Utility scale developers of solar, wind, and storage power plants compete in an industry with razor-thin margins, sky-high capital expenses, and drawn-out timelines. Every new development is a gamble that reveals its odds only after years and millions of dollars. Given the historic nature of the industry, most of the tools used to evaluate these developments are artefacts of the 20th century. However, "clean tech" branding has drawn the attention of big-data savvy firms entering the data-rich industry with a strong competitive advantage. If conservative energy companies don’t adopt best data practices, their high development costs will kill any hopes of future project success.
It is time development firms realize they are big data companies.
Data Driven Development
The goal of development is to minimize project risk by minimizing uncertainty. A perfectly developed project would know exactly how much energy it will produce and exactly what its costs will be (along with being fully permitted and ready-to-construct). To reduce risk, developers use "data driven development." They generate an insane amount of data, ranging from traditional formats like historical wind speeds and irradiance to less organized formats, like engineering reports or permitting documents. Data from all these sources gets compiled in the minds of the decision makers, who then predict a project’s economics. This prediction must have enough evidence backing it to convince other key stakeholders as well (like financiers looking to lend money to the project or utilities looking to buy the project). Developers succeed by reducing risk sufficiently on economic projects at the lowest cost possible.
Reducing development expenses typically means generating less data (because you pay for fewer studies). With less data, a project must have higher risk. So, there is a limit to how effective a development firm can cut a project’s costs, and they must look to innovations in construction, technology, and performance to gain an edge. Unless, of course, you had a way to decrease project uncertainty enough for the key stakeholders without needing to get more data. What if less data didn’t mean higher project risk? What if you could gain more insights from the data you already have?
Big Data Innovations
In the world of "Big Data", tools exist that can extract untold wonders from the data we generate, and rich data sources are like gold mines waiting to be stormed by the data science "49ers." One would be hard-pressed to find a richer source of untapped data than project developments. However, most of this data goes unused by traditional developers. Here is an example:
Example Development
Let’s say a developer is considering bidding its "Bertrand" solar farm into some upcoming Requests For Proposals (RFP: when a company interested in buying power from a solar farm asks developers to send them projects to pick from). To decide whether to pay the price to bid this project and if there are any studies it should kick off to further reduce development risk ahead of time, the developer holds a "meeting of the minds." There are a lot of factors that go into these decisions, and to prepare for this meeting, different departments throughout the company are asked to summarize their findings for each key component of the project’s development.
For one such component, an engineer reads a preliminary geotechnical study to assess the risk that the site’s soils will drastically increase construction costs. This engineer must skim through a several-hundred-page document to pull out the key information, which she then presents to her manager, who relays the information to her manager, and so on up until it is presented in the meeting. The study that generated this document cost hundreds of thousands of dollars and took several months to complete. All this time and expense translates into one piece of knowledge the decision maker digests among many other points before making the decision about the RFP.
If you’ve ever head of cognitive bias, or if you’ve ever played the game "telephone", you know that the decision made from this process will suffer from a lot of "noise" distracting from the correct "signal." The decision maker is not capable of making an unbiased decision on the project, the Data presented likely misses the nuances it warrants, and the entire process was timely and expensive. Meanwhile, the development firm has mountains of relevant data that aren’t being used in this decision. Data savvy firms confront all these problems.
Better Development
To reduce the costs of this project’s development, a firm can pull in all the data already generated for nearby projects, along with relevant publicly available datasets. To reduce the uncertainty and bias when making decisions, machine learning models can offer data-driven suggestions. And to reduce miscommunication, all of a project’s data can be accessible and summarized conveniently in one location.
The dashboard below presents a ranking system to compare a project to other nearby developments, displays the project timeline and upcoming deliverables, and even estimates the probability that the project will win upcoming RFPs. This information can be used by decision makers to know what development tasks should be kicked off and which RFPs to bid into or avoid. Most importantly, every data point generated through a project’s development cycle gets incorporated into the pool of data that impacts the entire development portfolio, reducing the need for future studies.
This example only scratches the surface of what’s available to big-data driven developers. Those firms that also operate power plants could combine operating and development data to unlock even greater insights, and independent power producers bidding into electricity markets provide another rich data source to bring into the mix.
Conclusion
Right now, effective data management and analysis offers a huge competitive advantage over current utility scale developers, but this will be short-lived as the low costs and high benefits of an improved data platform scream for rapid adoption. Sooner than later, proper data handling will become an industry necessity. In what has become a hallmark of the energy industry in the 21st century, those companies that are slow to adapt will be quick to fail.
If you are a development firm, it is time you realize you are a big data company. Give yourself the tools you need to succeed and train your energy experts to let their data work for them. For another example of how Data Science can benefit your team, you may be interested in reading this article. You can find a roadmap of free resources that will level up your team here. At the very least, talk to your team about better ways you could be using your data. You will be amazed by the low-hanging fruit available to you.
