An interview with Lee Taylor of REsurety on REsurety’s REmap tool
Low energy demand during the coronavirus shutdown, combined with low natural gas prices and high renewable generation, has resulted in unprecedented low power prices in multiple U.S. markets, according to a new information services product that harnesses a massive project performance dataset and suite of financial analytics that industry leaders use to make long-term asset and contract decisions for wind and solar energy. REsurety’s new REmap tool is discussed by REM with Lee Taylor, the company’s founder and CEO.
How did REsurety and REmap get started?
I completed research in business school on managing weather risk in renewables, with the idea that when and how much the wind blows and the sun shines, rather than the cost of natural gas and coal, was becoming the driving force of power market risk management. However, the majority of the tools required to comprehend and manage this new risk were unavailable.
REmap is the start of a new chapter for us. We discovered that a lot of the data and analytics we built to support an insurer holding the weather risk of renewable energy projects could be useful to a broader audience – for a C&I buyer to evaluate RFP bids, a developer to decide where to focus greenfield development, and an investment bank to advise on the buy side or sell side of an M&A opportunity.
How is the information in the dataset gathered in the first place?
With a great deal of effort. Independent system operators’ acronym soup, as as ERCOT in Texas. NASA publishes weather information. On a monthly basis, the Energy Information Administration publishes data on how projects are performing. Electronic quarterly reports are available from the Federal Energy Regulatory Commission. The FAA is aware of the location of wind turbines, among other things.
The initial stage was to integrate all of that data and have it communicate with one another. Because manual reporting isn’t perfect, a lot of it comes down to data quality.
Then there’s the proprietary modelling to go along with it. For example, you might know that in June 2017, a specific plant produced 100 megawatt-hours. That’s not very helpful in determining the value of those 100 megawatt-hours, because the electricity price fluctuates every hour, so the hours in which those 100 megawatt-hours were really generated can indicate whether June was a good or bad month for that project.
To credit those 100 megawatt-hours to the particular hours they are likely to have been created, we employ our knowledge of turbine technology, solar panel technology, and everything else that goes into how a given hour of weather converts into fuel and electricity. After that, you may create measures for congestion, value, hedgeability, and so on. REmap is based on the combination of assimilating public data and then supplementing it with proprietary modelling.
What stories has the tool gathered by pulling power prices from wind and solar so far?
We begin with a map of the United States that depicts every climate zone where the weather resource yields a consistent power value, and each dot represents a hypothetical wind or solar facility. If we wish to look at operational projects, we can see that every dot on the screen now represents a real-world project.
Every month, the data and tool are updated, bringing fresh information: What is California’s reaction to increasing solar power? What part does solar penetration play in Texas? As we emerge from the coronavirus economic shutdown, how quickly is the rebound impacting one market vs another?
The combined combination of cheap natural gas costs after a warm winter, lower global demand from coronavirus, and relatively strong wind output in the lower Midwest is one tale that has emerged from this. Early in the year, the price of power was already rather low at $12 per megawatt-hour, but by April, the average value of power in regions of western Oklahoma, for example, had dropped below $2 per megawatt-hour. This is really unprecedented. In April, the Northern Illinois region of PJM also hit an all-time low.
Given the factors this tool has picked up, which areas of the country are currently enjoying low power prices because of those factors?
Almost the entire country is currently experiencing inexpensive power prices. And if you’re enjoying them, you’re most certainly a power consumer rather than a producer.
Whether you’re a wind farm, a solar project, or a gas plant, power prices are cheap across the board, but they’re especially low in some locations. Oklahoma’s $2 per megawatt-hour example is excessive. However, you can find all-time lows or close to all-time lows in numerous sections of the country, thanks to low natural gas prices and low demand due to the economic effects of the coronavirus.
These two factors have an impact on the overall energy system, but in the renewables environment, they can be neutralised or accentuated depending on how well your generation timing matches up with hour-by-hour lows or highs in power pricing. In most regions, except perhaps along the Texas coast, wind is less valuable than baseload. Solar is very cheap in California, but quite expensive in regions like Texas, where daytime electricity prices are greater than evening power prices.
This is why, from a revenue standpoint, developers are increasingly looking to build wind projects in areas where it’s not particularly windy, and solar projects in areas where it’s not particularly sunny, because the value is so low in areas where a given technology type already has a high penetration.
Who can use this tool currently, or to whom are you providing the information that it generates?
At the moment, it’s available to any paying customer or anybody who wants a trial.
As the use of these kinds of tools and information grows, what are the long-term implications for the clean energy industry?
The sector has changed dramatically when we first began the company in 2012. You developed wind projects where it was windy and where a larger utility motivated by policy was going to acquire your power for 20 years at your interconnection point in the early days of the renewables industry. Similarly, for solar, simply go where it’s sunny.
As the renewables market has matured and grown in popularity, the math has become much more sophisticated.
Renewable energy is becoming financially self-sufficient. I’m sure we can have a lively discussion about the importance of policymakers in pricing, the negative externalities of coal and gas plants, and the function of the Production Tax Credit in wind, but renewables are now winning due to economics.
That’s terrific, but it also means that, at least in the United States, a lot of the complexity and risk that was previously socialised to customers through utility procurement of renewables has gone to private entities.
You have buyers who are motivated by sustainability, such as Microsoft, who are acquiring power from massive amounts of renewables. “Does the wind blow during the hour when my data centre is running?” they’re now asking. And, unless I invest heavily in batteries, if the electricity markets shift and I have a lot of demand at night, my solar plants are unlikely to supply that.”
The magnitude and complexity of the hazards that must be handled in terms of congestion have substantially changed. This necessitates a significant shift in the knowledge that decision-makers require to comprehend and manage those risks.
Our goal with this tool is to improve decision-making by providing better data. That’s where we envision REmap fitting into the renewables market’s continued progress.