The biggest impact on a project’s economy is the wind resource. Hence, the more weather data available, the easier it is to make a go or no-go decision. What’s more, the 20-year power purchase agreement may be a thing of the past. Selling power from hour-to-hour may be here to stay. Those were a few of the comments from Todd Perry, Director of Valuation and Analytics at EDF Renewable Energy, at the recent AWEA Wind Resource and Project Energy Assessment Seminar. The question to which Perry responded was: What would you like to see in the wind data that would help make decisions? Perry’s answer revealed a side of the business many do not get to hear about and so make his comments insightful.
At EDF Renewable Energy, Perry is responsible for valuing all renewable-energy projects, whether a green-field development or acquisition. He runs economic models to help identify the value in proposed projects. “So basically, my job is to take information from resource assessments and capital-expenditure estimates, and put them in a spreadsheet to show senior management the risk/reward tradeoff presented by a particular project,” he said.
It takes a lot to make an assessment. “I’m on the investment side, or preconstruction, so I want to know things such as, what is our exposure or risk? We increasingly build projects with some component of merchant (market) exposure. You may not know this, but the single biggest risk in a project, of all the inputs to a model, is the wind resource. That has the largest impact on project economics, the greatest variability. So I want to know, how does the wind resource affect project economics, the merchant exposure, and how can we get more data to support decisions?” he asks.
Merchant exposure deserves some discussion. Not long ago, wind farm owners would sign a 20-year Power Purchase Agreement (PPA) with a utility and all the output generated by the project would go to the utility. “Today, those long term PPAs or off-take agreements are harder to come by. With improving technology, some things happen more frequently. One is that more projects are lasting longer, about 25 years, with five years of price uncertainty on the back end. And instead of a PPA, some owners sell into the merchant market in an hour-by-hour fashion. We take whatever the spot market price is.”
That opens projects to a lot of risk because the amount of power sold or the price received is unknown. “That is a hit-or-miss method for financing a project. We have projects that are fully merchant, meaning there is no assigned off-taker. We sell 100% of the energy into the market and we accept all that price risk. So merchant exposure refers to the uncertainty of the resource along with the uncertainty that surrounds the price we receive.”
So what might allow a better resource assessment? “Typically I receive a P50 estimate of the resource, for the 10- or 20-year annual energy output, in addition to a 12×24 (12 month x 24 hour matrix of average expected production), or 8760 data (estimated hourly production for a “typical year”) which gives more granularity on when that energy on average should be produced over a 20-year period. I care about 12×24 and 8760 data, especially with merchant exposure. These data are necessary but insufficient to our needs, so I just want to know more. I want more data and better data,” he said.
At another company, the standard procedure in the assessment group was a 30-year back cast of hourly data for the project at hand. “That is what I want, 30 or 50 years, as much hourly data as you can give me, back cast for the project. You might say ‘good luck with that’ and think it is useless. Not so. It’s useless if I’m trying to identify how much energy the project might produce in Hour Ending 7 of January 2017; those data will not tell me that. But they will tell how variable the estimate of production is in that period or hour, and that is useful information. I want to know how risky production is in that hour – how risky are the estimates. Typically, I get P-level estimates with an assumption of normality around the data. With an hourly, 50-year data set, I can figure out for myself what the hourly distribution of the data looks like, along with the distributions of the monthly and annual data.”
There are several reasons to care about such information. Production guarantees typical to PPAs mean that below a certain production, the company will pay penalties. How likely is it that production will fall below a minimal guarantee? “With a 20-year annual P50 figure, I cannot answer the question. But give me the data and I can answer the question with a historical back cast of annual production to see how often production falls below a certain threshold,” he added.
When sizing a project for tax equity, it’s important to know the loss, or the probability of meeting a certain threshold. “So we use the data for a lot of things and that is a main point. The more data, the more I can evaluate the risk of the project or resource in an effective manner – and convey useful information to senior management,” he said.
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