strategies—could enable relatively rapid soft-cost reductions. In addition, strategies for reducing installation-labor costs are critically important, such as the development of “plug and play” PV systems and widespread implementation of PV installer training and certification programs. Developing more accurate, granular analysis of installation-labor costs would enable the effectiveness of such strategies to be evaluated and optimized.

Finally, understanding the location-dependent variability of soft costs throughout the United States is important. Our future work will seek to expand both the geographic scope of our softcost analysis and the geographic specificity of the results.

Soft costs are both a major challenge and a major opportunity for reducing PV system prices and stimulating SunShot-level PV deployment in the United States. The data and analysis in this report are a first step toward the more detailed understanding of PV soft costs required to track and accelerate these price reductions.

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