kWh Analytics, along with Munich Re and MUFG, have just closed on a groundbreaking new structure - the Wind Proxy Hedge structured with the kWh Analytics “Indifference Structure” - for a 59 MW wind project in Maine, developed by Greenbacker Capital Management. Notably, the Wind Proxy Hedge creates a floor on revenues tied to a lack of wind resource and provides lenders with additional cash flows when considering downside debt sizing cases.
Jason Kaminsky, CEO of kWh Analytics, joins Norton Rose Fulbright and the Currents podcast to discuss this year’s Solar Risk Assessment report published by kWh. He summarizes the findings in the report, including risks to the battery energy storage industry. Additionally, for the first time, kWh Analytics has released a full audiobook version of the 2024 Solar Risk Assessment.
Our COO, Jason Kaminsky, joined Clean Capital for an episode of Expert’s Only. He discusses the next phase of solar and best practices to use data to better manage assets and ensure predictable revenue. Jason and Jon spoke about the next phase of solar and the cutting edge advancements they’re making in data management.
“Our team enjoys working closely with kWh, as they’re a market leader in solar risk management, and we’re thrilled to welcome Jason to the show.” - Jon Powers, Clean Capital
Originally posted on Renewable Energy World.
Our understanding of solar asset performance is changing. kWh Analytics recently published the 2020 Solar Generation Index (“SGI”), an industry-wide validation study, that found operating assets are underperforming by an average of 6.3% as compared to their P50s, on a weather-adjusted basis. Jason Kaminsky, the Chief Operating Officer at kWh Analytics, had the opportunity to discuss these findings and how the industry is adapting with leaders at Arevon, Clean Capital, VDE Americas, and Solargis during this year’s Solar Asset Management North America (SAMNA) virtual conference. Here are three key takeaways from asset managers, owner’s engineers, and weather satellite companies:
1. The “Swinging Pendulum” of Performance Estimates
While the 6.3% underperformance results from the SGI were startling, no one on the panel was surprised. The panelists described maturation of solar coinciding with a gradual shift toward aggressive production assumptions. It was agreed that if you looked at projects five to ten years ago, it was common for them to outperform their production estimates. In contrast, Anand Narayanan, Vice President of Asset Management at Arevon, noted that in today’s competitive landscape with pressure on margins and new modeling complexities, assets are challenged to perform above their P50. He advocates that additional scrutiny of production assumptions is necessary to truly understand performance limitations and the probability of meeting P50 estimates.
When asked to diagnose the reason for this, Brian Grenko, Vice President at VDE Americas, attributed this swing as a gradual change in the assumptions used in technical and financial modeling that results in “death by a thousand cuts.” Grenko summarized it best when he said that today’s P50s represent expectations only “when everything goes as planned.”
2. It’s All About the Data
Data is key to managing solar P50 estimates. In addition to the macro trends identified in reports like the SGI, panelists also discussed the value of site and portfolio-specific data to improve underwriting and diagnose underperformance issues. This starts from understanding the input data to P50 modeling.
Narayanan emphasized the value of leveraging Arevon’s existing operating fleet to support diligence: “As the largest owner of solar assets in California, Arevon has access to generation and weather data to compare performance numbers and underlying assumptions.” This information ensures asset management is comfortable with the underwriting before they manage the asset. Kaminsky added that the kWh Analytics Solar Technology Asset Risk (STAR) Comps reports provide generation and weather data for projects across the country, and these reports are used primarily for asset due diligence and asset management.
Once acquired and operating, the focus shifts to monitoring plant performance and having the right tools to diagnose drivers of underperformance. An all too familiar goose chase in asset management is verifying the impact of weather.
Zoe Berkery, Head of Asset Management at CleanCapital, shared that she’s seen inconsistencies when comparing on-site pyranometer results to a weather file used by the IE at the inception of the project. “On-site pyranometers can be very expensive to upkeep and are sensitive to soiling,” she added, “Which has led the team to explore satellite-based weather options. The updated approach has led to more consistent results across projects and reduces variability.” Giridaran Srinivasan, Business Consultant at Solargis, concurred that ground-based readings suffer from several data quality challenges including “data logging issues, calibration errors, and lack of sensor cleaning.” Kaminsky added that one way to address these challenges is by using satellite data run at scale through production modeling software.
3. The Evolving Role of Asset Management Teams
With growing scrutiny of underwriting accuracy, it is unsurprising that asset management teams are playing a larger role in the project development lifecycle. Narayanan and Berkery both confirmed that their asset management teams are pulled in earlier and earlier to evaluate production assumptions. Narayanan explained that this is driven by his team’s access and understanding of plant data: “We are looking at plants on a daily basis and can identify the factors that have not been modelled properly and make sure those are taken into account in diligence.”
Solar is a maturing asset class with another trillion dollars to put to work over the next six years. As an industry, we have the tools to course correct the systemic miscalculation of solar generation and guide the evolution of solar. A combination of objective market data, analysis with industry benchmarks, and coordinated effort will be paramount to accurately diligence and manage the industry’s growing solar fleet.
Full article available on Solar Builder.
“In large-scale solar, every penny counts, so it helps to stay up to date with every way to achieve better construction efficiencies, cost savings or improved LCOE. Here are some innovations and ideas that caught our eye this month.
STAR Comps to avoid Overcomps
Solar risk management firm kWh Analytics collaborated with 10 of the top 15 solar asset owners on a huge industry-wide energy validation study, the 2020 Solar Generation Index, analyzing over 30 percent of non-residential PV systems in the U.S. On average, systems underperformed their initial estimates by 6.3% on a weather-adjusted basis. Not great! The report concluded that performance estimates are systemically over-estimated. In parallel, kWh Analytics launched Solar Technology Asset Risk (STAR) Comps reports with leading sponsors and asset owners, including New Energy Solar and Captona. These will use industry data to validate solar production estimates on more than 1 GW of solar assets. Equipped with objective data and comparables through STAR, the solar industry can course correct and improve accuracy and certainty of its investment returns.”
Full article available on Reuters.
“Underperformance of solar assets is occurring far more often than expected and cost pressures and widening technology options will increase the challenge, industry experts said.
A recent report by a group of data and measurement specialists has highlighted the impact of solar asset underperformance on returns.
Report leader kWh analytics found P90 production levels are occurring in more than one out of three years, rather than the expected one-in-10 years. P90 production levels reduce equity cash yields by 50%, it said. The study covered 20% of the US operational fleet.
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Originally posted on Solar Builder.
Every mature asset class requires market data to improve the accuracy and certainty of investment returns, and solar is getting there, but still needs work. To address this need, solar risk management firm kWh Analytics announced two new initiatives. First, it collaborated with 10 of the top 15 solar asset owners on the “2020 Solar Generation Index” (SGI), the largest industry-wide energy validation study. The report analyzed over 30% of the market’s non-residential systems in the U.S. and found that on average, systems underperformed their initial estimates by 6.3% on a weather-adjusted basis. The report concluded that performance estimates are systemically over-estimated and that assets are often not yielding the expected returns.
“Although underperformance impacts multiple stakeholders, the long-term equity investors are the most exposed to inaccurate energy forecasts. Change won’t happen on its own. It is up to us as an industry to collectively allow hard data to overcome opinions, however well-intended,” said kWh Analytics CEO and Founder Richard Matsui. “We look forward to the shared work of improving our solar industry and accelerating the clean energy transition.”
In parallel, it issued the industry’s first Solar Technology Asset Risk (STAR) Comps reports with leading sponsors and asset owners, including New Energy Solar and Captona, to use industry data to validate solar production estimates on more than 1 GW of solar assets.
The STAR Comps reports are meant to provide an objective standard to assess solar performance for solar asset investors. STAR Comps leverages the industry’s largest database of solar performance to validate or invalidate performance estimates and loss assumptions for similarly designed systems. The STAR Comps report supports deal teams by improving efficiency and accuracy of asset diligence for projects under construction or under consideration for M&A. It also provides asset managers with context on asset performance to identify addressable versus exogenous performance issues.
“The STAR products are an innovative set of tools that combine analytics and industry data to offer unique insight into our systems’ performance. Our asset management team can now validate and contextualize what we see in the field with industry metrics and more accurate weather analytics to inform our O&M strategies,” said Paul Whitacre, Director of Asset Management at New Energy Solar Manager.
Equipped with objective data and comparables through STAR, the solar industry can course correct and improve accuracy and certainty of its investment returns.
“kWh Analytics has data on production results that were previously ‘best guess’ estimates. It was only a matter of time that we began using market data to validate those numbers,” said Captona Founder and Partner Izzet Bensusan. “The STAR Comps product helps bridge the gap between the Independent Engineer reports and actual performance of projects and provides insight into what we can expect as the future owner and operator of a project.”
Originally posted in Norton Rose Fulbright’s Project Finance Newswire.
The solar industry has anecdotally begun raising concerns about whether solar power plants are underperforming compared to their P50 output forecasts.
What began as hushed conversations at industry conferences is now widely discussed and analyzed. Individual engineering firms and asset owners are beginning to review their portfolios to assess whether or not their original P50 forecasts were accurate.
DNV GL published a piece in the annual “Solar Risk Assessment” report identifying a 3% to 5% overestimation bias in P50 forecasts, even after adjusting for weather. NextEra published a technical discovery around biases in hourly-resolution energy predictions that overestimate solar resource availability. Behind closed doors, asset owners will also acknowledge struggles to hit P50 figures as consistently as the definition attributes.
Under a P50 forecast, a project is supposed to have a 50% chance of performing at least as forecast. This figure is the base case for the project and is generally the most optimistic projection used in financings. Financiers also run sensitivities by looking at other forecasts — for example, P99 and P90 — as well. A project should have a 99% chance of performing at least at the P99 forecast, if not better.
Generating a production estimate integrates weather forecasting and equipment performance expectations into complex physics models. As with any technical model, results vary based on the assumptions used.
kWh Analytics collaborated with 10 of the top 15 asset owners in the United States to conduct the industry’s largest cross-sectional energy validation study, quantifying the accuracy — or inaccuracy — of solar projects’ P50 estimate. We looked at data from 30% of the operating utility-scale and distributed solar capacity. The results are reported in an inaugural “2020 Solar Generation Index” report.
Projects on average underperformed by 6.3%, even after adjusting for weather.
This means that actual performance of the US solar fleet is closer to P90 expectations than the P50 definition used by project stakeholders.
It is important to note that while 6.3% underperformance is the average, there is a wide distribution that highlights significant variability among projects. In the bottom quartile, projects are falling more than 10% below forecast while the top quartile performers are meeting their P50 expectations. As a result, we can see that each project is indeed unique, even if the general trend points towards a 6.3% bias.
The issue of energy estimation is not unique to solar. The wind industry similarly struggled to align lenders, owners and operators on expectations around energy output and is still developing tools to address accuracy and biases.
If unaddressed for solar, systemic asset underperformance can have serious implications for the equity holder cash flows, investor returns and the long-term financeability and credibility of solar as an asset class.
The impacts are discernible from day 1 of operation.
For an equity investor or sponsor who sits last in line behind the tax equity and debt, P90 performance realities mean equity cash yields are cut in half for the life of the asset. For lenders, given the prevalence of P90 scenarios, underproduction poses a risk to debt coverage.
As a risk management company that enables insurers to provide all-risk production coverage to solar assets, kWh Analytics is also observing this trend firsthand through claims against a “solar revenue put” product that actual output will be at least at a guaranteed level. (For more information about solar revenue puts, see “New product: solar revenue puts” in the October 2016 NewsWire.)
To date, insurers have continued to pay all claims in full within 30 days and remain committed to providing sponsors with credit-enhancing insurance products.
However, if unaddressed, inaccurate production estimates and return uncertainty will have long-term consequences for the solar industry.
Every major asset class leverages market data to improve the accuracy and certainty of investment returns. If we look at other mature asset classes like consumer credit or mortgages, companies like Experian and CoreLogic exist to provide market data to validate asset performance and modeling assumptions for investors. Solar is at an inflection point now where we have more than a decade of asset performance data that can be leveraged to inform diligence and improve operating assumptions.
kWh Analytics is using its industry database to offer objective market comparables to evaluate expected yield and performance estimates for pre-construction and operating plants. This new offering, the Solar Technology Asset Risk (STAR) Comparables Report, equips deal teams with historic performance of similar plants to help evaluate performance and financial risk of their projects. In addition, it has helped asset management teams contextualize their portfolio’s performance against projects in the field to improve O&M and asset management strategies.
The solar industry has generated the data required to improve the forecasts. The next step is to leverage that data in investment decisions.
Originally posted on S&P Platts.
A study of approximately 30% of utility-scale and commercial and industrial solar generation operating in the US between 2016 and 2019 showed the average system underperforming by 6.3% compared to what the performance was expected to be during financing, according to solar risk management company kWh Analytics.
The San Francisco-based consulting firm noted that reliable production forecasts "are the cornerstone to solar financeability." The report said 30% of the fleet that was studied, "a quarter of the projects missed their production targets by over 10%, even after weather-adjustment."
"Fundamentally, it is our hope that this report serves as a platform to discuss the data-driven approaches to inform deployment of capital,"said Richard Matsui, CEO of kWh Analytics, in a statement accompanying the report.
The kWh Analytics report is titled "2020 Solar Generation Index," and was released Oct. 5. It noted that every asset class is governed by market cycles and modeling assumptions, and those assumptions "naturally swing between optimism and conservatism."
There is "optimism that naturally emerges from market growth [that] can inadvertently undermine the long-term stability of the industry as a whole."
The solar asset class has now generated a decade of actual data, kWh Analytics said, that can be used to guide sustainable growth, and some of the data "affirms positive attributes."
But some of the data also reveals "that we have collectively turned a blind eye to realities of solar asset performance," the report said.
The consulting firm said its study was a "coordinated initiative" with 10 of the 15 largest asset owners in the US.
"Combining the contributed data and kWh Analytics' HelioStats database, this analysis encompassed over 30% of the industry's non-residential solar projects across more than 30 different asset owners," the report said.
It said that all projects analyzed were larger than 1 MW in DC capacity.
The analysis compared data relative to P50 expectations. The P50 estimate is a statistical measure that indicates the base case of predicted energy yield.
"P50 expectations were degraded annually based on the annual degradation factor assumed by the asset owner," the report said. "The results do not incorporate system losses due to utility curtailment."
Stakeholders in a project can be "financially motivated" to increase production estimates, as it is directly correlated to the amount of capital that can be raised. Moreover, developers may be concerned about the near-term impact on solar asset valuations resulting from adjusted P50 estimates.
The analytics group said it believes a "course correction" will enable the solar industry "to continue the structural trend of lower-cost capital entering into the industry."
The report contends that most in the solar industry have used the same software tools and engineering firms to generate production estimates.
"The lack of accepted standards means that production forecasts can vary dramatically depending on who is running the model," it said.
Until now, data has been unavailable to validate production forecasts with real-world operating data at scale, the report said.
"This research highlights the need to bring this real-world data into the project evaluation process to meet investment return expectations," Matsui said.
Predicting energy yields for generation sites did not begin with solar: other generating assets like natural gas and wind also rely on modeling by engineering firms to assess energy yield potential.
"When lenders began doing their due diligence on solar projects, they often borrowed the same 'playbook' to model their exposure and return expectations off of predicted production estimates," Hao Shen, head of data products at kWh Analytics, said in an interview Oct. 7.
"What started as the concerns of a few asset managers has evolved to be the topic du jour at conferences and in the boardroom," Shen contends. "However, these conversations to date have largely been based on anecdotal evidence from single projects or portfolios."
Originally posted on pv magazine USA and pv magazine International.
kWh Analytics is introducing a new tool to address the solar industry’s systemic overestimation bias and accelerate the adoption of a more data-driven approach to arriving at production estimates.
Finding a way to address and correct the industry-wide bias toward optimistic performance expectations is important because accurate production estimates are a key ingredient to the financeability and growth of the sector, said Hao Shen, director and head of data products at kWh Analytics.
On a weather-adjusted basis, solar assets underperformed their target production on average by 6.3% between 2016 and 2019, according to a recent report by kWh Analytics. One-quarter of projects studied by the company missed their production targets by more than 10%, after accounting for weather.
Production estimates factor into the market valuation of a solar system and the financial models underpinning a solar power plant’s economics, but until now asset owners have not had access to data that could contextualize their portfolio’s performance, kWh Analytics said.
According to Shen, the use of market data to improve the financeability of an asset is an inevitable step in the maturation of the asset class, and kWh’s Solar Technology Asset Report (STAR) Comp took aims to address this gap for solar.
Using its STAR Comps, asset owners and investors can input their metadata and receive objective performance yield, weather and loss assumption metrics reports that show how a solar asset stacks up relative to a peer set of comparable industry systems, Shen said.
The solar industry needs these types of risk management tools now because its continued success depends on its ability to reliably deliver the results that it promises to investors, he added.
“The use of objective market data will force accuracy,” said Jigar Shah, co-founder and president of Generate Capital.
In June, kWh Analytics likened the solar industry’s bias toward overly optimistic pricing to the big three credit rating agencies’ pre-financial crisis, saying that the independent engineers that are hired by solar developers to give solar production estimates have an inherent profit motive for giving aggressive projections. At that time, it said that investors needed to take a step back and adjust to the reality that unreliable energy estimates have been baked into projections.
Available on Norton Rose Fulbright.
“In Episode 123, Richard Matsui, CEO and founder of kWh Analytics, joins us to unpack a new kWh study that found US utility-scale solar projects are underperforming P50 production estimates on average by 6.3%.”
Originally posted on BusinessWire.
SAN FRANCISCO – kWh Analytics, the market leader in solar risk management, today released the “2020 Solar Generation Index” (SGI) in collaboration with ten of the industry’s fifteen largest solar asset owners. In parallel, the company announced that it issued the industry’s first Solar Technology Asset Risk (STAR) Comps reports with leading sponsors and asset owners, including New Energy Solar and Captona, to use industry data to validate solar production estimates on more than 1GW of solar assets.
The 2020 SGI report is the largest industry-wide energy validation study. The report analyzed over 30% of the market’s non-residential systems in the U.S. and found that on average, systems underperformed their initial estimates by 6.3% on a weather-adjusted basis. The report concluded that performance estimates are systemically over-estimated and that assets are often not yielding the expected returns.
Every mature asset class requires market data to improve the accuracy and certainty of investment returns. To address this need, kWh Analytics released the STAR Comps reports to provide an objective standard to assess solar performance for solar asset investors.
STAR Comps leverages the industry’s largest database of solar performance to validate or invalidate performance estimates and loss assumptions for similarly designed systems. The STAR Comps report supports deal teams by improving efficiency and accuracy of asset diligence for projects under construction or under consideration for M&A. It also provides asset managers with context on asset performance to identify addressable versus exogenous performance issues.
“The STAR products are an innovative set of tools that combine analytics and industry data to offer unique insight into our systems' performance. Our asset management team can now validate and contextualize what we see in the field with industry metrics and more accurate weather analytics to inform our O&M strategies,” said Paul Whitacre, Director of Asset Management at New Energy Solar Manager.
“kWh Analytics has data on production results that were previously 'best guess' estimates. It was only a matter of time that we began using market data to validate those numbers,” said Captona Founder and Partner Izzet Bensusan. “The STAR Comps product helps bridge the gap between the Independent Engineer reports and actual performance of projects and provides insight into what we can expect as the future owner and operator of a project.”
“Although underperformance impacts multiple stakeholders, the long-term equity investors are the most exposed to inaccurate energy forecasts. Change won’t happen on its own. It is up to us as an industry to collectively allow hard data to overcome opinions, however well-intended,” said kWh Analytics CEO and Founder Richard Matsui. “We look forward to the shared work of improving our solar industry and accelerating the clean energy transition.”
Equipped with objective data and comparables through STAR, the solar industry can course correct and improve accuracy and certainty of its investment returns.
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Sarah Matsui
About Solar Technology Asset Risk (STAR) Comps
kWh Analytics leverages the industry’s largest database of solar assets (>30% of the U.S. installed base) to develop representative market comps and objective metrics to benchmark system performance, weather factors, and underlying loss assumptions against your development or operating asset.
About kWh Analytics
kWh Analytics is the market leader in solar risk management. By leveraging the most comprehensive performance database of solar projects in the United States (30% of the U.S. market) and the strength of the global insurance markets, kWh Analytics’ customers are able to minimize risk and increase equity returns of their projects or portfolios. kWh Analytics also provides HelioStats risk management software to leading project finance investors in the solar market. kWh Analytics is backed by private venture capital and the US Department of Energy.
About New Energy Solar
New Energy Solar was established in November 2015 to invest in a diversified portfolio of solar assets across the globe and help investors benefit from the global shift to renewable energy. The Business acquires large scale solar power plants with long term contracted power purchase agreements. In addition to attractive financial returns, this strategy generates significant positive environmental impacts for investors. Since establishment, New Energy Solar has raised over A$500 million of equity, acquired a portfolio of world-class solar power plants. The Investment Manager, New Energy Solar Manager Pty Ltd, has a deep pipeline of opportunities primarily across the United States and Australia.
About Captona
Captona is a North America-focused investment company specializing in power generation and energy infrastructure assets. The Firm seeks to acquire operating and development assets within the North American power sector and aims to create value through technical and financial restructuring.