NewEnergyNews: TODAY’S STUDY: ALL THE COSTS OF SOLAR

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Gleanings from the web and the world, condensed for convenience, illustrated for enlightenment, arranged for impact...

The challenge now: To make every day Earth Day.

YESTERDAY

  • FRIDAY WORLD HEADLINE-Aussie Farmers Worrying About Climate Change
  • FRIDAY WORLD HEADLINE-The Climate Change Solution At Hand, Part 1
  • FRIDAY WORLD HEADLINE-The Climate Change Solution At Hand, Part 2
  • FRIDAY WORLD HEADLINE-New Energy And Historic Buildings In Europe
  • THE DAY BEFORE

    THINGS-TO-THINK-ABOUT THURSDAY, December 1:

  • TTTA Thursday-First Daughter Ivanka May Fight For Climate
  • TTTA Thursday-Low Profile High Power Ocean Wind Energy
  • TTTA Thursday-A Visionary Solar Power Plant
  • TTTA Thursday-EVs Have A Growth Path
  • THE DAY BEFORE THE DAY BEFORE

  • ORIGINAL REPORTING: How The Clean Power Plan Drove The Utility Power Mix Transition
  • ORIGINAL REPORTING: How Utilities Are Answering The Distributed Energy Resources Challenge
  • ORIGINAL REPORTING: Looking At New Rates To Unlock The Utility Of The Future
  • THE DAY BEFORE THAT

  • TODAY’S STUDY: The Power Potential Of Personal Wind
  • QUICK NEWS, November 29: Climate Change Forces Hard Choices In Alaska; New Energy To Utilities-“Can’t-Beat-Us-So-Join-Us”; Fact-Checking Trump Hot Air On Wind
  • AND THE DAY BEFORE THAT

  • TODAY’S STUDY: Getting More New Energy On The Grid
  • QUICK NEWS, November 28, 2016: Pope Talks Climate Change At Trump; Solar Comes To The Mall; The Big Possibilities Of Backyard Wind
  • THE LAST DAY UP HERE

  • Weekend Video: Why President Trump Can’t Stop New Energy
  • Weekend Video: 7 Things Climate Change Will Mean
  • Weekend Video: Wireless EV Charging Stations
  • --------------------------

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    Anne B. Butterfield of Daily Camera and Huffington Post, f is an occasional contributor to NewEnergyNews

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    Some of Anne's contributions:

  • Another Tipping Point: US Coal Supply Decline So Real Even West Virginia Concurs (REPORT), November 26, 2013
  • SOLAR FOR ME BUT NOT FOR THEE ~ Xcel's Push to Undermine Rooftop Solar, September 20, 2013
  • NEW BILLS AND NEW BIRDS in Colorado's recent session, May 20, 2013
  • Lies, damned lies and politicians (October 8, 2012)
  • Colorado's Elegant Solution to Fracking (April 23, 2012)
  • Shale Gas: From Geologic Bubble to Economic Bubble (March 15, 2012)
  • Taken for granted no more (February 5, 2012)
  • The Republican clown car circus (January 6, 2012)
  • Twenty-Somethings of Colorado With Skin in the Game (November 22, 2011)
  • Occupy, Xcel, and the Mother of All Cliffs (October 31, 2011)
  • Boulder Can Own Its Power With Distributed Generation (June 7, 2011)
  • The Plunging Cost of Renewables and Boulder's Energy Future (April 19, 2011)
  • Paddling Down the River Denial (January 12, 2011)
  • The Fox (News) That Jumped the Shark (December 16, 2010)
  • Click here for an archive of Butterfield columns

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    Some details about NewEnergyNews and the man behind the curtain: Herman K. Trabish, Agua Dulce, CA., Doctor with my hands, Writer with my head, Student of New Energy and Human Experience with my heart

    email: herman@NewEnergyNews.net

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      A tip of the NewEnergyNews cap to Phillip Garcia for crucial assistance in the design implementation of this site. Thanks, Phillip.

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    Pay a visit to the HARRY BOYKOFF page at Basketball Reference, sponsored by NewEnergyNews and Oil In Their Blood.

  • ---------------
  • WEEKEND VIDEOS, December 3-4:

  • Trump Truth And Climate Change
  • The Daily Show Talks Pipeline Politics
  • Beyond Polar Bears – The Real Science Of Climate Change

    Wednesday, December 11, 2013

    TODAY’S STUDY: ALL THE COSTS OF SOLAR

    Benchmarking Non-Hardware Balance-of-System (Soft) Costs for U.S. Photovoltaic Systems, Using a Bottom-Up Approach and Installer Survey – Second Edition

    Barry Friedman, Kristen Ardani, David Feldman, Ryan Citron, Robert Margolis, and Jarett Zuboy October 2013 (National Renewable Energy Laboratory)

    Executive Summary

    This report presents results from the second U.S. Department of Energy (DOE) sponsored, bottom-up data-collection and analysis of non-hardware balance-of-system costs—often referred to as “business process” or “soft” costs—for U.S. residential and commercial photovoltaic (PV) systems. Annual expenditure and labor-hour-productivity data are analyzed to benchmark 2012 soft costs related to (1) customer acquisition and system design and (2) permitting, inspection, and interconnection (PII). We also include an in-depth analysis of costs related to financing, overhead, and profit.

    The second annual survey of U.S. PV installers was deployed from September 2012 to May 2013, focusing on customer acquisition and PII costs for the study period of January 1 to June 30, 2012. We gathered data from 55 residential PV installers, representing 4,260 residential installations and approximately 27 MW of residential capacity installed during the first half of 2012. We cleaned the data for outliers, yielding sample sizes ranging by cost category from 47 to 53. We also gathered data from 22 commercial PV installers, representing 269 commercial PV installations during the 6-month study period for a total of 66 MW of capacity.

    According to our analysis, the soft costs accounted for a significant portion of total installed PV system prices in the first half of 2012: 64% of the total residential system price, 57% of the small (less than 250 kW) commercial system price, and 52% of the large (250 kW or larger) commercial system price. In contrast to the first edition of this report, in this second edition we have unpacked the “other soft cost” category using a detailed “bottom-up” cost-accounting framework into five categories: transaction costs, indirect corporate costs, installer/developer profit, supply chain costs, and sales tax. Specifically, we model a third-party ownership structure, capturing the costs of doing business that have not been previously quantified, such as engineering, procurement, and construction (EPC), developer and finance department staff and overhead, professional/legal services, capital costs during construction, and other costs that may not be attributable to specific PV projects. The detailed breakdown of soft and hard costs are shown in Figure ES-1.

    As shown in Figure ES-1, we find that economies of scale helped reduce soft costs, particularly when comparing the residential and small commercial systems with the large commercial systems. Among the individual soft-cost categories we characterized, supply chain costs, indirect corporate costs, transaction costs, and installer/developer profit are dominant contributors, followed by installation labor, sales tax, and customer acquisition. PII contributes relatively little cost when measured in terms of dollars per watt but presents a market barrier that can deter project completion entirely. It is difficult to know for certain how many projects are deterred in this way each year, but the issue underscores the importance of considering market barriers and other market factors, rather than limiting soft-cost analysis to installed costs. Our analysis suggests that customer acquisition and PII costs decreased from 2010 to 2012.

    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, in 2010 dollars (DOE 2012).1

    The soft costs we characterized contributed $3.19/W for residential systems, $2.90/W for small commercial systems, and $2.02/W for large commercial systems, in 2010 dollars.

    Soft costs for residential and large commercial systems declined in the United States between 2010 and 2012, while small commercial soft costs increased. This second benchmarking effort characterizes all PV soft costs—which the previous edition did not—and represents the most granular analysis to date that measures progress toward the SunShot soft-cost-reduction targets.

    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 series of benchmarking reports are a step toward the more detailed understanding of PV soft costs required to track and accelerate these price reductions.

    Summary of Survey Results

    In 2012, soft costs accounted for more than half of total system prices for residential, small commercial and large commercial photovoltaic (PV) installations (Figure 1).2 We used our various data sources and methods to attribute all these soft costs to specific categories in the first half of 2012 (Figure 1 and Table 1). For residential systems, the greatest soft costs were supply chain costs ($0.61/W), installation labor ($0.55/W), customer acquisition ($0.48/W), and indirect corporate costs ($0.47/W). For small commercial systems, the largest soft costs were developer/installer profit ($0.94/W), indirect corporate costs ($0.47/W), supply chain costs ($0.42/W), and transaction costs ($0.36/W). For large commercial systems, the largest soft costs were indirect corporate costs ($0.47/W), supply chain costs ($0.42/W), and transaction costs ($0.33/W).

    In the United States, residential photovoltaic (PV) hardware costs declined from approximately $3.30/W in 2010 to $1.83/W in 2012 (in 2010 dollars), with wholesale module prices widely reported at well under $1/W. Over the same period, the capacity-weighted average of residential U.S. PV system prices declined from $6.60/W to $5.02/W in 2010 dollars (Barbose et al. 2013). Thus, similar to the period of 2005–2010, non-hardware balance-of-system (BOS) costs—often referred to as “business process” or “soft” costs—continued to account for an increasing portion of average installed residential PV system prices in the United States: from approximately 50% ($3.30/W) of total installed price in 2010 to approximately 64% ($3.19/W) in 2012. Commercial PV system costs followed similar trends.

    The U.S. Department of Energy’s (DOE’s) SunShot Initiative aims to reduce the installed-system price contribution of all soft costs to approximately $0.65/W for residential PV systems and $0.44/W for commercial systems by 2020, in 2010 dollars (DOE 2012). This is the second report in the National Renewable Energy Laboratory’s (NREL’s) benchmarking series established to track and analyze the rapidly evolving price structures of PV systems, with a particular focus on soft costs.

    A number of previous analyses have examined non-module PV system hardware costs, including the costs of power electronics and other BOS hardware elements. Several other analysts have examined soft costs, which include permitting and commissioning, profit, overhead, installation labor, customer acquisition, and financing. Few have attempted to understand what has sometimes been called the “other” soft-cost category. Our analysis provides current benchmarks for a fuller scope of residential and commercial PV system costs. Unlike hardware costs, which can be benchmarked readily with data 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 needed to complete the various stages of a PV system sale and installation. To accomplish this, we fielded our second annual survey of U.S. PV installers, collecting data on labor hours required per installation as well as aggregate expenditures for customer acquisition and system design. As in our first benchmarking study (Ardani 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. Our survey data and analysis focus on soft costs related to (1) customer acquisition and system design and (2) permitting, inspection, and interconnection (PII). In conjunction with our soft-cost modeling, we are able to unpack the “other soft cost” category by attributing all soft costs to specific categories.

    Concurrent with our installer survey, we conducted a series of in-depth interviews with members of financing departments at large PV installation companies on the subjects of third-party financing and overhead costs, and we collected data from industry participants’ corporate public filings. These interviewees vetted a first-of-its-kind, bottom-up model that attempts to capture previously opaque cost values. This includes the first effort to articulate overhead that can be layered across the downstream value chain, including costs associated with bundling systems for investors. The model was developed for both residential and commercial third-party-owned installations. The remainder of this report is structured as follows. Section 2 briefly describes the existing soft-cost literature. Section 3 describes our survey and analysis methodology. Section 4 describes the residential PV system data collection and results, and Section 5 does the same for commercial PV systems. Section 6 goes into depth on costs related to third-party financing and overhead, and Section 7 discusses the study’s limitations. Section 8 summarizes the soft-cost reductions during the period between the two benchmarking studies (2010 and 2012 benchmarks) as well as the differences and similarities between the two datasets. Finally, Section 9 draws conclusions and outlines areas for potential future work. Appendix A contains our installer survey instrument. Throughout the report, all values are in 2012 dollars unless otherwise specified; in particular, Section 8 compares the 2010 and 2012 analyses and the SunShot targets in 2010 dollars…

    Comparison of 2010 and 2012 Benchmarks

    This section compares the 2012 results presented in this report to the results presented in our survey of 2010 PV soft costs (Ardani et al. 2012) and to the SunShot soft-cost targets (DOE 2012). To make this comparison, we converted the 2012 results (in 2012 dollars) to 2010 dollars. Thus the 2012 values presented here in 2010 dollars are different than those presented elsewhere in the report. We present the 2010 survey results and SunShot targets in their original, 2010-dollar values.

    Several methodological differences between the 2010 and 2012 surveys affect the results, including modifications to the 2010 survey instrument—made principally to reduce the response time required of installers—and correction for potential double-counting and/or definitional inconsistencies (see Study Limitations, Section 7). First, with respect to survey modifications, the 2012 survey did not include questions on installation labor that were included in the 2010 survey. Instead, an efficiency improvement assumption of 7% was applied to the 2010 installation labor cost benchmarks. As a result, inflation-adjusted installation labor amounted to $0.53/W for residential installations in 2012 (compared with $0.59/W in 2010), $0.38/W for small commercial installations (compared with $0.42/W in 2010), and $0.16/W for large commercial installations (compared with $0.18/W in 2010). Second, whereas the 2010 survey distinguished between marketing and advertising and “all other customer-acquisition costs,” the 2012 survey did not. Rather, the 2012 survey asked for each sector (residential and commercial), “What was the total cost of customer-acquisition activities in Q1 and Q2 of 2012? (including marketing and advertising, sales calls, site visits, travel time to and from the site, contract negotiation with system host/owner, and bid preparation, but excluding system design),” from which a $/W value was calculated. As with the 2010 analysis, 2012 system design costs were examined separately. However, for both 2010 and 2012, all these costs are rolled up into a single customer- acquisition category in this section. Finally, the transaction costs, indirect corporate costs, and installer/developer profit modeling we performed for 2012 is new; along with the sales tax and supply chain cost values from Feldman et al. (2013a), this modeling enabled us to attribute all soft costs to specific categories in 2012, rather than leaving a large “other” soft-cost category as in 2010.

    For reference, Figure 15 shows total PV system costs in 2010 and 2012, separated into hardware costs and soft costs, compared with the 2020 SunShot targets. As system costs declined between 2010 and 2012, the proportion of total costs attributable to soft costs increased.

    Soft-cost details are presented in Figure 16 (residential), Figure 17 (small commercial), and Figure 18 (large commercial). In each case, the proportion of total soft costs categorized as “other” was eliminated between 2010 and 2012 owing to the new cost data, analysis, and categorization in 2012. Aside from those new values, customer-acquisition costs showed a large change for residential PV systems, declining 31% between 2010 and 2012. Customer-acquisition costs for small commercial systems also decreased substantially (32%), but the largest change in previously categorized costs was an 80% reduction in the assumed permitting fee. The previously categorized costs for large commercial systems remained largely unchanged.

    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 and model- driven analysis of soft costs in the areas of customer acquisition, financing, PII, and installation for the first half of 2012. This work continues the effort started with Ardani et al. (2012) to 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 detailed in this report constitute a significant portion of total installed PV system prices. The soft costs we characterized—which accounted for all soft costs -- constituted 64% of the total residential system price, 57% of the small commercial system price, and 52% of the large commercial system price in the first half of 2012.

    Clearly, economies of scale help reduce these soft costs, particularly when comparing residential and small commercial systems with large commercial systems. Among the individual soft-cost categories we characterized, supply chain costs, indirect corporate costs, transaction costs, and installer/developer profit are dominant contributors, followed by installation labor, sales tax, and customer acquisition. With some form of sales tax exemption for solar PV equipment in place in 20 U.S. states plus Puerto Rico (DSIRE 2013), sales tax represents an area of potentially straightforward cost reductions for the remaining states. PII contributes relatively little cost when measured in terms of dollars per watt but presents a qualitative market barrier that can deter project completion entirely. Our analysis suggests that customer acquisition and PII costs decreased in general from 2010 to 2012.

    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, in 2010 dollars (DOE 2012).26 The soft costs we characterized (including assumed permitting fees) contribute $3.19/W for residential systems, $2.90/W for small commercial systems, and $2.02/W for large commercial systems, in 2010 dollars. Soft costs for residential and large commercial systems declined in the United States between 2010 and 2012, while small commercial soft costs increased. Because this second benchmarking effort characterized all PV soft costs—which the previous edition did not—it represents a step forward in measuring progress toward the SunShot soft-cost-reduction targets. This improvement resulted from capturing transaction costs, indirect corporate costs, and installer/developer profit using our newly developed model along with the sales tax and supply chain cost values from Feldman et al. (2013a). Improving our model and our understanding of related costs will be a focus of future work.

    In general, our future work will continue to improve the accuracy and comprehensiveness of PV soft-cost analysis—providing data-derived metrics in support of private and public soft-cost-reduction efforts. For example, more work is required to distinguish the cost of goods sold from operating margins. In addition, 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 cost-reduction potential of solutions, such as web-based dissemination of third-party-verified PV consumer data and remote PV site assessment.

    Strategies for reducing installation-labor costs are also 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. Understanding the cost of system operations and maintenance over its lifetime is another area for future research, not only from an installed cost perspective but also from an LCOE perspective. Interconnection delays represent yet another significant area where true costs may not be fully understood.

    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 soft-cost 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 series of benchmarking reports are a step toward the more detailed understanding of PV soft costs required to track and accelerate these price reductions.

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