REsurety’s CEO, Lee Taylor and Owen Glubiak, VP of Business Development, will attend the Gulf Coast Power Association Spring 2025 Conference from April 14 – 16 in Houston, TX.
The complete conference agenda is available here.
To learn more about REsurety at the GCPA Spring 2025 Conference or to connect with our team, please contact us below.
Return to the event page main menu.
How can I reduce the financial uncertainty of my PPA portfolio at a time when volatility is on the rise? This is one of many questions that was addressed.
In this webinar, REsurety’s Lee Taylor shared insights for navigating the challenging world of buying clean energy, after over a dozen years of helping customers quantify and manage their risks. The session kicked off with Lee explaining the inherent complexity and volatility in renewable energy contracts and the need for understanding the risks involved.
Lee shared specific use cases illustrating the impact of commodity market and weather-driven volatility, and shared the tools available to clean energy buyers seeking more predictable monthly, quarterly, and annual cashflows from their PPA portfolios.
Lee concluded the session by answering live questions from participants.
Watch the recording below.
Return to the events page main menu.
REsurety’s VP of Business Development, Owen Glubiak, attended the ERCOT Market Summit from February 25 – 27 in Austin, TX. Contact us here.
ERCOT Market Summit 2025 will provide a truly remarkable discussion by Texas policy makers, regulators, utility executives, renewable and dispatchable generation developers, financiers and other experts on how the myriad changes will affect risks, rewards, and challenges in the future, and the strategies that leading players are using to capture new opportunities now.
Infocast’s ERCOT Market Summit 2025 will provide an unparalleled deep dive into the impacts of new policies on reliability, power pricing, project development and bankability, and how to best meet the shifting needs of commercial, industrial, and retail customers.
A consummate group of experts and insiders will:
Last year Infocast’s ERCOT Market Summit reached new heights of attendance and audience satisfaction. Attend the ERCOT Market Summit along with 800 of your peers and take advantage of your best opportunity to connect with key players and hear their plans for the upcoming year, while obtaining critical insights into the business environment of the next five years.
Return to the event page main menu.
REsurety’s CEO, Lee Taylor, and Owen Glubiak, VP of Business Development, attended NAEMA East Meets West 2025 from January 28 – 31 in Vail, CO. Contact us.
Since 2011, when Xcel Energy launched the event then known as “Face Plant,” this gathering has become a cornerstone for energy professionals. The North American Energy Markets Association (NAEMA) is proud to continue this legacy with NAEMA’s East Meets West 2025, hosted by Xcel Energy at the Hythe in Vail, Colorado.
Set in the iconic mountain town of Vail, this year’s event combines industry insights with the unique backdrop of a community rich in history and alpine culture. Founded in 1962, Vail was established by World War II veterans from the 10th Mountain Division who trained in the nearby mountains. Drawn by the area’s pristine slopes and scenic beauty, they transformed it into a premier ski destination that would grow into one of North America’s largest ski resorts. Over the decades, Vail has retained its spirit of adventure while expanding to include world-class dining, shopping, and year-round outdoor activities.
The Hythe, a luxury resort inspired by Vail’s skiing legacy, offers an ideal venue with mountain views, après-ski lounges, and an adventure concierge ready to guide guests through the area’s rich landscape. Join NAEMA members and industry leaders from across the U.S. and Canada in Vail to advance NAEMA’s mission of fostering a dynamic and thriving energy marketplace.
Return to the event page main menu.
Renewable energy certificates (RECs) play a crucial role in steering investments toward renewable energy sources like solar, wind, and hydro. However, despite their importance, the current REC market often lacks transparency regarding the actual emissions impact of these certificates. Buyers aiming to reduce their carbon footprint may not fully understand the real-world impact of their REC purchases, leading to inefficiencies in pricing and market adoption.
This webinar, hosted by S&P Global Commodity Insights and REsurety shed light on the current dynamics of the REC market. We introduced the concept of Emissions Adjusted RECs and discussed how integrating emissions data can revolutionize REC markets, enabling more accurate and impactful trading opportunities.
Key Takeaways
If you had previously registered for the webinar, you can access the recording of the webinar below.
Return to the events page main menu.
Watch the recording of our recent webinar that explored the benefits and challenges of carbon matching in global decarbonization efforts. The upcoming revision of the Greenhouse Gas Protocol will redefine the future of clean energy procurement, and voluntary markets are crucial in allowing clean energy customers to continue to drive systemic grid decarbonization. Carbon matching is one strategy available to customers in facilitating reporting claims and driving clean energy projects worldwide.
This webinar delved into the basics of carbon matching and its benefits for customers in financing clean energy projects and reducing emissions, as well as the challenges of implementing this approach.
Speakers:
Return to the events page main menu.
We were excited to return to the VERGE 2024 event in San Jose, CA, on October 29-31. REsurety’s Lee Taylor, Adam Reeve, Owen Glubiak, Christine Donohue, and Sam Peffer were in attendance.
Lee Taylor was also part of a panel, Putting Impacts First: Building a Comprehensive Clean Energy Procurement Strategy, on Wednesday, October 30. Learn more about the panel below.
Title: Putting Impacts First: Building a Comprehensive Clean Energy Procurement Strategy
Description:
Businesses face pressure from investors, competitors, and employees to excel in sustainability when procuring energy. It is easy to get caught up in the rush to meet targets, but this is more than just a numbers game. We are all part of a more significant movement to combat climate change.
Join our panel of thought leaders to delve into research, understand the impact of emissions and particulate matter, and discover best practices to humanize your approach to everything from environmental attribute certificates (EACs) to emissions mitigation across your business to further solidify your environmental strategy.
Location: LL21E, Convention Center
Speakers:
Daniel Howard, CEO, Quantum Energy
Faraz Ahmad, Operations Manager, Amazon
Katie Robinson, Sustainability Strategist, Akamai Technologies
Lee Taylor, CEO, REsurety
Shalini Majumdar, Manager, Sustainability Strategy & Implementation, ENGIE Impact
Tom Harper, Partner – US Energy Market Advisory, Baringa Partners
About the event
VERGE 24 is a three day day climate tech conference that is the center of gravity for professionals catalyzing transformative, profitable change through decarbonizing their operations and supply chains. It is where practitioners doing the hard, ongoing work of integrating climate solutions share their successes, setbacks and insights to smooth the path forward for all.
Join more than 6,000 leaders on October 29-31 in San Jose, CA to discover what it takes to deploy climate tech at scale. Tap into the knowledge, connections and inspiration needed to de-risk your strategies and supercharge your impact alongside other visionaries, experts and innovators leading the way to a regenerative and just future.
Return to the event page main menu.
We were excited to attend the S&P Global Commodity Insights Financing US Power Conference again on October 23-25, 2024, in Houston, TX. REsurety’s Emma Marjollet and Steven Nixon were in attendance.
About the event
The 2024 Financing US Power Conference is set to be an unparalleled gathering of prominent figures and specialists from the power finance and generation sectors. This year, our speakers collectively manage over $8.5 trillion in assets, underscoring the caliber and influence of the insights and discussions you will experience. Join us for engaging conversations, bespoke networking sessions, and invaluable perspectives you won’t find anywhere else.
Return to the event page main menu.
REsurety’s CEO, Lee Taylor and Owen Glubiak, VP of Business Development, attended the Gulf Coast Power Association Fall 2024 Conference from October 1 – 2 in Austin, TX.
Return to the event page main menu.
REsurety attended the ACP Resource & Technology Conference from September 30 – October 2, 2024 in Phoenix, Arizona. REsurety’s Aaron Perry, Director of Analytics Services, joined the panel, The Green Hydrogen Saga on Tuesday, October 1. Sarah Sofia, Senior Research Associate at REsurety, joined the panel, Advancements in Transmission Technology – Focus on GETs on Wednesday, October 2. Learn more about the sessions below.
Title: The Green Hydrogen Saga
Location: Regency C
Speakers:
Session Chair: Marcel Mibus, Apex Clean Energy
Presenter: Dan W. Bernadett, P.E., ArcVera Renewables
Presenter: Kaitlin Brunik, National Renewable Energy Lab
Presenter: Shadi Darvish, DNV
Presenter: Aaron T. Perry, REsurety
Title: Advancements in Transmission Technology – Focus on GETs
Location: Regency C
Speakers:
Session Chair: Sarah Toth, Rocky Mountain Institute
Presenter: Ted I. Block-Rubin, Smart Wires Inc.
Presenter: Sarah Sofia, REsurety
Presenter: Allison Wilkes, LineVision
Register to attend the conference here.
About the event
Join your peers as we build the future of the clean energy industry. Share and discover cutting-edge technological innovations and advancements in the area of renewable energy assessment and performance and reliability improvements at ACP’s Resource & Technology Conference.
Return to the event page main menu.
As the world shifts from a fossil-fuel-driven, high carbon environment to the decarbonized future of tomorrow, Marathon Capital is dedicated to supporting businesses, governments, and non-governmental organizations navigate the risks and challenges associated with their environmental impact.
In this webinar, REsurety’s Blair Allen spoke with Marathon’s Joan Hutchinson about how she helps customers navigate the challenges in valuing wind and solar energy. She drew from experience and explained how she’s helped a variety of customers understand capture prices, capture rates, and the different ways to calculate them. Questions they answered included:
The event was interactive and there was a Q & A session after the presentation. Watch the full recording of the webinar below.
Blair Allen, Director, Customer Success, REsurety
Blair Allen is a clean energy executive dedicated to accelerating the world’s transition to a zero-carbon future. Throughout his 5 year tenure with REsurety, Blair has held various positions across the organization including product management and his current role leading the customer success team.
In his current position, Blair plays a critical role in ensuring that the company’s customers – clean energy buyers, sellers, and investors – are all able to optimize the REsurety platform to meet their financial and sustainability goals. Customers rely on Blair and his team for industry knowledge, technical expertise, and willingness to suggest innovative solutions to overcome customers’ unique challenges.
Prior to joining the REsurety team, Blair worked as a Senior Market Analyst offering price and congestion forecasts to customers with physical or financial risk in MISO.
Blair holds a Bachelor’s degree in Philosophy from Bucknell University, with a minor in Economics
Joan Hutchinson, Managing Director & Co-Head, Offtake Advisory, Marathon Capital
Joan Hutchinson, Managing Director & Co-Head of Offtake Advisory, is based in Marathon Capital’s San Francisco office. Joan advises corporate clients to set and meet sustainability goals, including energy offtake agreements, carbon reduction, and energy transition strategies. She advises developers on physical and financial offtake agreements to support financing and enhance project value. Joan has advised Nestlé, TC Energy, and Tokyo Gas on recent sustainability initiatives.
Joan has over 25 years of experience in North American energy markets and renewable energy project development. Before joining Marathon Capital, Joan was Vice President of Origination & Business Development at Ørsted A/S, where she led the origination of wind, solar, and storage projects resulting in over 1600 MW of transactions in three years. Many of the transactions Joan completed were power purchase agreements with first-time corporate buyers.
Prior to Ørsted, Joan was SVP Origination & Marketing for Ridgeline Energy, responsible for negotiating contracts for the sale of power and renewable energy credits from renewable projects, leading solar development, and the acquisition of wind and solar projects. Joan was Director of Origination at Citigroup Energy and held various origination and trading positions with Powerex, Inc.
Joan received her Bachelor of Electrical Engineering from the University of Victoria in Victoria, B.C., and holds a Series 79 license.
Return to the events page main menu.
REsurety’s Christine Donohue attended the 2024 Smart Energy Decisions Renewable Energy Forum on June 12-14, 2024, in Aventura, Florida. To learn more or get in touch with REsurety, click the button below:
About the forum
The Renewable Energy Forum delivers peer learning, networking, and consultative meetings to energy and sustainability professionals pursuing information and solutions to advance their organization’s renewable energy goals.
Held once a year and produced by Smart Energy Decisions – the first digital resource dedicated to addressing the information needs of large power customers – the Renewable Energy Forum is a valuable step in your emissions reduction journey.
Access the agenda for the event here.
Return to the event page main menu.
REsurety creates the State of the Renewables Market report every quarter to provide readers with data-driven insight into the value and emerging trends of renewable generation in U.S. power markets. We use our domain expertise in power markets, atmospheric science, and renewable offtake to analyze thousands of locations and summarize key findings.
In this webinar, editor Carl Ostridge and lead analyst Devon Lukas discussed the editor’s note, which examined how there’s more than meets the eye when it comes to ERCOT’s record-breaking solar generation. They also unpacked key findings highlighted in the Q1 2024 edition of the report, including recent trends and drivers behind renewable energy value across the U.S.
The session was interactive and there was an extensive Q&A session after the presentation. Watch the recording or read the transcript below.
Carl Ostridge, SVP, Analytics Services
Carl Ostridge has more than 15 years of energy experience, specializing in energy risk management, electricity markets, and renewable energy project performance. Prior to joining REsurety, Mr. Ostridge worked for DNV GL analyzing and improving the accuracy of wind farm energy analyses and developing models to predict wind farm energy output. His extensive industry experience and proven analytical skills support REsurety’s industry-leading tools and expertise in weather-related risk and valuation for renewable energy projects.
Mr. Ostridge holds a Master’s degree in Astrophysics from the University of Exeter in the UK.
Devon Lukas, Senior Analyst
Devon Lukas is a data analyst with experience developing data visualization tools. Before joining REsurety, she conducted undergraduate research on floating offshore wind turbine structures, completed greenhouse gas emission analyses for the Pioneer Valley region of Massachusetts as well as the UMass Mount Ida campus, and developed various computational tools for renewable energy data sources. At REsurety, Devon is part of the pre-trade services team in which she primarily structures and analyzes weather-related risk mitigation contracts for clean energy buyers & sellers.
Devon holds a Bachelor of Science degree with a double major in Physics & Astronomy, and an integrated concentration in Renewable Energy from the University of Massachusetts in Amherst, Massachusetts.
Carl Ostridge: Okay, it looks like we’ve got good numbers here now, so we’re going to get started. So, yeah, thanks everyone for joining. This is the quarterly market report webinar for Q1 of 2024. You have myself, Carl Ostridge, and my colleague, Devon Lukas. We’re going to be presenting today on the content that we had in the market report, as well as some additional content for you all. Before we get started, just a few quick administrative notes. So we are recording the webinar and then we’ll make that recording available to everyone who’s registered shortly after the webinar is completed. If you have questions, there’s the Q&A function in Zoom, so please use that to ask questions as we go along. And then when we get to the end of the content, we’ll cover as much of those and answer as many of them as we can. So I’ll cover a quick introduction in a second. After that, we have two topics. Devon is going to present a deeper dive on recent solar generation records in ERCOT, and then I will talk through some of the trends in wind and solar value that we’re seeing in Q1 of 2024 and some of the underlying drivers.
So first, a quick intro to the webinars and the reports. We create these State of the Renewables Market reports every quarter, and the aim there is to create some data-driven insights that we are seeing in value for renewable energy across the country. So we have domain expertise at REsurety covering power markets, atmospheric science, and renewable energy offtake. And we use that to analyze all of the data that goes into these reports. We’re data geeks, so we use as much data as we can. All of the metrics and all of the calculations that we’re presenting here and in the report use hourly data. So we have hourly generation and price data concurrent with one another to go into the metrics and expose the weather driven trends that are present. All of the data is then curated by our team of experts and then we make it available in the reports, in this webinar, and then also via our software platform.
So now with all of that out of the way, I will hand it off to Devon. She’s going to explain why there’s more than meets the eye when it comes to the recent record-breaking solar output levels in ERCOT.
Devon Lukas: Great, thank you, Carl. So, as Carl mentioned, we spent some time thinking about the record-breaking solar in ERCOT this quarter, and what’s actually going on behind the scenes there. So over the past few years, solar records have been happening a lot more frequently, and it would be pretty hard to ignore the massive amounts of solar build out in ERCOT. In the past five years, the maximum solar output has grown from just one gigawatt in 2019 to over 18 gigawatts in this past March. And that growth has been skyrocketing in 2024 with roughly three and a half gigawatts of output added each year since 2020. And we’re already at five gigawatts of increase from 2023, only through this first quarter of the year. So this, of course, is going to fundamentally change the grid stack and ERCOT and have a meaningful impact on the market and power prices. But there is a bit going on behind the scenes, as this massive increase in development does not always directly translate to actual output. And this causes actual potential output for solar to be missed. So most notably, solar curtailment played a big role here, and we’ll be diving into that over the next few slides.
So there’s multiple drivers behind solar curtailment, and these drivers have to be sort of just right for a solar output record to actually be hit. Firstly, when load is low, there’s less demand and therefore less generators need to be running. But when we pair that with high renewable generation, most of that load is being met and this can lead to low prices and drive curtailment of resources. And then moving along the top row there. Additionally, if projects are bound by transmission constraints, power cannot reach consumers. Even if demand is high, actually, local constraints can still cause curtailment even if power cannot get where it is needed. And with low prices, generators no longer need to keep running. But there is an intricacy here for wind projects eligible for the production tax credit. So these projects will continue to keep generating even at low and negative prices, forcing solar to curtail first, as those projects are often receiving the investment tax credit instead at the time of build, rather than a production based one. So low prices overall cause renewable curtailment, but tax incentives is what causes solar curtailment specifically over wind.
So due to these drivers of curtailment, conditions need to be just right for a new solar record to actually be broken. And we saw these drivers of curtailment in action over the past quarter. So in this plot here, we have hours over the quarter showing curtailment as a function of net load, where net load represents just the total load minus renewable generation. So that’s a function of both wind and solar generation. So the blue dots here represent record breaking hours while the green represent missed ones caused by curtailment. And as you can see here as the net low drops below that 25 gigawatt line, solar generation curtails, as we move along the right axis, towards the right on the x axis. And in contrast, these blue records are able to be broken due to higher net load and minimal renewable curtailment. So for net load to be high and for solar records to be broken, since it’s a function of both wind and solar, wind generation needs to be low, which is something to keep in mind for the next slides as we check out an example of each of these blue and green scenarios.
So first, let’s look at an example of when a solar output record was hit on February 19th. In our plot here, we have generation and load. We have actual solar and uncurtailed solar in the green, and actual wind and uncurtailed wind in the blue, with net load in the gray. So in this case, irradiance potential was high and that was paired with low wind generation, allowing net load to be relatively high here. And this was enough so that net load did not drop below that 25 gigawatt threshold that we were just looking at. And all conditions were met for a record to be broken at 17.2 gigawatts.
So in contrast, a record was missed a few days later on February 24th, and we can see here that the only criteria that was met was the high potential for solar. So the light green line there represents uncurtailed solar, and it would’ve been roughly 300 megawatts higher than the record broken on the 19th had it been hit. And looking a little deeper into what’s going on here, you’ll notice that the wind begins to ramp up in those morning hours where solar begins to stray from its potential as that’s happening, and net load drops as a result. And you’ll also notice a little bit of curtailment for wind – the difference between the lighter and darker blue lines there – in the later half of the day, but much less than solar curtailment, which is driven primarily by those differences in tax credits as mentioned a few slides back. So as I mentioned earlier, wind needs to be low for net load to be high, and we can see that the opposite is true here. So as a result, this potential peak on this day that could have been 300 megawatts higher, was missed.
So another important part of this story is the tax implications that I was referencing earlier behind wind and solar generation behind low prices. And so this plot here shows the percent of generation curtailed for wind and solar over the quarter, where solar often has a much larger percentage curtailed than wind. And as we know now, wind tends to elect for the production tax credit, so therefore they will generate even during negative price scenarios. And we can see the February 24th missed record event was due to almost 20% of potential solar generation being curtailed at that time. So it’ll be interesting to see if solar projects elect for the production tax credit going forward now that the inflation reduction Act allows for that and how that might change the dynamics in the future. But this is sort of a long term scenario that we won’t see show up for a while as this trend is coming out of the recent legislation there.
So bringing back our first plot of solar buildout but now adding in the maximum of uncurtailed solar into the mix as well, we can see the differences between curtailed and uncurtailed records. So over time solar curtailment has grown where solar potential and actual output continue to diverge as prices drop. And at the end of the quarter here, we can see that our record would’ve been roughly 400 megawatts higher, at 19.1 gigawatts, if it wasn’t for the curtailment drivers that we’ve been diving into.
So lastly, looking at the emissions rate side of things, because that’s also a pretty interesting detail, we have a view of Locational Marginal Emissions, or LMEs, as a function of renewable curtailment here. And as we’ve discussed, during periods of high net load, there’s very little renewable curtailment and marginal emissions rates are roughly equivalent to that of natural gas plants. But as net load drops below that threshold and renewables begin to curtail, marginal emissions rates also drop because this curtailment happens when renewables are online, and therefore the overall grid has a lower carbon impact at that time. And this is interesting because this means that these sort of lost emissions impact due to the curtailment and the lost generation is actually relatively small because most of that curtailed energy would’ve been displacing other clean fuels anyways, rather than fossil generators. So as always with power markets, there’s a lot going on behind the headlines of a record-breaking solar output and solar curtailment is something we continue to keep an eye on here at REsurety.
So I’m going to pass off to Carl now to talk about trends in wind and solar generation and value.
Carl: Yeah, thanks, Devon, that was great. Yeah, as Devon said, I’ve got a few slides now looking at overall trends, high level trends, and some of the drivers behind changes in wind and solar value in Q1 2024.
So when we did this same webinar back covering the Q4 2023 period, the story was actually fairly straightforward. On the left hand side here, we see the year over year change in Q4 value for wind across a number of different markets, and we can see that everything is down to a meaningful extent. And most of that was driven by big changes in average power prices. And that in turn was driven by pretty big changes in natural gas prices. When we look now at the right hand side of the slide here, we’re looking at the Q1 year over year changes, and it’s a much more mixed story. We see actually ERCOT West wind value is up 25% year over year. While there’s a big and meaningful move downwards in wind value in California.
Looking at the same thing for solar, again, it was sort of a consistent picture in Q4 with everything being down meaningfully and in relative alignment across the different markets. We see in Q1 on the right hand side, year over year change is pretty flat for many markets when it comes to solar value. And then there’s this, again, large downward movement in solar value in California. And we’re going to dig into that as we move through the next few slides.
So first we’re just going to look at changes in average power prices. So this, again, is showing year over year changes in Q1, comparing 2023 average power prices to 2024. And again, we see ERCOT West average power prices being up almost 20%. That’s being driven by an increase in local load as well as continued challenges around transmission in that part of the grid. It’s keeping prices higher. When we look at California, we see this big 60% drop in power prices. We’ve pulled in here the Henry Hub prices. That’s a good indication of natural gas prices across large parts of the country, but it hasn’t recently been a good indication of natural gas prices in California. Those prices had stayed higher than the national averages after the 2022 peak. Those have now come down and you’ll see that there’s been about a 70% drop in natural gas prices in California. And that’s a big driver of the change in power prices there too.
So the other component that’s driving wind and solar value is the capture rate. This is the ratio of the wind or solar weighted value and those average prices. So it’s an indication of how much of those average prices wind or solar projects are able to capture. And we’re using hub level prices here, that’s something worth keeping in mind. The wind capture rates are fairly stable over time and there’s much less of a seasonal variation compared to what we’ll see on the next slide for solar. But there is one thing that I wanted to point out here, and that’s the highlighted data point in Q1 for 2024. We see for ERCOT West, a reverse in the trend. So there had been a downward trend. Wind capture rates were reducing as more wind was coming online. And now as that solar capacity is increasing, that’s helping to move the high prices out into those shoulder and overnight hours. And that’s helping to now increase wind capture rates in that market.
Now we’re looking at the same chart but for solar capture rates. So we see that bigger seasonal trend with Q2 and Q3 capture rates being much higher than the Q1 and Q4 capture rates. But there’s a couple of points here that are worth drawing attention to. First is in ERCOT West, whereas the wind capture rates had been going up in the last couple of years, we see a continued decline in solar capture rates in ERCOT West. And actually the 2024 Q1 capture rate is the lowest that it has been, not just in Q1, but across all quarters. And that’s coming off the back of the Q4 capture rate for 2023, which was at that point the lowest too. So as more solar is coming onto the grid in that market, that’s reducing the relative value of solar generation. And we’ll see more on that in a couple of slides. Second point I wanted to make on this slide is looking at the capture rates for California. So we’ve already seen that there’s a big drop in the average power prices. We’re also seeing that there’s a big drop in the capture rate as well for solar in California. So on the next slide we will take a closer look at that.
Here we’re looking at the average power prices throughout the day for the entirety of Q1, in both 2023, that’s the blue line, and 2024, that’s the green line. And this is for SP15 in CAISO. The first thing that stands out is this really, really large drop in average power prices. So the green line sits meaningfully below the blue line, and that’s the part of this which is being driven by that drop in natural gas prices. The second thing is that now with prices as low as they are, we see some negative prices. And it’s important to stress that what that means is throughout the entire quarter, the average price during the middle of the day at SP15 was negative. And that obviously has a huge impact on solar capture rate. Solar is operating and generating during times when there are significantly lower or even negative average prices compared to the rest of the day.
Now if we dig into some of those trends that we were seeing in ERCOT, we’re looking at the same equivalent chart that we were just looking at for California, now West Hub in ERCOT. Again, Q1 year over year prices. 2023 is the blue line. 2024 is the green line. So we don’t see that big change on average, they’re in much closer alignment on average, although the 2024 prices are slightly higher. And then what we do see is during the day, we see prices that are in much closer alignment, even perhaps slightly lower, in 2024 compared to 2023. While in those shoulder hours and the nighttime hours, those prices are slightly higher. So again, same reason that this is having an impact in California on capture rates. It’s a similar driver here. Solar generating during the day is capturing those much lower prices relative to the average throughout the rest of the day. That’s also the reason why we’re seeing higher capture rates for wind projects in ERCOT West.
So just to round this out and sort of bring it back to the content that Devon was talking about just now, we’ve added in the net load, the average net load, in Q1 of 2023 and Q1 of 2024 here. And this just helps sort of confirm the hypothesis that the incremental and increased solar capacity and generation on the grid is part of what’s driving these lower prices. We can see that the green dashed line is on average higher than the blue dashed line. So net load was higher in 2024 Q1 than 2023 Q1. But during the middle of the day when there’s now this incremental solar generation on the grid, we’re seeing actually the net load during those hours is pretty closely aligned and maybe even slightly below those 2023 numbers. So when you have a lot of renewables – solar, wind – on the grid, that’s keeping prices low and keeping those solar capture rates low as well.
Okay. So that’s all the content that we have in terms of the presentation for the webinar. We’ll now jump over to some of Q&A. So we have a few questions coming in. Just a reminder to use the Q&A function in Zoom to ask any questions that you have about the content we just covered or anything else in the report.
And to kick us off, I’m going to ask Devon a question that came in about the comment you made related to the different tax credits. So how would we expect solar projects, if they started qualifying and electing for the PTC rather than the ITC, how would we expect that to change some of the dynamics that we were presenting on?
Devon: Yeah, I can take that one. So as I mentioned earlier, solar has been sort of historically confined to the investment tax credit. But with the Inflation Reduction Act, solar projects are now able to opt for production tax credit instead, which has been primarily wind until this time, as solar buildout is increasing. So this would change the curtailment dynamic we see today because both wind and solar generators would likely still generate during negative prices, instead of just wind. And it’s likely that both wind and solar projects would curtail and be more of a function of region and economic dispatch rather than just fuel types. So it would be definitely interesting to see as that future trend potentially happens.
Carl: Perfect. Okay, a few more questions coming in. Thanks for those. So there’s a question here about the metrics that we’re presenting and whether we’re using hub prices or node prices for the wind and solar value metrics.
So that’s a really good question. Everything that we’re presenting here uses hub prices. The intent there is to show the tradable value and across a large region, it’s obviously very important for individual projects how their nodal price compares to that hub price. That nodal price also is in some ways a closer indication of the revenue streams that that project might be receiving, depending on its offtake structures. So yes, everything here is hub, but it’s obvious that using nodal prices is an important metric as well. And that’s something that we’ll take into account maybe in the future reports and webinars as well.
So there was a question about the trend that we’re seeing in when the records are set. So it seems like they grow quickly in January, in the earlier part of the year, and then they sort of flatten out through the summer. And the question was, maybe what’s driving that?
So my thought there is that there’s a desire for those projects, the new projects that are being built, to be online and operational before the summer hits. They want to make the most of those higher prices. They want to be able to capture the maximum value that they can that’s expected to occur during those summer months. And so I would expect that the majority of projects that are being developed, there’s a target, there’s a desire to try and get those online by the end of the year and maybe those start spilling into the first few months of the following year. And then obviously the other component to that is the solar resource is ramping up through those months as well. And so I think a combination of the new projects being commissioned, the solar resource ramping up, is what’s driving the big increase in the maximum output of solar in ERCOT during those months. And then it flattens out during the summer because the speed of new commissioning is probably slowing down and also the solar resource potential starts to flatten out and then obviously decrease later in the year.
So there’s a question here about how batteries may help change and maybe improve the capture rates that we’re seeing in some of these markets where there’s a significant increase in solar and renewable capacity.
So my thoughts there, in terms of what impact that will have and will it help sort of save the capture rates, I think it will. And I think we’re starting to see that in California now. There’s been a lot of media attention and press around some new records being set around battery discharge in some of those markets and that helping to obviously perform energy arbitrage rather than just supporting ancillary markets. And so I think that you have to get to a certain point, there has to be a certain amount of energy arbitrage value for those batteries to start actually behaving in a way that will help those capture rates recover. But I think we’re going to start to see more of that in California, especially because there’s now a requirement to include storage there with new solar development. And then I think further out, but we’ll start to see it in ERCOT as well, the extent to which that shows up and how much storage is showing up compared to the amount of solar that’s still being commissioned and how quickly we will see an impact there I think remains to be seen.
So I think that’s all we have for questions. So I think that just leaves me to say thank you everyone for joining. Thank you to Devon for the great presentation. And we’ll see you next time for the Q2 report and the Q2 webinar. Thanks all.
Devon: Thanks everyone.
Return to the events main menu.
REsurety’s Adam Reeve, Christine Donohue, and Sam Peffer attended the Clean Energy Buyers Association Spring Summit on May 22-24, 2024, in Denver, Colorado.
We also hosted a Conversation Table on Wednesday, May 22 from 3:45 – 5:15 PM. Details about the session are below.
Title: Better Forecasting for PPA and REC Settlements
Description: As the scale of your clean energy procurement grows, how can you better manage the swings in REC production and financial settlements year over year? Budgets that are set using developer-provided 8760s will always be wrong, because they use stale data and are not accounting for the causal relationship between generation and power prices. The reality, of course, is that renewable energy production and power prices are both driven by the weather – and the weather is anything but constant! Learn more about how to more accurately and easily manage portfolio-level forecasts and better prepare for the financial impacts of extreme weather events and changing market conditions in this session.
Host: Adam Reeve
About the summit
CEBA Connect’s Spring Summit is the premier event for energy and sustainability professionals dedicated to advancing a carbon-free energy future – and it is just around the corner. Join the CEBA community as we build common understanding of emerging market and policy trends and advance opportunities to scale clean energy to decarbonize the power sector.
Return to the event page main menu.
REsurety’s Jennifer Newman attended the ESIG 2024 Forecasting & Markets Workshop on June 11-13, 2024, in Salt Lake City, Utah.
On Thursday, June 13, Jennifer spoke on the panels, Bridging the Gap between Atmospheric Science and Grid Modeling, from 8:00 AM – 9:45 AM, and Closing Plenary Session – Panel Discussion of the Issues and Challenges in the Energy Transition, from 10:15 AM – 12:00 PM.
Title: Bridging the Gap between Atmospheric Science and Grid Modeling
Location: Salons EF
Session Chair: Cathy Finley, Associate Professor, University of North Dakota
Speakers:
– Connecting the Dots: Status and Prospects: Justin Sharp, Principal/Owner, Sharply Focused
– Modeling Extreme Events in WECC using PNNL Weather Datasets Including Climate Change Impacts Through 2099: Jon Jensen, Engineer, System Adequacy Planning, WECC
– Status of Climate Change Projections on Wind & Solar Resources: Jared Lee, Project Scientist II, NSF National Center for Atmospheric Research
– Forecasting Tail Events and Impact on Energy Price: Jennifer Newman, Vice President of Atmospheric Science and Research, REsurety
Title: Closing Plenary Session – Panel Discussion of the Issues and Challenges in the Energy Transition
Location: Salons EF
Session Chair: Sue Hapt, Senior Scientist, NSF National Center for Atmospheric Research
Description: There are many barriers that have limited the pace of adoption of carbon-free energy generation well below the baseline trend necessary to meet aggressive decarbonization goals set by governments around the world. Some of these barriers include inadequate replacement or retiring generation with clean resources; unprecedented electrification and resulting load growth; inadequacies in weather and climate models, data and forecasts.
We plan to summarize the current situation and have an interactive discussion with panelists and the audience about what steps can be taken to overcome these barriers. Insights and feedback for ISOs, utilities, government labs, forecasters and researchers will be sought.
Ensuring that the energy system planners and operators and atmospheric science communities have a mutual understanding of each other’s needs is key to meeting society’s decarbonization goals.
Summary Comments from the Power System Perspective (10 min): Paul Sotkiewicz, President, E-Cubed Policy Associates
Summary Comments from the Meteorology Perspective (10 min): John Zack, MESO
Panelists for the Discussion on Overcoming the Barriers:
Facilitated by Sue Haupt, NSF National Center for Atmospheric Research, and Matt Schuerger, Senior Fellow, ESIG
– Paul Sotkiewicz, President, E-Cubed Policy Associates
– John Zack, MESO
– Peter Markussen, Managing Director, Energinet (Denmark) (invited)
– Eric Ela, Technical Executive and Program Manager, EPRI
– Jim Wilczak, Research Meteorologist, NOAA
– Justin Sharp, Principal/Owner, Sharply Focused
– Jennifer Newman, Vice President of Atmospheric Science Research, REsurety
Contact REsurety to learn more here.
Return to the event page main menu.
REsurety’s Lee Taylor joined Gulf Coast Power Association’s panel on April 16th at 3:45 pm. Learn more about the session below.
Session Title: The What and How of Sustainability
Description:
Many corporations have announced significant sustainability goals – including net zero ambitions within the next one to two decades. Are these goals achievable? What are corporations doing to achieve these goals in the near term? Have the products used by corporates evolved as their focus on sustainability has increased? What are third parties doing to facilitate achieving these goals?
Speakers:
Moderator: Kellie Metcalf, Managing Partner, EnCap Investments
Alex Beck, Co-Founder, GoodLynx
Chris Dorow, Regional Category Manager, Power and Utilities, BASF
Tina Moss, Sr. Director Net Zero Strategy & Execution, Americas, LyondellBasell
Lee Taylor, Co-Founder & CEO, REsurety
Return to the event page main menu.
Adam Reeve, REsurety’s SVP of Sales & Customer Success, attended S&P Global Commodity Insights’ Global Power Markets Conference on April 15-17, 2024, in Las Vegas, Nevada.
About the conference
The leading annual event in the global energy sector, the Global Power Markets Conference, is making its much-anticipated return to Las Vegas! This year, we are thrilled to announce an unprecedented number of executive speakers, showcasing the event’s growing prominence and value in 2024. Join us as senior-level participants from across the power market spectrum – including utilities, investment firms, development companies, trading entities, and governmental bodies – convene to engage in pivotal discussions on the most pressing issues and trends driving the future of energy. This is a unique opportunity to network with industry leaders, gain essential insights into the dynamics of today’s power markets, and explore the challenges and opportunities that lie ahead.
Return to the event page main menu.
REsurety creates the State of the Renewables Market report every quarter to provide readers with data-driven insight into the value and emerging trends of renewable generation in U.S. power markets. We use our domain expertise in power markets, atmospheric science, and renewable offtake to analyze thousands of locations and summarize key findings.
In this webinar recording, you’ll hear from the editor of the report, REsurety’s Carl Ostridge, as he discusses the editor’s note and unpacks some of the key findings highlighted in the Q4 2023 edition of the report. There is a Q&A session after the presentation.
Carl Ostridge, SVP, Analytics Services
Carl Ostridge has more than 15 years of energy experience, specializing in energy risk management, electricity markets, and renewable energy project performance. Prior to joining REsurety, Mr. Ostridge worked for DNV GL analyzing and improving the accuracy of wind farm energy analyses and developing models to predict wind farm energy output. His extensive industry experience and proven analytical skills support REsurety’s industry-leading tools and expertise in weather-related risk and valuation for renewable energy projects.
Mr. Ostridge holds a Master’s degree in Astrophysics from the University of Exeter in the UK.
Tara Bartley: Thank you everyone for joining us today. My name is Tara Bartley and I am pleased to introduce Carl Ostridge. He is our SVP of analytics here at REsurety. We recently published our Q4 2023 State of the Renewables Energy Market report. And Carl is here today to share some of the findings, insight, and to answer questions that you might have. Please submit your questions via the Q&A function online and Carl will address your questions at the conclusion of his presentation. I’ll turn it over to you, Carl.
Carl Ostridge: Okay, thanks, Tara. Yeah, this is the first time that we’ve added a webinar onto one of these quarterly reports. So I’m excited that we’re able to add this format to the content as well. So we’ll start with a quick introduction for those who aren’t familiar with the reports. We can give some background there, as well as what we’re trying to achieve with the webinar. Then we’ve got two main topics for the rest of the time. The first is covering the content that’s in the Editor’s Note of the most recent report, and that’s related to the variation that we see in marginal emissions rates for renewable energy projects across the country and some of the implications associated with that. And then the second topic is looking at some other data and some other trends, specifically related to generation and value for wind and solar projects and digging into that in a little bit more detail. Then, as Tara mentioned, we’ll wrap up with some Q&A so please do submit those questions as we’re going through, and we’ll do our best to answer as many of those as we can.
So just a bit of background on the market reports. We’ve been generating these and publishing them for around a year and a half now as a market report. And then we’ve just now added this additional webinar format. And we create these each quarter to provide our customers with data driven insights into value and emerging trends that we’re seeing in renewable energy across the U.S. footprint and in the different ISOs. And we’ve got a team here of experts in power markets, and atmospheric science, and renewable energy offtake to be able to analyze all of the data that we have, and that covers thousands of different locations. All of the analysis that we present here is based on hourly data of price and generation and emissions. And we aggregate that all up and we try to present some insightful and useful content in the reports, and now in these webinars too.
So jumping in to the content here. Like I said, this first topic is covered in the Editor’s Note of the report, and this is related to trends that we’re seeing and variation that we see in marginal emissions data across the country.
We recently were able to complete the coverage of the U.S. for this data set. And so now we have coverage across the country in all of the deregulated ISOs. And so we wanted to summarize what we’re seeing in the data there and highlight some implications. Before we get too deep into the analysis though, I wanted to start with just a bit of background on Locational Marginal Emissions. This is the data set that we’re going to be looking at here. What is that, and how should we think about it? So one of the ways that I find useful to think about Locational Marginal Emissions is that it’s analogous to Locational Marginal Prices, which I expect that most people are familiar with already. So in each hour or each time period, and in each location on the grid, there is a Locational Marginal Emissions rate and that indicates the emissions in CO2 equivalent for the marginal generator during that time in that location. So it varies by time and it varies by location. But it gives you a sense of the emissions that are avoided by adding incremental renewable energy generation in each location at each time. That means that it can be used as an indication and a metric around the carbon impact that those projects have. The other thing to say is that it’s not a REsurety-specific concept. This is an idea that’s supported by multiple parties. There are different marginal emissions data sources available. We highlight some of them here on this slide. PJM publish, as an ISO, their own marginal emissions data for their particular footprint. Our data set, as I mentioned before, now covers the entire U.S. deregulated ISOs and there’s a nodal granularity, but there are also other publishers out there as well.
So on the next slide, we have a couple of examples here to step through that will hopefully solidify an understanding of what we mean by Locational Marginal Emissions and how to think about it with respect to renewable energy projects. So we have this first example, this is for a wind project in central Texas and we have three different charts here. The one on the top represents the generation from the project. I think we have a few weeks of time period covered here. The middle one is the prices, the Locational Marginal Prices, at the project location, and on the bottom we have the Locational Marginal Emissions. And this is a project in a location that isn’t subject to meaningful transmission constraints. I guess that’s an important thing to say, is that the marginal emissions rate is affected by and driven by, in many cases, the transmission congestion and constraints that may exist in each different location. The example we have here, there’s no meaningful transmission constraints. And so what you see is, even as the project generation fluctuates from close to full capacity down to very little output on different days, the marginal emissions rate stays fairly constant and the marginal prices, yes, they go up and down, but there isn’t a strong correlation between project output and the prices.
If we go to the next slide, we see a different scenario. On this one, we see a project that is in a location with meaningful transmission constraints. Now we’re looking at a project on the Gulf Coast of Texas, south of Houston, and this is a region where there are a lot of wind farms and transmission constraints, meaning that when it’s windy, not all of that wind power can reach the load center. And that means that at times, projects might be curtailed. And also that wind can be the marginal fuel in that particular grid region at those times. We see that in the data here. So during this highlighted period in the middle, we see when the project output is high, the nodal prices are now dropping down during that period compared to the hub prices, which is the yellow line behind. And then you can also see multiple periods where the Locational Marginal Emissions rate is dropping too, and dropping down to zero. And that indicates periods where the marginal unit, the marginal generator in this local region, is wind. So hopefully that’s a useful way to explain Locational Marginal Emissions and how to think about it in the context of renewable energy generation and what might drive higher or lower marginal emissions rates and emissions impacts for different projects.
On the next slide here we have a map that was taken from the report. So this is just a nice visualization of all of that data that we have. Each of the points here represents a wind or a solar project that’s operational. And we’ve calculated the generation-weighted emissions rates. So effectively its emissions impact during this 12-month period. And there are lots of different insights that you can see immediately from this map. There’s different regional variation, there’s also projects that sit very close to one another that seem to have quite different emissions impacts. And so we’ll dig into that in the next couple of slides.
This chart now is really just taking that same data from the previous slide and presenting it in a slightly different way. Each point on this chart is still a renewable energy project – wind or solar- and we’ve just grouped them by ISOs. We’ve also stacked up those ISOs in order of emissions impact, with the lowest on the left and the highest on the right, from an average perspective. The boxes that are included for each of these charts, the middle line is the average value of all those projects, and then the top 5% and the bottom 5% values are also included. So there’s a few takeaways here that I want to highlight. The first is that renewable energy projects in CAISO have the lowest emissions impact out of all the projects that we’re looking at here, on average. And that sort of makes sense from an intuitive perspective. CAISO is a grid that has lots of renewable energy already, particularly solar, and so in many areas of that footprint, adding more solar means that you’re often competing with existing solar and therefore not having as much of an emissions impact in terms of displacing thermal generators on the grid. But it’s worth noting the magnitude of the difference. The average in CAISO is less than half of the average impact across a number of other ISOs like MISO, NYISO, and PJM. And even the top 5% of projects in California in that CAISO footprint are displacing less CO2 equivalent than average projects in lots of other ISOs. On the other end of the spectrum, we have PJM. So that’s where the renewable energy projects are having the largest impact. And, again, that makes some sense from an intuitive perspective. PJM is a big market with lots of load and relatively low renewable energy penetration rates and it also has a relatively dirty generator stack compared to some other markets. And so that combination means that incremental wind or solar additions in that grid have a relatively large impact compared to other regions. The final point that I want to highlight here is, yes, there’s lots of variation across the ISOs and between them, but there’s also lots of variation within them. And you look, there’s lots of overlap in the project-specific emissions impact numbers within and across the ISOs. So there’s two things really driving that. One relatively small factor in most markets is technology. So in markets where there’s already a lot of wind or solar installed, the opposite technology tends to have more of an impact and that’s due to the offsetting anti-correlated generation profiles, generally speaking, between wind and solar projects. And that adds some variation. There’s a bigger driver, and we’ll touch on this in the next couple of slides, which is down to transmission. And so within each ISO, there can be meaningful differences and constraints imposed by transmission and that introduces a big difference in the impact that each project can have.
So on this slide, we have an example where we’re zooming in just to one part of that bigger map. So we’re looking at SPP projects and specifically wind. So now we’re removing any technology drivers here that might exist. So just wind projects in SPP, and we’re looking just in the Oklahoma footprint here, just for simplicity. And it’s pretty clear here that there’s a big difference in the relative emissions impact between projects located in the west of that state’s footprint versus the few projects that exist in the south and further east. And that’s being driven by transmission. So all those projects to the west that have lower values, those more red colors in this particular plot, that means that they’re competing with other renewable energy generators more often. And when it’s windy, is the same effect that we were looking at in that earlier example from ERCOT. Curtailment is often happening, and wind is more often the marginal generator in that particular region. Those projects to the south are not constrained by that same transmission issue, and therefore able to more often displace thermal generators on the grid.
The next slide has another similar example but just from a different part of the country. Again, we’re just looking at wind projects here and now in the MISO footprints. These are MISO projects, wind across Iowa and Minnesota. And again, we see this little pocket of red projects that are having a lower emissions impact compared to projects further east that are having a higher emissions impact. And again, it’s the same transmission driver that we were seeing on the previous example as well.
Okay, so just to wrap it up there is one important implication that we wanted to touch on on the next slide. And this is related to a lot of the discussion and debate happening around clean hydrogen tax guidance and the tax credits. So the current guidance from IRS is that an hourly matching framework will be implemented, and that the renewable energy procurement, under the hourly matching regime, will need to come from within the same region as the electrolyzer. Those regions don’t perfectly line up with ISOs but they’re close enough for the purposes of this analysis and this example. And, again, this variation within the ISOs is important, and what that means when we think about the real world implications for this hourly matching framework. There could be a big difference if the electrolyzer is procuring the renewable energy from one project that maybe is having a lower emissions impact, or from another which is having a much higher emissions impact. And so it’ll be interesting to see how that debate and how that process unfolds as we go forward, but this data provides some insight into how those different projects will, in the real world, behave differently.
Okay, so moving on to the second part of the presentation. Just picking out some of the important trends that we’re seeing in the data in a more general sense across a few different markets.
The first thing that it’s almost impossible not to talk about, is the level of solar build out that’s continuing to happen in ERCOT. This chart speaks for itself. Over the last five years, ERCOT has added a bit shy of 14 gigawatts of solar. And it’s added that at a rate of about three and a half gigawatts a year for the last few years. And that’s showing no signs of slowing down. There was a big jump at the start of this year, and so they reached a new record of 15.1 gigawatts of maximum output within the last week or so. And that’s having a big impact on price formation, the fundamentals of the market, but also solar value in ERCOT, and we’ll touch on that over the next few slides as well.
So the next couple of slides, the text here is small, these are lifted from the reports, you can zoom in and look at the specific values in the report itself. The color scheme doesn’t really portray it here, because there’s a lot more dynamic range that we’re not showing in these zoomed in views, but wind, which we’re showing here, and also solar value on the next slide, we’re down 30 to 50%, year over year, Q4 2023 versus Q4 2022. So we’re going to just dive into that in a little bit more detail and see what some of the drivers are there. So this is wind. The next slide shows a similar story for solar. So yeah, we’ll dig into some of the drivers now on the next slide.
So just starting from the simplest view, looking at the average prices, we say ATC here (around the clock), it’s just the simple average of power prices in just a select number of markets here to keep it simple. We’re looking at CAISO, ERCOT, PJM, and SPP. We see that again, somewhere in the same order of magnitude 30 to 50ish percent was the decline in average prices. And so that starts to give you an indication that that’s a big driver behind that drop.
On the next slide, we add in gas prices. So that’s always the first place that I go to when I see a big drop in average prices. It’s probably being driven by natural gas. And we see that’s certainly the case here. Similar magnitude of drop in Henry Hub, in this case, natural gas prices year over year. There was a big run up in natural gas prices in 2022. And then some milder weather and some at least settling into a status quo, geopolitical issues meant that those gas prices have started to come down and that’s had an impact on power prices too.
So on the next slide one other thing that we always look at here is capture rates. So this is the ratio of the generation-weighted value over that simple average value. And it gives you a sense o,f for wind or solar projects, how much of that average value were they able to capture? And there’s a few things that jump out here. We’ve plotted out, so you can see kind of a year over year comparison across the different quarters. The ERCOT line, you see generally there’s a pretty strong downward trend, with a couple of exceptions. The Q3 results was definitely a point that bucks the trend and actually increased in 2023, compared to the year before. And that was driven by a lot of the heat and the extreme weather during the summer. And then the high prices and the volatility that followed. Q4, on the other hand, we saw a big drop year over year, and there’s been a relatively mild winter so far, and that’s definitely contributing to part of that as well, sort of returning to that downward trend.
On the next slide, this is now looking at wind capture rates. And the first thing that I noticed when I look at this is that the trends are much flatter, much more stable compared to those solar capture rates, at least in the markets we’re looking at here. And you can also start to see maybe some indications that the capture rates might be moving up in some places, particularly ERCOT West. Q4, the capture rate moved up year over year, and this is something that we expect to see. It’s not a really strong trend in the observed data yet, but as that solar build out increases, we expect the market and the prices that are now responding to that increased solar build out, it’s going to move some of the higher priced hours out further later in the day into hours that generally have higher wind generation levels. And so we expect some upward movement in wind capture rates over time as that solar build out increases. I highlighted one of the points here for PJM, and we’ll cover that actually on the next slide.
And so there was this very high wind capture rate in Q4 2022 and a much lower value in 2023. And actually, this just points to really having to dig into the hourly data to understand what’s going on. And this is December, 2022 prices real-time from CAISO, ERCOT, and PJM. And you can see that there were different reasons in these markets, why they had high prices. California is the dark blue line there, and you see that, generally speaking, the prices were just high all month. And so that decrease year over year, in average prices was being mostly driven by gas or higher gas prices on the west coast compared to other parts of the country. And that since eased somewhat, and it’s brought the prices down. ERCOT and PJM are a different story. They had relatively normal prices through most of the month. And then there was a weather event and the storm that passed over those regions around the Christmas periods, so towards the end of the month, and you see those high prices that resulted. That obviously drove up the average and also drove up the wind capture rates as well as some solar capture rates as well. So you really do need to understand the hourly data in order to really understand some of the drivers. But just to summarize, I suppose there were three drivers of that drop in wind and solar value. Gas prices dropped by around 50%. There was some weather-driven volatility that we’re addressing here. And then in markets like ERCOT, the changing grid mix, and the continued solar build out is also having an impact too.
So I think that’s all the slides that we have. I see Tara just added a reminder there to submit questions through the form. We have some already coming in. But please do add more as things come to mind. And we’ll jump into Q&A starting now.
Tara: Thank you very much Carl. That was a lot of information and you timed it perfectly. It’s exactly 25 minutes, so we’ll give you a brief break to maybe grab a sip of water. So yes, please submit your questions online. The first one that we always get is: will we get a copy of the presentation? We are recording this session, so we will send you a link to the recording by the end of the day tomorrow. And be sure to let your colleagues know if they weren’t able to attend a live session, they will also receive a copy of the recording.
So the first here is a comment where one viewer says, “I really liked the report, especially where you point out the attractiveness of PJM for renewable projects in comparison with CAISO.” So the first question here is, “How are you seeing customers use your LME data as part of their procurement strategy?”
Carl: Yeah, and there’s definitely a link that we can draw between the comment and the question there. So certainly what we’ve seen, I think there’s two ways that people are using LME data. The question specifically asked about procurement process and procurement strategy so I’ll talk about that first. For energy buyers, they’re able to use that data to understand the emissionality – the emissions impact that those projects will have – and for many and an increasing number, that’s an important factor for them when they’re considering their procurement. And they want that emissionality to be a consideration alongside the economics of the transaction. So it allows the extra dimension to factor into the procurement and to maybe help drive decisions. For some, that’s the primary factor – the emissionality. The Emissions First Partnership is a group of corporate buyers who are driving that as their primary goal. There are others who maybe aren’t part of the Emissions First Partnership, but want to also have an eye on emissionality and maybe use that as a tiebreaker when they have two projects that look very similar, but maybe have different emissions impacts. And then maybe the second part of that is on the developer side, we do see developers using the emissions data as a means of differentiating their projects as part of those RFPs. Being able to get ahead of the questions from the buyers and say the data shows that the project in this location has an above average emissions impact can be a differentiating factor during a competitive RFP process.
Tara: Thank you, Carl. The next question is, “It looks as though all of this data is historical, do you have forecasted data as well, and do you produce similar generation-weighted values?”
Carl: Yes, so all of the data in the report and the webinar so far is historical. But it is important to note that we do have forecasted values as well. Those cover price and also marginal emissions rates as well. And so we are able to forecast out 20 years into the future across all of those deregulated ISOs, and we use a fundamentals-based model to forecast hourly prices across a range of different fundamental scenarios. And then importantly as well, we use our Weather-Smart approach, which allows us to maintain that correlation between the weather, renewable energy generation, and market prices to really give a full range of possible outcomes within those forecasts as well.
Tara: Thank you. The next one is, “How might you consider a portfolio of different projects or technologies for calculating electrolyzer utilization under an hourly matching framework?”
Carl: Yeah, so this is a really good question. And we’ve been working with a number of hydrogen developers on this exact question. And when you’re thinking about an hourly matching framework, the goal for a hydrogen electrolyzer is to try and get as close to constant offset between renewable energy and their consumption and to try and keep that utilization rate for the electrolyzer as high as possible. And so all the hourly data that we have, we have in our Explorer product. We have synthetic projects covering the entire country, we have full modeled hourly generation data for almost all operational projects in the country as well. And so we’re able to combine those, do some analysis on optimization to try and find the right mix of wind and solar generation to maximize that utilization rate of the electrolyzer as well.
Tara: Thank you. The next one is, “You showed wind and solar As-Gen prices declining year over year. Do you expect that to continue? Why or why not?”
Carl: Yeah, so that’s a good question. I think it’s obviously market specific. I suspect the question here is related to some of the content that we had in ERCOT. So, yes, I think our expectation based on that fundamentals modeling that I was talking about earlier, is that at least the capture rates, the relative value of solar compared to the average prices, we expect that to continue declining as more solar is added to the grid because you’re going to see an effect similar to California, where there’s a depression in prices during the middle of the day when solar is generating, and then maybe some increased volatility and increased prices as the morning and evening ramps occur. And that will naturally drive down those solar capture rates. So we do expect that to continue, especially given the rate of increase in solar installations that are happening currently in ERCOT. I guess we should say that we expect maybe the opposite to happen for wind. I mentioned that a few slides ago, but that same effect on prices, we expect to have a positive impact on wind capture rates.
Tara: Okay, I think we’ve got one more here, Carl, and that is, “Beyond implications for 45V green hydrogen, how is marginal emissions data being used by your customers today?”
Carl: Yeah, so we covered some of this earlier in the question around procurement. So we’re definitely seeing that as a big use case, corporate buyers and other buyers looking to understand the emissions impact that their procurement strategy can have. We’re seeing it used by developers to understand how their projects stack up against regional benchmarks from an emissions perspective. And then we work with some storage assets and developers to look at ways to dispatch storage potentially to maximize carbon impact, as well as optimize around prices and the pure economics. So there’s really a number of different ways that that’s being used. Those are some examples that come to the top of my mind right now.
Tara: That’s great. Thank you very much, Carl. It looks like that is all that we have for questions for today, although it was quite a few. Thank you so much for addressing all of those. Here in the chat I just included the link to the Emissions First Partnership that you mentioned, Carl, so folks can take a look there. Again, we’ll send out a recording to this session tomorrow via email, we’ll also include a direct link to the report in case for some reason you haven’t downloaded it yet. Oh, one more question just came in, Carl, can you handle one more?
Carl: I can take one more, yeah.
Tara: All right. “Do you have any predictions for curtailment? Where is it worst? Where will it get worse? And how does curtailment interact with the capture rates?”
Carl: Yeah, so I’m going to try not to give too much of a hand wavy answer to this one because I think if people can predict with lots of certainty where price basis will be bad, or how it will develop in the future, then they’d probably make lots of money if they could predict that perfectly. So we obviously can’t, and that sort of transmission planning and forecasting isn’t our area of expertise. But what I would say is you can look at historical data to understand where those trends have been developing in the recent past. You can also look at interconnection queues to understand if there’s a lot more wind and solar projects expected to be added in a certain location. And that’s obviously what’s driving a lot of this is when you have lots of the same technology type in the same region, and especially if there’s an existing transmission constraints, then transmission just isn’t getting built fast enough for the projects that are being added. And so my assumption there would be that if there’s a lot more projects expected to be built, or with interconnection agreements, then that, all else equal, would cause basis to get worse. So I would not necessarily offer a prediction of the future but encourage an analysis of data in the past and also data that’s available on where projects are being built to inform that.
Tara: Thanks again, Carl. It looks like that wraps it up for our Q&A session here. And on the next slide, you’ll see a couple of options for reaching out to us at [email protected] or to visit our website to learn more. Again, thank you very much, Carl, for sharing all of this data and your insights. And thanks to everyone for joining and this has been a great success. So I might commit Carl to doing this again next quarter if time allows. So look out for emails from us on our next webinar. Thanks, everyone.
Carl: Thanks everyone. Bye.
Return to the events main menu.
REsurety attended the 2024 Solar + Wind Finance & Investment Summit on March 10-13 in Phoenix, AZ. Co-Founder & CEO, Lee Taylor, SVP of Sales & Customer Success, Adam Reeve, and Emma Marjollet, Account Executive, represented REsurety at the show.
Infocast’s Solar + Wind Finance & Investment Summit in 2023 gathered an unprecedented number of leading industry players to network, make deals, and get fully briefed on the renewables markets. This exceptional event is back to once again gather a who’s who for phenomenal deal-making and strategizing opportunities.
Join us for 2024’s summit March 11-13, with an exclusive pre-summit evening of Keynote sessions followed by a welcome reception on March 10.
Mark these dates in your calendar and get ready for the most powerful networking place for top-level renewables and financial executives and capitalize on today’s booming renewables market!
One year into the Inflation Reduction Act and the industry is still figuring out the new normal, with lingering supply issues, inflation, and interest rate hikes on the one hand and expanded tax credits, transferability, and long-term policy certainty on the other. This is a particularly critical year to attend Solar + Wind Finance & Investment Summit for a deep dive into how IRA guidance is affecting assumptions and how the emerging transferability market is creating a whole new outlook on financing possibilities. You’ll get insider tips on how to successfully navigate these waters, capitalize on the booming opportunities of the future, handle the lingering difficulties of the present, and meet everyone you need to know to take maximum advantage of your opportunities to develop, finance, and invest in today’s renewable energy projects.
Mingle with other C-Suite executives at the preeminent networking event for leaders in finance, solar, wind, and storage, top-rated by attendees as the place where deals and valuable business connections are made. Get the latest update on the finance and investment landscape, gain prescient insights into market trends, and network with the “highest quality of industry participants on the conference circuit.”
Bump into all the right people at the premier source of information for shaping your organization’s strategic direction – Let’s reconnect once again!
Return to the event page main menu.
The summer of 2023 felt like an endless chain of heat waves in the U.S. but nowhere more so than in Texas, which experienced its second hottest summer on record. In this webinar, meteorologists Jennifer Newman and Jessica Tomaszewski discussed the meteorological drivers of the heat wave, the temperature and demand records that were set, and the impact the heat wave had on power prices. They discussed:
The session was interactive and there was an extensive Q & A session after the presentation.
Jennifer Newman, VP, Atmospheric Science Research
Jennifer Newman is an atmospheric scientist with experience in boundary layer meteorology, remote sensing, machine learning, and wind resource assessment. Prior to joining REsurety, Dr. Newman completed a postdoctoral fellowship at the National Renewable Energy Laboratory with a focus on improving turbulence estimates from Doppler wind lidars. As a research scientist at REsurety, she investigates new methods for estimating the risk of potential wind projects.
Dr. Newman holds Doctor of Philosophy and Master’s degrees in Meteorology from the University of Oklahoma. She also holds a Bachelor’s degree in Atmospheric Science from Cornell University where she graduated with distinction in research.
Jessica Tomaszewski, Senior Research Scientist
Jessica Tomaszewski is an atmospheric scientist with experience in boundary layer meteorology, numerical weather prediction, and wind resource assessment. Prior to joining REsurety, Jessica completed a National Science Foundation Graduate Research Fellowship with a focus on simulating interactions between wind farms and the lower atmosphere, as well as two summer internships at NextEra Analytics investigating improvements to the wind farm wake modeling process. As a research scientist at REsurety, she builds and investigates new techniques for analyzing renewable resources and mitigating their financial risk.
Dr. Tomaszewski holds a PhD and Master’s degree in Atmospheric and Oceanic Sciences from the University of Colorado Boulder. She also holds a Bachelor’s degree in Meteorology from the University of Oklahoma.
Jennifer Newman: Okay, thanks everyone for joining the webinar this afternoon. My name is Jennifer Newman, I lead the Atmospheric Science Research team at REsurety. And I’m joined today by Jessica Tomaszewski who is a Senior Research Scientist at REsurety.
So just to give an overview of what we’re going to cover today, we’re going to start with a background of REsurety and then we’ll dive into the 2023 summer heatwave. We’ll talk about what caused it, what impact it had on demand and renewable energy production in ERCOT. We’ll then talk about whether we can expect more frequent heat waves in the future as a result of climate change. And finally, we’ll discuss how you can take extreme events like this heat wave into account for price forecasts. Finally, we’ll leave some time at the end for Q&A. So as a reminder, everyone except for Jessica and myself are muted, but you can put questions into the Q&A box, and we will address them at the end of the presentation.
So just to give an overview of REsurety – REsurety provides software and solutions for those in the clean energy industry to help them meet their financial and sustainability goals. And although we are a solutions provider, we also spend a lot of time into the data that underlies our solutions. So we have in-house experts in atmospheric science, power markets, and data science, and we’ve supported over 10 gigawatts of renewable energy transactions with our proprietary weather data, or hourly generation models, our power price forecasts and our hourly emissions data.
REsurety provides its customers with a software platform that provides data-driven insights in different parts of the project lifecycle, ranging from initial procurement all the way up through portfolio management. So starting at the left, our Project Explorer tool is aimed at that initial prospecting or procurement phase. This tool provides metrics on generation, financial value, and emissions impact for all operational wind and solar projects in the United States, as well as metrics at hypothetical indicative project locations. Moving along to the middle, the Project Evaluator tool is aimed a little bit further along in the project lifecycle, and this allows the user to provide specifics about their project or their contract, and then simulate the value risk and environmental impact of that contract. So as an example, this tool could be used to rank projects in an RFP by their environmental impact. And then finally, on the right, our Portfolio Tracker tool is for projects that have become operational already. And this allows the user to track and audit their contracts, as well as visualize portfolio-level metrics in a single dashboard.
So at this point, I’ll hand it off to Jessica to give an overview of the summer 2023 heat wave.
Jessica Tomaszewski: Great, thank you for that introduction. To kick us off, I’d like to start with an interactive poll question to get us primed for thinking about heat waves. Feel free to respond to the prompt that should pop up on your screen with your answer to the question, “Was 2023 Texas’s hottest summer on record?” Seeing quite a few responses coming in already. We are actually split so far, about 64% of you are saying yes, 2023 was Texas hottest summer. 40% now saying no. Bobbling back and forth a bit there. I will go ahead and stop that. Final results: split, 60% saying yes, 40% saying no. I think I can actually share the results there.
But the answer is no, it was not. Texas experienced its second hottest summer in 2023 as denoted by this plot here that is ranking each state’s 2023 summer against its respective 129 year history of summers. So you can see that while much of the South had summers classified as much warmer than average, only Louisiana experienced its hottest summer in 2023.
And so that brings me to my second poll question and another test of your memories here. Which year was actually Texas hottest summer on record? And you should be prompted with another poll question here. Your options are 2019, 2011, 1988, or 1980. Getting some responses in. Looks like we’re split between 2019 – oh, 2019 is pulling ahead. 40% responses are voting for 2019, 26 and 23 voted for 2011 and 1980, respectively. I’ll go ahead and end that, share the results with you there. Looks like the winner in our poll here for what is Texas’ hottest summer, landing at 2019, and you would be incorrect.
2011 is the hottest summer on record for Texas, which is what is denoted here in this plot. This is showing a time series of each summer’s average temperature, with 2011 as the reddest dot there on top, with an average June, July, and August temperature of 86.8 degrees Fahrenheit. Coming in second is this past summer that just occurred in 2023, with an average summer temperature of 85.3 degrees Fahrenheit. This constitutes an average temperature of four degrees warmer of the 20th century average, which is that horizontal gray line shown there at 81 degrees Fahrenheit. And temperatures in the 80s might not sound terribly hot, but I just want to keep in mind here that these averages include nighttime temperatures. So if you’re thinking about an 85 plus degree summer over the course of three months, that makes for an impressively warm summer. And for additional historical context, only four summers have been cooler than that horizontal line, that 20th century average, since the last 23 years.
Such extreme heat can have severe or even deadly human impacts. So tools like air conditioning can help people stay safe and comfortable, and as more homes become cooled by AC, powering these units does require significant energy from the electrical grid, which in Texas is largely operated by an entity called ERCOT, to meet that demand.
This relationship between hot temperatures and energy demand can be seen in this hourly time series at a point taken in central Texas over the month of August. Temperature is shaded in orange, and I’ve added a horizontal line at about 100 degrees Fahrenheit to draw attention to just the sheer number of consecutive 100 plus degree Fahrenheit days that occurred. Plotted on top of temperature is the demand in blue with the pre-2023 demand record of 80 gigawatts dashed horizontally just to emphasize how many times we did surpass that record in 2023, and then call attention to August 10, which set a new demand record in ERCOT of 85 gigawatts. These high temperatures and high demand led to high power prices as well, which is seen in this green line of real time prices at a nearby hub on the bottom panel there. Prices surpassed $1,000 per megawatt hour several times and even hit $5,000 on August 17.
As mentioned, the summer of 2023 set the new record for energy demand of 85.5 gigawatts, which represents about a 7% jump from the highest demand day last year. And despite 2011 holding the record for the hottest summer, its peak demand actually ranked seventh in history, it’s not the highest demand day, which indicates that there’s been a large increase in energy demand from growing population, growing infrastructure, that’s occurred over the last decade.
So next, I want to talk through the meteorological conditions that led to the heatwave. And to do so I want to first review a quick intro to atmospheric dynamics. It’s important to keep in mind as we talk through these processes that the atmosphere is a three dimensional fluid, and the upper layer affects the surface conditions and vice versa. So we’re really going to be thinking about a column of atmosphere here when we talk about the dynamics. An important feature of the upper layer there that I’ve denoted is something called the jet stream. When the jet stream takes on a wavier shape, as it’s doing in the schematic, you see troughs and ridges start to form which drive and impact the weather at the surface below. Troughs are known for causing more unsettled weather conditions like warm fronts, cold fronts, thunderstorms, precipitation, but today we’ll actually be focusing on the seemingly less exciting feature here known as the ridge in the jet stream, which I’ve highlighted a pink there. Ridges form when the jet stream works northward, in the Northern hemisphere at least, and are associated with warm air, clear skies, sunny conditions, and high pressure a lot up at the upper levels of the atmosphere. This acts to suppress clouds, suppress storms, and just make for fair weather conditions at the surface. And these features of the jet stream – these troughs, these ridges – are properties that can be seen on weather maps, or pressure fields – various ways to view these features. But basically what we’re going to do is take a horizontal slice of the atmospheric column and we’re going to be looking at a horizontal slice in the upper levels next.
Here, what you’re looking at is this shading on an upper level atmospheric map that represents something called the geopotential height, which is not a term or quantity or value you really need to worry about too much. But what this value is is averaged over all of the Julys and Augusts between 1980 and 2023. And we can use the value to infer the location of the jet stream. In a typical summer, as I’m showing here, this ridge in the jet stream forms over the southwestern United States, which drives that sunny, dry weather, often associated with the summer months. Pretty typical summer situation plotted here on the left.
In the summer of 2023, the ridge in the jet stream was particularly amplified, which eventually leads to the creation of the heat dome, which I’ll explain the meaning of next.
In this schematic you can see where the general location of the heat dome set up. It basically consists of the presence of extreme, persistent heat, over a particular region. Now I’ll break down exactly how a heat dome forms, how it sustains itself, how it progresses.
So a key feature of a heat dome is that it’s self sustaining. It’s fueled by its own impacts, which is what this graphic is illustrating here, courtesy of the Washington Post. What happened in the summer of 2023 is that a stronger than typical ridge formed in the jet stream, which caused those nice sort of fairweather, sunny, clear sky conditions to form, which allows the sun, in the summer months, to continue to heat the surface, raised the temperatures, and form a hot air mass. And so you see this red bubble here representing the hot air mass. As we know, heat expands, heat rises, so it forces that atmospheric column that I mentioned before to also expand in this hot bubble situation that you’re seeing here in the schematic. At the top of that heat dome, high pressure starts to form aloft. That high pressure will actually sort of act to push the air in the bubble back down towards the ground. If you remember anything from your thermodynamics classes about the ideal gas law, you will remember that compression of air towards the surface will lead to compressional warming. So we have a combination of clear skies, ample sunshine, and compressional warming now acting to warm the surface even further. With this high pressure aloft, it’s also acting to divert clouds, thunderstorms – anything that would sort of help bring the temperatures down is being prevented by the heat dome itself. As the heat dome continues to get hotter, it will be able to evaporate more moisture out of the soil and that allows the ground to heat even further. As the ground heats further, the heat dome gets hotter, the ridge builds further northward, and allows even more heating to happen. And so this feedback loop will just continue to persist, which is what makes heat domes so persistent and stagnant. And what I want to do now is focus a little bit more on the bottom piece of the feedback loop, as soil moisture provides a seemingly subtle but key component here to the formation of the heat dome.
And so this next schematic is illustrating this exact idea, the importance of soil moisture and sustaining heat domes. In the image on the left, you can see that when the ground is wet, the sun’s energy can be used both for raising the temperature, which is what’s shown in the red there, and for evaporating moisture. So the act of evaporating moisture is latent heat, which means you don’t actually feel that energy as temperature, as heat. The surface experiences it as moisture from the ground being evaporated into a vapor form. And so that doesn’t actually raise the temperature. So moist soil can help keep temperatures feeling cooler, which is why when you step on your hot driveway, it feels very hot, and you walk back on your grass that it’s a bit of relief, it’s a bit cooler. However, on the right, we get more of this hot driveway situation where when the ground is dry, less of the sun’s energy will be used for evaporation, which means more of it is available to directly raise the temperature. And so this acts as a feedback loop in itself. So as the ground gets hotter, it evaporates more moisture, which makes the ground drier, which makes the temperatures hotter, which just continues to fuel this heat dome.
And as seen in August of 2023, mean temperature and precipitation percentiles are showing that parts of Texas experienced record drought alongside the extreme heat, which just furthers this story of how the heat dome played out here in 2023.
Texas isn’t the only area that experiences heat domes. And in fact, the last major occurrence of one in the United States was in June of 2021. You probably remember hearing news reports of an anomalous ridge also then setting up in the jet stream, in a particularly unusual location over the Pacific Northwest, which caused record heat, severe drought, and wildfires, which we experienced the downwind impacts of over much of the United States.
So now I will pass it back to Jen to discuss the impact of heat waves on demand and renewable energy generation.
Jennifer: Right, so there’s a pretty obvious relationship between demand and temperature. So just to show that with a little bit of data, this plot here shows ERCOT-wide demand on the Y axis and cooling degree days on the X axis. Cooling degree days are a measure of how many degrees you would need to cool your home in order for it to feel comfortable. So the higher that cooling degree day metric is, the warmer it is outside. So if you look at this scatterplot, there’s that obvious relationship where when you had higher temperatures, the demand tended to be higher as well. And all of those dark red circles are experienced in August, also highlighted that peak demand record day on August 10, but there is a little bit of scatter here where you can have a similar temperature, but different values of demand. And that’s because there are other non-temperature factors that also impact demand. Things like the relative humidity, the wind speed, or whether it’s a weekday or a weekend.
What’s also fairly obvious is the impact that heat wave is going to have on solar generation. So this map here shows the August 2023 solar irradiance anomaly. So what this is telling us is how sunny was it in different parts of the country in August 2023, compared to a typical August. So if you look at Texas, you can see that a lot of it was pretty far above average in terms of the solar irradiance. And that’s because we had this heat dome in place, we didn’t have much cloud cover, we didn’t have storms coming through, so the sun was shining almost all the time and we had really high solar generation values as a result.
So what’s not as obvious is the impact that heat waves have on wind generation. So I’m going to pose this to the group. So this is our next poll question: what impact do heat waves have on wind generation? You should see that popping up in just a second. All right, so what impacts do heat waves have on wind generation? Do they increase wind generation? Are they actually good news for wind? Do they decrease wind generation? Or does it depend? Alright, so about half of you are saying they decrease wind generation, some people are saying it depends. Give everyone a couple more seconds. All right, so I’m going to end that and share the results. So the winning answer was that heat waves decrease wind generation. The answer is actually that it depends. If the answer was more straightforward, then this section would be a lot shorter, and you wouldn’t have to listen to me. But I’ve already got the slides ready, so we’re going to talk about why it’s a little bit more nuanced.
So now we’re looking at a similar map, but for the August 2023, 100 meter wind speed anomaly. So this is telling us how windy it was in different parts of the country compared to a typical August. So looking at Texas again, you can see there were some parts of Texas where it was actually more windy than usual. And because this is compared to a typical August doesn’t necessarily mean it was super windy, it just means that compared to how August typically looks in Texas, it was a little bit more windy than usual. So next we’re going to talk a little bit about why people might think that heat waves are going to decrease wind generation.
So there is somewhat of a link between extreme temperatures and low wind speeds. And that’s because both extreme temperatures and low wind speeds are associated with a common atmospheric phenomenon, which is a high pressure system. So when you have these high pressure systems in place at the surface, they tend to divert clouds. You don’t have a lot of storms moving through or cloud cover. So temperatures can get fairly extreme, either very warm or very cold. And in addition, these air masses tend to be very stagnant. You don’t have these storm systems coming through bringing gusty winds. So you tend to have fairly low wind speeds when you have these high pressure systems in place. This was actually exactly what we saw during winter storm Uri in 2021. So if you look at the left figure, this is a sea level pressure chart during that February 2021 event. So there’s a big high pressure system in place at the surface. And now looking over at the right, this is zoomed in at 80 meter wind speeds in Texas. So some of those shaded areas that are the lighter or darker blue are areas where the 80 meter wind speed was below five meters per second. And this is barely above the cut in of a wind turbine. So during that event, we had this high pressure system, we had really cold temperatures, and we had fairly low wind speeds.
But you don’t always have these high pressure systems in place during these extreme temperature events. And to compare and contrast here, I’ve shown the sea level pressure for both winter storm Uri on the left, and the summer 2023 peak demand day on the right. And even if you’re not a meteorologist, you can tell that the colors and these figures look a little bit different. So on the left for winter storm Uri, we had, again, this big, high pressure system in place, that really stagnant air mass with low wind speeds. And then in contrast, during the peak demand day from this past summer, there was actually a low pressure system that was moving through Texas. And in fact, there were several days in August when the Weather Service issued red flag warnings for wildfire risk because we had fairly breezy conditions. So why didn’t we have these really stagnant, low wind speed conditions during the heat wave this summer?
So the answer comes down to how pressure at the surface reacts to either a very cold air mass or a very warm air mass. So when you have a really cold air mass, like we had with winter storm Uri, all of that cold and dense air sinks, and then it builds up at the surface. So you can see all those little air particles building up at the surface. And all of those building up air particles are going to make that surface pressure even higher. So that’s going to deepen that surface high pressure system and kind of enhance those stagnant weather conditions.
But something different happens when you have a really warm air mass. And that’s because warm air tends to rise because it is less dense than cold air. So when you have a really warm air mass in place like we did this summer with the heat dome, all that warm air is going to tend to rise which is actually going to lower the surface pressure. So we don’t really have this kind of feedback loop with that surface high pressure deepening like we do during these cold air events.
So the key takeaway here is that those surface high pressure systems that we associate with low wind generation are going to tend to be more persistent during cold weather events versus warm weather events. So we aren’t always going to see these persistent high pressure systems when we have these heat dome events.
So next, we can dig into what wind generation actually looked like this summer. So this scatterplot shows the cooling degree days again on the X axis. And on the Y axis, I’m showing the daily ERCOT wind generation minimum. So this is telling you the lowest level that ERCOT wind generation dips throughout each day. So what you can see is that there’s not really much of a correlation between how hot it was and how low or high the wind generation was. And you can have times when the temperature was fairly similar, but the level of wind generation was very different. Just to add a little bit more detail here, this top dashed line shows the ERCOT low wind scenario. And the bottom dashed line shows the ERCOT extreme low wind scenario. And these are scenarios that ERCOT planned for going into this summer, in order to make sure that they could meet demand. So if they had really high demand and wind was very low, they wanted to make sure they would have enough resources to meet that demand. So there were a handful of days this past August where the wind generation did dip below that low wind scenario. But all of these days took place toward the end of August and there are actually some relatively windy days toward the beginning of August. So next we’re going to zoom in on a couple of days that had pretty similar temperatures, but very different levels of wind generation. And we’ll look at how that impacted the price of power.
So we’re looking here at ERCOT demand on the top, ERCOT wind generation in the middle, and then the ERCOT West real-time price on the bottom. And we’re showing the same hourly plots for August 14 in blue and August 17 in green. So what you can see is that although the peak demand and the afternoon demand profiles on both these days looked very similar, the wind generation was very different on each of these days, with the wind generation being about twice as high on the 14th as compared to the 17th, the 17th did kind of dip to that low wind scenario. So as a result, the prices on August 17, were much higher and reached the market cap of $5,000 per megawatt hour. And that’s because we didn’t have this really low cost generation source in the wind available. So more expensive generators had to be brought on line to meet demand and that increased the price of power. So a key takeaway from this section is that the wind generation isn’t always going to be low on hot days, although it’s obviously important to plan for the scenario where it might be, because that’s obviously going to impact the price of power.
Alright, so I’m going to hand it back to Jessica to talk about why we can expect more frequent heat waves in the future.
Jessica: Sounds good. So before we think about the future impacts of heat waves, let’s think about the recent history. And we’ll do so with another poll question, I believe our final one. True or false: heat waves have become more common in recent years? Go ahead and respond to the pop up you see there. Some falses, some truths. Looks like we’re trending towards true being our answer. I’ll share the results here. We’re at 86% of folks saying that is true, heat waves have become more common in recent years.
And those 86% would be correct. The average amount, length, and intensity of heat waves have steadily increased in the U.S. since the 1960s, which can be seen in this plot on the left showing the average number of heat waves per year. And in the plot on the right, where you’re looking at the upward trend and cooling degree days, which is as Jen mentioned earlier, is a representation of how hot a day is compared to a standard temperature like 65 degrees, that number has been trending upwards in the last few decades, indicating that heat waves have become more more frequent and intense these last several years.
Scientists have used climate simulations to visualize this increase in extreme heat wave events as the Earth warms due to global warming. So they first defined a heat event, or they first identified a heat even,t during this pre-industrial period, which is defined as the years between 1850 and 1900, or before the impacts of human warming started to take place, and identified an event that had temperatures so high that they were only reached one time over a given land area during those 50 years. Thus, constituting a literal 1 in 50 year event. And that 1 in 50 year event is denoted on this horizontal bar as this orange box there just to give a visual of what that looks like. So during the baseline period, a 1 in 50 year event is shown by that one orange box, or a 2% likelihood of occurrence. If we look at the last several years, 1980s through now, the average global temperature has increased by one degree Celsius since that pre-industrial period. And simulations now see that that type of heat wave, that 1 in 50 year event, or an event that used to happen only 1 in 50 years, now happens 5 times in 50 years, you see those five orange boxes there along that horizontal gray line. These climate simulations can then be projected forward with potential global warming scenarios. And we can see that as the Earth continues to warm, extreme heat becomes more common. Even if the world can meet its goal of limiting warming to 1.5 degrees Celsius, extreme heat waves are likely to be almost twice as likely as they are today, and nine times more likely than they once were, and those probabilities only increase as global warming increases, as you can see with those subsequent horizontal lines and the many more orange boxes that begin to appear.
The physical drivers of the projected increases in heat waves are of ongoing discussion in the scientific community. One factor is the increase in soil drying. So as I mentioned before, the feedback loop between soil moisture and temperature is extremely important, especially considering heat wave events. So as the Earth warms, there’s more heat available in the atmosphere and that additional heat available will allow more moisture to evaporate out of the soil, which will amplify that effect of solar radiation on the surface temperature which dries out more soil and just perpetuates that feedback, making heat waves more intense, more stagnant, and have more severity. Another potential element driving increased heat waves in a changing climate is the destabilizing of the jet stream, or wavier jet stream, and that idea is shown in the schematic on the right. As the Earth warms, there’s more heat in the atmosphere, and that heat may result in atmospheric dynamic responses that destabilize the jet stream and cause more anomalous and persistent ridging that acts to create and sustain these heat domes. The flip side of that is that this type of mechanism, this wavier jet stream, can also allow for more intense polar air intrusions as well. So you probably remember, we discussed earlier, winter storm Uri back in February 2021 was a result of a destabilized jet stream. Future warming could impact the jet stream enough to allow for events like that to happen with increasing severity.
And finally, I will return the mic back to Jen to discuss accounting for extreme weather events in your power price forecast.
Jennifer: Okay, so now that we know that these extreme events are likely to become more frequent, we’re going to talk about how you can take them into account when you’re forecasting the price of power.
So we’ll first talk about why it’s so important to capture these extreme events. And that’s because these extreme weather events tend to have a pretty extreme impact on the price of power. That’s both because they increase demand and they can also impact the generation that’s available to meet that demand. So to kind of see how extreme events have impacted the price of power in the past, I’m showing here the monthly average price of power in ERCOT West. So you can kind of pretty easily pick out when some of these more extreme weather events have occurred. So you can see when the August 2011 heat wave occurred, and again, that was the hottest summer on record for Texas, there was that heatwave in August 2019, February 2021, winter storm Uri is basically off the charts, I’ve kind of put an arrow there showing where the price would be. There was a smaller heat wave last summer in July that led to some high prices, and then again, that August 2023 heat wave. So you can see for the majority of this historical period, the prices are kind of between $25 and $50 per megawatt hour, but they can be two to three times as high as that when you have these extreme weather events. And importantly, the status quo in the industry is to assume that you’re going to experience these typical weather conditions in the future. This is known as the 8760, or typical weather year approach. And when you’re doing that, you’re basically assuming that you’re going to have these kind of normal average power prices when we can see that that’s obviously not the case all the time.
So what REsurety does is instead of using a typical weather year to power a power price forecast, we use a variety of different historical weather conditions. And to show the value of this approach, this plot shows the hourly forecasted price of power for a June day in 2030. So the black line denotes the price of power that we forecast if we assume weather conditions resemble a typical weather year of 2013. And all of those individual colored lines indicate the power prices that we forecast based on different weather conditions. So you can see depending on what weather conditions you expect, you can have much higher prices of power.
So next we’re going to take that weather variability approach to forecasting power prices for August 2023. So this blue bar shows the range of power prices that were forecasted as part of REsurety’s Q3 Weather-Smart forecast. And this forecast takes weather conditions from 1980 through 2022 into account. So this is basically pretending that we’re at the beginning of the summer so we don’t know what the weather in August is going to be like, but what can we forecast based on weather that we’ve experienced in the past. So again, that blue bar shows the range of power prices that were forecasted and each of those green lines denotes the forecast from a particular weather year. And the actual August 2023 price was about $200 per megawatt hour. So it is on the tail of the distribution, but it isn’t the highest price that we actually forecasted. And that award goes to August 2011. So if we took August 2011 weather conditions and put them in the grid of 2023, we would forecast a higher price of power of $220 per megawatt hour. And that’s partially because August 2011 was hotter than 2023, as we discussed, and it was also less windy than 2023. And both of those factors would lead to higher power prices. So again, going into the summer, we didn’t know exactly what the weather was going to look like but by including this large range of weather conditions, including some extreme events, we were able to see that an outcome like August 2023 was indeed possible going into the summer. And then if you use that typical weather year approach you would get that purple circle. So you would only forecast $70 per megawatt hour for August. So again, this is assuming that you’re experiencing average temperatures, average wind speeds. So you’re definitely going to underestimate the potential volatility that could be experienced this summer if you just assume these typical meteorological conditions.
So just to close with some key takeaways, in 2023, Texas experienced its second hottest summer on record, second only to 2011. And a new peak demand record of about 85 gigawatts was set. The heat wave was due to the heat dome, which was a region of warm air that can get trapped over the surface for days or weeks at a time. During the heatwave solar generation tended to do pretty well while wind generation was variable, it kind of depended on the day. A heat wave like the one we experienced this summer are expected to become more common in the future as a result of climate change. And you can take extreme events like this heat wave into account in your price forecasts by making sure you incorporate a variety of different weather conditions into your price model rather than just a single typical hydrological year.
So that ends the content of our presentations, we’re going to move to questions now. So feel free to put any questions you have into the Q&A, and we’ll start to answer those.
Jessica: Great. So one question I’m seeing here is that we’ve spoken quite a bit about 2011, which was Texas’ actual hottest summer on record. Was that event the result of a heat dome as well?
And I can show you on slide 59, what 2011 looks like in that same slice of the atmosphere that I showed you back when we were talking about 2023. So the short answer is that 2011 was also a heat dome event over Texas. You can see there that the summer of 2011 jet stream, while a bit less intense and less amplified than in 2023, it was actually much wider in size and more persistent in duration. So this is an average from June, July, and August of 2011, and you can see just this big blob of high pressure in those upper levels of the atmosphere, which caused persistent heating and drying of the soils. So I’ve also included the drought monitor from August 30, 2011. You can see just this massive blob of red over Texas, indicating just how intense and persistent that heat dome that year was.
Jennifer: Alright, we had another question come in. Can I see the same analysis for weather in other markets, for example, in SPP?
So the answer is yes. REsurety uses what we call our Weather-Smart approach, where we use this variety of weather conditions and our power price forecasts for all of the ISOs where we produce power price forecasts. And currently we have coverage in six of the seven U.S. ISOs, and we’re looking to add the seventh very soon.
Alright, I’m going to the next question. What was unique about August 17 that led to power prices hitting the $5,000 per megawatt hour price cap, as opposed to that many other high demand days last summer?
So as I discussed a little bit earlier, there’s some other non-temperature dependent factors that also impact demand. So if it’s very humid, that’s going to drive up demand. And it also depends on what generation sources you have available to meet that demand. In the case of August 17, again, the wind generation was very low. And because that is a fairly inexpensive source of generation, and it wasn’t necessarily there on August 17, more expensive generators needed to get used on that day and that drove up the price of power. So that’s kind of what made that day a little bit different from some of the other days when temperatures were very high.
Jessica: Looks like we have a question here on the relationship between wind and temperature. Does this relationship depend on other factors? Can we not assert a direct or inverse correlation between these two variables?
And I think the short answer here is that weather conditions at the surface are really complicated and there’s not a direct influence between temperature and wind. Wind is driven, as Jen mentioned in her portion of the presentation, wind is driven typically by pressure differentials across the surface. So my plot on slide 15 that shows the impact the jet stream has on the surface conditions, you can see that there are surface fronts there, there’s warm fronts, there’s cold fronts. Those types of intense differentials in temperature and pressure at the surface will drive more of your wind speed anomalies as opposed to just temperature by itself. So what you really need is an area of warm temperature and an area of cold temperature sort of in proximity to drive a pressure gradient between them and that’s what’s going to fuel wind.
Jennifer: And actually, if we go to slide 58 in the appendix, I actually did do a little bit of analysis, seeing if you could find a correlation between temperature and wind generation in ERCOT. So we call these plots the fuzzballs because there’s a lot of scatter in them. What this is basically showing is for each month, so there’s 12 plots here, what does a representative portfolio capacity factor look like in ERCOT as a function of the average five day temperature? So this is kind of looking at over the duration of five days are you tending to see these really cold temperatures and then a few days of also very low wind speeds. And what you can see is that in some of the colder months, so the top couple panels of January and February, there is somewhat of a relation, there’s a lot of scatter there, where you do tend to see lower wind speeds, lower capacity factors with those colder temperatures. And this is probably because of those stagnant high pressure systems that tend to be more common in the winter. But then when you look to the summer months, so like July and August, that’s where you really see a lot of the scatter in that relationship kind of breaks down because there are other factors that are going to impact the capacity factor. So there is somewhat of a relationship, but it’s primarily just in the winter months and that’s because of the prevalence of those stagnant high pressure systems.
Another question, I’m wondering how users of the power forecasting tool typically make use of the distribution of prices based on historical weather years. Do you try to estimate likelihood of occurrence of each weather year or do you just provide the distribution and leave interpretation to the user?
So that’s a great question. And what we typically do is we provide quantiles. So we’ll provide a P50 of a price forecast or a capture rate across all those weather years in addition to a P99, P1, etc. And what we found is our customers tend to use this in kind of a stress test scenario. So they want to see how can I plan my budget or adjust my risk accordingly so that if this kind of P99 scenario does happen, how can I kind of adjust for that? So that’s kind of typically how we’ve handled it, instead of just presenting this whole distribution, we’ve given quantiles just to give an assessment of what are the best and worst case scenarios.
Alright, another question. Why does wind generation typically tend to be low in the afternoon? So Jessica, do you want to take that?
Jessica: Yeah, sure. So I think we showed some plots showing a typical day cycle of wind generation over the course of the day. Wind is typically low in the afternoon, because the sun is fully up and at its highest point, and is heating the surface of the earth and causing the sort of overturning of the air, which atmospheric scientists refer to as “mixing” often. And what that’s doing is spreading out the wind speeds throughout the atmospheric column that I mentioned. Wind actually ramps up in the evening hours, especially in the southern plains of the United States, due to a phenomenon known as the low level jet. The low level jet tends to set up during the nighttime hours due to dynamics that occur over the southern portion of the U.S. and cause amplify wind speeds just a little bit above the surface. So at about the point where we build wind turbines. And then as I mentioned, that wind will persist throughout the night and then as the sun comes up, will sort of erode those higher winds away so that during the day, those winds aren’t available for wind turbines to extract energy from.
Jennifer: Alright, and we’ll just answer one more question before wrapping up. Is this information – I assume information on the power price forecasts – only available ISO-wide or can you look at a specific renewable asset to see how it performed in extreme weather events?
So yes, we do modeling at specific renewable energy assets. So we take all of that weather data, and we look at not just what was the impact of that weather on demand and the price of power, but also how does that impact generation at a specific renewable energy asset. So we can then look at what is the risk profile of that renewable energy asset and how might it perform with these extreme weather events.
Alright, so at this point, we’re going to wrap up. So thank you everyone for attending the webinar. We really appreciate all of your attention and all the great questions. Thank you.
Return to the events main menu.
Do you struggle understanding and forecasting performance of your Power Purchase Agreements? Many clean energy buyers share this same challenge. In this session you will learn an easier way to:
We shared real-world use cases where clean energy buyers have found simpler, more efficient ways of understanding and reporting on their portfolios. We even gave specific examples of corporations uncovering hundreds of thousands of dollars in previously undiscovered errors. Watch the recording of the full session below.
Irina Gumennik, Director, Analytics Services
Irina is an analyst with more than 15 years of experience in the renewable energy industry. Before joining REsurety, Irina was part of the team that permitted, constructed, and commissioned the Block Island Wind Farm, the first offshore wind energy project in the United States. Irina also worked for the world’s largest demand response service provider on demand-side solutions and battery storage products. At REsurety, Irina directs portfolio analytics services to analyze performance drivers and identify risk management strategies.
Irina holds a Master’s degree in Civil & Environmental Engineering from Tufts University in Medford, Massachusetts. She also holds a Bachelor’s degree in Environmental Science and Public Policy from Harvard University.
Carl Ostridge, SVP of Analytics Services
Carl Ostridge has more than 15 years of energy experience, specializing in energy risk management, electricity markets, and renewable energy project performance. Prior to joining REsurety, Mr. Ostridge worked for DNV GL analyzing and improving the accuracy of wind farm energy analyses and developing models to predict wind farm energy output. His extensive industry experience and proven analytical skills support REsurety’s industry-leading tools and expertise in weather-related risk and valuation for renewable energy projects.
Mr. Ostridge holds a Master’s degree in Astrophysics from the University of Exeter in the UK.
Hannah Gollan: So all call materials as well as any other events or resources mentioned during this call will be available on Interconnect, which is our member portal. If you are a non-member, you will be receiving an email with some of those links outside of Interconnect so you can view them. But everyone will be getting a post-event email with links and resources and contact information for our wonderful speakers today. Now, without further ado, on to our presentation. Just to note that we do have some time at the end for that Q&A, as I mentioned, so as you have questions go ahead and drop them in the Q&A panel, and our speakers will address them. There will not be a chat function as this is a webinar. So again, everything is recorded, you’ll get a post-event email with the presentation materials, and please drop your questions in the chat. So today, we have our speakers Irina Gumennik, the Director of Analytics Services at REsurety and Carl Ostridge, SVP of Analytics Services at REsurety. And I am going to go ahead and stop sharing and pass it to them.
Carl Ostridge: Thanks, Hannah. Sorry for the echo there. Okay, I think that’s all sorted. Right. Thanks, Hannah. And, yeah, thank you for the introduction. So hi everyone, my name is Carl Ostridge. I’m going to start us off with a brief introduction for REsurety for anyone on the call that isn’t familiar with us already, and what we do. I’m going to give a quick overview of the content and the agenda that we’re going to cover today. And then, I’m going to hand off to Irina to guide us through the first part of the presentation. So REsurety is a solutions provider that serves the financial and sustainability needs of the clean energy industry. Our core customers today are clean energy buyers, sellers and investors. And we have in house expertise in renewable energy, atmospheric science, power markets, and data science. And particularly relevant to today’s webinar, we provide settlement auditing and tracking services to more than 10 gigawatts of active renewable energy contracts each month. So REsurety also has a software platform, and that provides our customers with insights along different stages of the clean energy procurement lifecycle. So on the left, we have our Project Explorer, and that supports workflows related to initial prospecting and market discovery. In the middle, we have the Project Evaluator, and that supports workflows related to RFPs with project and contract-specific analyses. And then on the right, we have Portfolio Tracker, and that’s aimed at tracking and understanding the performance of existing projects and contracts, and we’ll touch on that a little bit later. So with the background and introduction out of the way, there are three primary points and topics that we want to address in the presentation today. The first is to highlight the invoicing and settlement challenges that we see clean energy buyers facing today, especially in the face of growing portfolios, and also growing contractual complexity. We want to demonstrate through some of the examples we’ve encountered through our work with our customers and highlight why we believe it’s important to have a robust auditing process in place for these monthly settlements. And why it’s also necessary to dig deeper than just the standard invoicing data to really understand the drivers of certain outcomes. Finally, we’ll wrap up with some proposed actions and best practices that we think everyone can adopt in terms of avoiding the types of errors we do regularly see, streamline this monthly settlement process, and also hopefully inform and improve future procurement decisions as well. So as Hannah said, we’ll save some time at the end for Q&A, so please do keep those questions coming through throughout the presentation. And with that, I’ll hand off to Irina to get us started.
Irina Gumennik: Alright, great. Thank you, Carl. So I’m going to be talking about some of the common issues that we often find with invoices and my hope is that your takeaway will be a few tips and tricks, things that you can be on the lookout for when reviewing the invoices and also get a sense of what the financial impact of some of these seemingly small issues can be. So at a high level, just starting, first, what are we hearing from the clean energy buyer community that needs to happen? And why is it challenging?
So the first thing, as Carl mentioned, just auditing the invoices that are coming in, ensuring that they’re accurate, and having confidence that you can release the invoice for payment. Challenges to that: it’s a lot of data, the best practice right now is still to share Excel files, they’re often going to be in different formats from different projects. It’s just a lot of data. And increasingly, it’s also starting to get more complex. The days of having a power purchase agreement that’s a fairly simple contract for difference with a fixed and a floating price at times generation, we’ve seen increasingly a lot of different ways to address different risks that result in basis sharing clauses, non settlement clauses, price floors, and they’re all going to be a little bit different from project to project. And so that’s going to be appearing in these invoices as well. And the turnaround time is typically short, the power purchase agreements are going to have a term in there around when the invoices are due. And it’s going to be like that every month, rinse and repeat. So a lot of data to get through pretty quickly.
The other piece of that is even if when you get to that point, you feel confident on the numbers, being able to explain if someone comes to you and says, “What happened this month? What were the drivers of the financial performance? What’s been going on the past quarter, the past year?” Being able to have ready information to answer that question can be challenging. The drivers can be complex, a lot of interaction weather, what’s going on in wholesale markets across the country, maybe across the globe, what’s going on with project operations at each project. And in some cases, particularly as a clean energy buyer, you might have limited access to the type of data that you would really want to have to understand the drivers.
And then on top of all of that, every month, also the workflow around trying to get a forecast. So the forecast could be just an accrual estimate for what invoice values you can expect next month. It could be budgeting for the next month or the next year. We’re not going to cover that particular topic here on this webinar today, but it’s something that will be on your mind. And so this is just a visual of that. I like to, as a reminder, just see that this is really the experience, you just get that influx of invoices, different schedules in the month, different formats, different granularity, and there’s just hundreds of rows of hourly and sometimes sub-hourly data and calculations. So that’s why it’s challenging.
And so the next thing I’m going to go through are three different examples that I would say account for a majority of the common issues that we find. So the first one is when settlement is using an incorrect floating price index. So for example, if a contract is supposed to settle using hub prices, but is actually settling at the node in the invoice. So in this graph here, the green lines are the hub prices that should be the contract settlement. But the gray line is what was actually invoiced. And you can see that what happens a lot is that the nodal prices tend to be less than the hub prices. And so that difference in project value between the hub and the node, which we call the hub node basis, really resulted in a $0.52 a megawatt hour overpayment by the buyer, which in aggregate was over $100,000.
And it also shows an example that tees up the next example where you can see sometimes prices can even go negative. And so that’s something that there are various ways to address and can lead to one of the next common errors where the easiest way to address negative prices, which are shown in this top graph, the gray bars that are below the zero line, those are hours where prices went negative. Those correspond to hours where there’s a lot of generation – that’s actually a typical pattern we see. And in this particular case, the settlement was supposed to just be $0 anytime prices went negative. But if that doesn’t happen, and the entire month of hourly price data is included, combined with the generation, that can also mean a potential overpayment, in this particular case, for just one month of about $95,000. And I say “would have overpaid” because in this case, the buyer was able to find the issue, something that we worked with them on and they went to the seller and were able to correct the invoice pretty quickly. I mean, it was a fairly small change to make in the Excel file, and they were able to to correct it before the final payment was due, and so the invoice was actually corrected before it was due.
So the third topic, that is one of my favorites, because there’s different flavors. But before I get into the different flavors of this topic, and what it can mean in the financial performance sense, is when there’s a misalignment between price and generation time series. So what I’m showing here is on the top, just the average hourly generation for a solar project in Texas. You can see that at nighttime, there’s no generation, then there’s a little bit as the sun comes up, a lot of generation during the sunny afternoons, and then a pretty quick decline during the sunset, and again, no generation at night. And then on the bottom graph, we have the prices at ERCOT North Hub I picked for this example, from July of this year. And you can see that in this plot, we have what’s labeled as hour beginning 19, which is really 7-8pm local time. That’s sunset, and that’s when the solar generation is rapidly declining, when you capture a little bit, and then as soon as the sun goes down you’re not going to be generating anymore. And not a coincidence, this is actually a pattern we see that happens to be when the prices are the highest on average for the month. And so that little bit of generation you’re able to capture as the sun goes down is really going to be worth a lot. So what happens if there’s an issue and the time series is offset? Well, now you basically have that hour beginning 19, that’s 7-8pm. It’s labeled as 7pm, but really, it’s measuring already nighttime after sunset. And so basically, there’s no generation there, you’re missing out on all of that value. And if you look at July in particular, to put some numbers around this, to go from $52 a megawatt hour value for that project for the month to $40. So it’s a $12 difference from just a one hour offset that seems maybe like a small difference, but can have a big impact. So that’s really what I’m showing here, I’ll go through four flavors of this. So this particular example, it’s something that can happen when daylight savings time is incorrectly applied. I know at REsurety, every March and November were kind of up in arms and ready for when daylight savings time is flipping, because that causes so many challenges to these hourly time series. So here what I’m showing, again, that gold line, now I’m just showing basically two days every hour. So the first day, nice sunny day, the second day, some clouds roll through, and the golden line is still representing the physical reality on the ground and how much solar generation is being produced. The gray line is showing what’s actually invoiced as I basically showed in the previous slide. And in this case, it would have meant $400,000 that from the buyers perspective would have overpaid if you assume like a $30 a megawatt hour PPA. And another flavor of this is a one hour offset in the other direction which flips the settlement where now the buyer is being overpaid by the seller by half a million dollars. So a one hour switch in the opposite direction means you’re capturing more of that sunset hour in the settlement. And so it can really go either way with a seemingly small difference. Another example is if you have been correct timezone, so this is one where the offset is going to be more than a few hours. And I think when you visualize it, it can be easy to see. And I particularly chose a solar project because you can see how there shouldn’t be any solar generation at night. But again, it’s easy to see when you plot it. But when you go back to that visual of all of those Excel files, all of those rows of data, it can be really easy to miss. And we actually had that recently where the data was sorted strangely and never unsorted. And so when we got it, we saw that there was solar generation at night. And that obviously is a flag that there’s some sort of issue going on. And that interestingly in this example, was about a similar difference where the buyers actually also would have been overpaid by $600,000.
The last example is some sort of meter communication malfunction. I picked arbitrarily 10 hours but I’ve seen all sorts of flavors of this. I saw one where it was 36 hours before daylight savings and then went to 38 hour offset after daylight savings. We had an issue that we saw recently where the offset actually changed each month as the meter kept going on and offline throughout the month. And something like that in this particular example would have been the buyer overpaying by a million dollars, which also probably, in this case is interesting, would have been flagged even before looking into the data because if you are the buyer of solar projects in Texas in the summer in July, you should be expecting to see payments coming your way as part of your PPA. And so if you receive an invoice that suggests otherwise, that’s probably an indication to dig in further and hopefully you can see that this can be one of the things you can check for quickly and hopefully get it resolved because something as simple as this can have a pretty big impact. So as much as I would love to keep talking about timezones, I could go on and on all day, I won’t. I’m gonna pass the mic back on to Carl for the next section.
Carl: Thanks, Irina. And the thing that I always have to remind myself about is that those examples that Irina was just giving, they’re all real. We’ve sort of anonymized them and made them general for the purposes of this presentation. But those are all issues that we’ve seen as we’ve been helping our customers with invoicing and settlement. And so, yeah, they may not happen all that often, but they definitely do happen. So definitely something to be aware of. So in this next section, we’re going to continue the theme of showing some examples to help illustrate the points we’re trying to make.
I have a couple of different examples that touch upon the Explain workflow. And so we just covered the auditing process, you get these invoices every month, how do you make sure that the calculations that are going into them are correct and accurate? Now we’ve got a couple of examples that get to the idea and the workflow of needing to explain why certain outcomes happened, understanding why maybe one contract is requiring a payment to the project, while another one you’re receiving a payment from the projects. Understanding whether the project is performing in line with expectations, and if not, how big of an impact is that having on your progress towards procurement and sustainability goals?
So the first example that I’ve got here is for a wind project in SPP. Again, we’ve sort of picked a general example with some real data. So we’re looking at a December month, and I’ve started here on the left with just the standard aggregated view that you might get in either the statement or the invoices, and each month, it’s telling you how many megawatt hours were generated, and the payment that’s required, either to or from the project. And just with these aggregated metrics, it sort of raises more questions than it answers when trying to think about how might you understand and explain what’s going on here. I look at this and say, “Well, that capacity factor is kind of low for a December, especially in SPP, and we don’t have the power prices on here. But in a month, where there’s reasonably high power prices, you might expect actually, the payments be moving in the other direction.” In order to be able to dig in and really understand what’s going on, we have to go a level deeper. So on the next slide, we’ll start that process. So here, we’re doing the first thing that I always recommend, and which Irina already touched on, which is to plot the data. So in every invoice, there’s the interval data, there’s hourly data that underlies those aggregate statistics. And so here, we’re plotting the hourly generation from the project. And initially, there might not feel like there’s much to see here, the generation’s moving up and down and in line with the weather patterns. I’ve highlighted this red box in the second half of the month, maybe if you look at it, you could say, “Well, it looks like the generation from the project’s a little bit lower.” But without more information, without more data, it’s actually really hard to know whether that lower output in the second half of the month is being driven by just the weather that the project’s experienced and the resource that was available, or whether it was due to some sort of operational issue. So in order to be able to go a level deeper, we need to add data to the situation, add information. So what we do, and what we’ll show on the next slide, is adding a modeled time series. So we take the weather data, we take weather data to model the output of the project, and show in each hour how much energy that project could have produced given the resource that was available to it. And that’s the green line that we’ve just added to the plot here. And when we do that, it becomes quickly apparent that yes, in fact, that lower generation in the second half of the month was due to some type of reduced output from the project. You can see that in the first 10 days or so of the month, the blue line and the green line agree pretty closely with one another. But then starting around the 12th the output of the projects reduced, that blue line drops below the green line. And actually that pattern continues for the rest of the month. One thing that’s also worth pointing out as we dig a bit deeper, we’ll see that actually there was also winter storm that came through right before Christmas here. And that actually has a bigger impact and reduces the output of the project even further. So now that we have this insight, the obvious question is “Okay, but how much did this affect my PPA settlement?” So again, to start digging into that, we have to add more data. Now we’re plotting up the prices from the invoice and the price time series. And we can see that actually, around the time of that winter storm, there were some high and volatile prices that also coincided with that reduced output. So now we’re starting to build a picture of, “Okay, maybe that payment that was due to the project was a function of this reduced output and the high prices during that time.” So the last plot we’re going to make here is to look at calculating the invoice settlement using the metered generation and the observed prices, and also the model generation. And so we can see how this contract would have settled if the project had been able to operate absent any operational issues. So this last plot here, we’re seeing a cumulative settlement throughout the month. And what this shows us is, for the first, let’s say two and a half weeks, of this month, there was actually very little difference between the settlement from a financial perspective of this contract, whether it is based on the metered generation or the modeled generation. But when we get to that period right before Christmas, and that winter storm, that’s where there is now a meaningful difference and a reduced financial outcome or reduced settlement value for the buyer, because of that reduced output for the project. So returning to these aggregate statistics that we started this example with, now we can add the second column to the table, we can look at what those aggregate statistics would look like with the modeled generation. And that allows us to start understanding and explaining the outcomes. And then so we can see in this case, the output of the project was reduced by about 30,000 megawatt hours, that means 30,000 RECs less towards your sustainability goals. And it also impacted the contract settlement from a financial perspective as well, as we just discussed. It’s worth saying here that most of the contracts that we see and work with include earlier time-based availability guarantee. So while this type of insight doesn’t necessarily affect the settlement outcomes of these contracts on an ongoing basis, it is an important insight to start building, in order to be able to understand why contracts are performing the way they are, and how that fits into your future plans and strategies around meeting your sustainability goals. You want to understand what’s driving things in order to be able to make informed decisions in the future.
So I have a second example here, this one around economic curtailment. And so this, just as a quick background economics curtailment is the project reducing its output in response to low nodal prices. If price is at the node, where the project connects to the grid, become sufficiently low, then it’s no longer economic for the project to continue operating. And so at that point, the project will reduce its output. The challenge here is that in order to understand whether the project that you’re contracted with is experiencing this issue, the standard invoice data that you receive doesn’t really give you, again, the insights to see that. So here we’ve plotted the generation time series, and also the hourly time series of prices. And, again, you look at it and there’s nothing immediately jumping off the page. You might look at some of the quick ramp up and ramp down events in the second half of the month. And think that maybe those look a bit interesting. But without additional data, it’s really hard to say whether that’s being driven by the weather operations or some type of basis or curtailment issue. So again, we’re going to add some more data, and supplement that invoice data to allow us to go a bit deeper. We’ve added the modeled generation again to this top chart. And then we’ve also added the nodal prices for this project to the bottom chart. And on what we see now very quickly is that yes, in fact, this project is experiencing a basis challenge and experiencing low nodal prices and that is resulting in a reduced output from the project as well. So you can see in each of these highlighted areas, the project’s actual output is much below the green line, the modeled output, and that tends to coincide with prices – very low nodal prices, which is the green line on the bottom.
And again, this is something where sometimes there are basis sharing provisions in the more recent tranche of contracts that we’re seeing. But most contracts, this isn’t something that’s going to define a settlement outcome. But it is an important insight to start building so that you can understand the performance of your existing contracts, and then use that to guide future decision making processes as well.
I’ll say a few words quickly on how REsurety helps our customers track, audit, and explain outcomes. And sort of build on those issues that we were just noting. And then finally, we’ll wrap up with a summary and some recommended actions that we believe everyone can follow in order to streamline this process.
So the Portfolio Tracker is the software tool that I mentioned earlier, we take a software plus experts approach. And our software does a few key things. And these are aligned with the recommendations of best practices that we think everyone should try to follow in some way, whether that’s with a REsurety software or not.
So the first is visualization, you want to take in all of that data that you’re getting in various different places, and visualize that in order to start gaining those insights and understand what’s driving outcomes. It consolidates data into one location, and for the reasons Irina was mentioning, that’s important if you’re receiving lots of different data, lots of different times during the month, in lots of different formats. Gives you one place to go to see all of that information in one place. There’s an independent calculation of the contract settlement. And that allows for quick checking against the values that are in the invoices and the statements that you receive from the project. We’re able to add those insights through our weather-based modeled generation time series. And then finally, we have a team here who can support our customers in additional ways through auditing and explaining the results each month as needed. So to wrap things up, we’ve highlighted three areas of challenge on the left here, and then we have some recommendations for best practices going forward. So I think we’ve established that tracking and auditing monthly invoices for a buyer can be challenging, there’s a large amount of data. The outcomes of these contracts are highly variable and difficult to understand and diagnose. We’ve also seen that invoice errors are relatively common. I think, in our experience, almost every contract that we have worked with and analyzed has experienced some type of issue at some point during its lifetime. And so these issues do happen, but they also often go unnoticed. And that’s because there’s limited time for processing this data and really digging deep and so an efficient approach is definitely needed there. And then the final point is that the standard invoice data typically doesn’t give buyers the insights that they need to be able to really explain what’s driving settlement outcomes for their contracts. And so having in things like a weather-based modeled generation time series is really important and insightful. Being able to look at nodal prices to understand whether a project is experiencing basis issues is also really important, but not part of a standard invoicing deliverable. So with that, some recommendations that we have. The first is, and this might sound like I’m stating the obvious, but really double checking those invoice calculations every month really is important. As I said, we see that almost every contract has some type of issue, whether it’s small or large, at some point during its lifetime. And so it really is important to have some robust auditing process in place. As we mentioned earlier, there are certain points where you want to pay extra attention, so I would say that’s when a new project is coming online, but also around the times when the clocks change every year. Those are definitely the points where we see most of those errors popping up. Visualizing the invoice data is really an important step and allows you to dig very quickly a level deeper than just those aggregated metrics that you get in the standard statements and invoices. And then also trying to go a level deeper on looking at operational performance to understand to what extent availability and other types of bases or curtailment issues are affecting a project, help you build insights and make smarter informed decisions in the future as well. So that’s all we have in terms of content. So I think with that, we will move to the Q&A. And I guess as we do that, just a reminder that if you do have questions about anything we’ve covered here, please do send those in. And we’ll start addressing questions now.
Irina: Great, I see a couple of questions so far. Here’s one that is one of my favorites. So we have years worth of settlement data in some of our contracts. Do you also process that and provide any insights into longer term trends? Like is basis getting worse over time? So absolutely, I think one of the, as Carl mentioned, around consolidating the information, that it’s not just each month, you end up having aggregated data going back to the beginning of the contract. And so there can be something like you signed a contract a few years back for a wind project in Texas, maybe not expecting it to be the superstar of the portfolio, but then in recent months, all of a sudden, it seems to always be outperforming some of the other projects in the portfolio. And you might be wondering, well, is this really an actual trend? What’s going on? So that’s something we’ve run into, and we can definitely provide the data to help with communicating internally that, yes, a wind project in Texas has been benefiting from the last few years from solar build out, for example. And so wind value has increased as more solar has been coming onto the grid. Conversely, maybe you have a project somewhere else like SPP, and there’s been more and more wind buildout, which has been reducing the value of wind generation in certain areas. And so maybe a contract there that you thought would be a better performer is now consistently not performing as well. And so we can definitely take all that data that’s now in one place for all of the projects across time, and do some analysis that can help not just understand what to expect from the projects existing in the portfolio, but also how are you going to use that to inform procurement decisions moving forward. So there’s definitely value to getting the data right, but that’s just step one. And then the fun part is actually processing that data. So the next question is – I think, Carl, maybe this one can be for you. Does REsurety also forecast settlement? So I know that wasn’t a topic of the webinar but I think now’s a good time to address that one. Carl, do you want to take that one?
Carl: Yeah, so I think there’s two components to forecasting that we think about. And I guess the first thing is, the answer is yes. REsurety does provide that as a service. But just to break it down into the two components of that workflow that I see, because they’re handled differently. The first is, at the end of each month before the buyer receives information from the project. They need to, in many cases, provide an estimate to their accounting team for how they expect the settlement of that contract to play out when those invoices are in and payments are made – that monthly accrual process. So that’s something where we can support that through the use of that modeled generation data that we were looking at. So that’s available almost up to real time. And so at the end of each month, we’re able to look at how the weather has played out at each project in your portfolio and use that as a means of estimating for settlement. Of course, when the metered generation comes in, for all the reasons we were just talking about, the reality can be a bit different than that, but at least it takes out the weather variability from that accruals process. The other forecasting aspect is more long term forecasting, so trying to budget and account for how you expect a contract to settle in the future. And the approach that we take to that is using something we call Weather-Smart modeling. So we have a fundamentals-based model that predicts power prices out into the future. And we also model scenarios across a wide range of different weather conditions as well. And that can give you a forecast of how you might expect each contract to settle given average normal conditions, but also more extreme weather conditions as well. So we have a combination of different fundamentals cases as well as different weather cases as well.
Irina: Great, thanks Carl. I see one more question about the calculation of the modeled generation and is it based on a default setup – wind turbine model and height – or does it let the user choose a setup corresponding to the project? So yes, it basically is the setup corresponding to the project for the portfolio tracking. We do model if the information is available based on the actual wind turbine at the site based on the actual hub heights at the site. And it is meant to represent a model specific to that project. Is there anything, Carl, you want to add to that particular case?
Carl: Yeah, just to say that we use all available information to be able to build the best model we can. Obviously from a customer perspective, the best outcome is that that model is as accurate as possible. And so we take all the information we can to do that and make it project specific. So I don’t see any other questions coming in so I think that means it’s probably time for us to hand back to Hannah in just a second. But I guess firstly, I’ll just thank everyone for their time. Thank you for coming. Thank you for listening. We hope it’s been informative. And yeah, thank you again.
Hannah: Thank you. Okay, great. Thank you so much for that presentation. It was so informative and we just really thank REsurety for being here today. Sorry, my screen is not wanting to work with me right now. Alright, here we go. Alright. So don’t forget that our energy customer calls are recorded and loaded onto Interconnect, our member portal. So you can always check – oh sorry, by energy customer calls, I mean Solutions Showcases. So you can go ahead and check back and review that recording and slide notes. And if you are a CEBA energy provider or service provider on this call today and you’re also interested in presenting your own Solution Showcase, please reach out to me I’m going to be scheduling 2024 calls in the upcoming months. So we’re looking forward to that. Our next call is going to be with Silicon Ranch on December 5th, 2023 at 2pm Eastern Time. This one is going to just be open to energy customers, so our CEBA energy customers, and you can find the link to register on Interconnect. Also, coming up is going to be our CEBA at VERGE conference. So we hope all of our CEBA members and beyond are attending. That is going to be on October 23rd through the 26th in San Jose, California. As a reminder, if you are a CEBA member, you will receive VERGE tickets as part of your membership, which will give you access to all six programs at VERGE this year. You also have access to CEBA-only content like our energy customer morning and our member reception. So if you are a CEBA member make sure you get registered through Interconnect as well. And with that, I hope you all had a wonderful afternoon. Thank you again to our incredible speakers from REsurety and I hope you all have a great day. We will be sending out that post-event email within the week and thank you so much.
Return to the event page main menu.