There’s a reason the uptake of renewables has been a slow and arduous process in the United States. I don’t believe it’s for lack of want or willingness, but rather due to major challenges related to the nation’s aging power grid. Many oil and gas companies are signing on to some sort of energy transition road map. Some predict it sooner, some later, but all agree, the transition will happen.
Renewables will be part of our future energy equation, but they will require adaptation on the part of grid operators before they can find their way into the mainstream U.S. power supply.
What any power grid values more than anything is balance and flexibility.
A grid needs to be resilient — to withstand the unpredictable elements that can throw it off balance. You don’t want blackouts, and you don’t want grids overloaded. Renewable energy sources such as wind and solar are unpredictable by nature. They are dependent on the wind blowing and the sun shining.
But this in itself is not reason enough to avoid or delay the integration of renewables into the U.S. power mix. Rather, it is a challenge to be addressed. The U.K. has found its way toward renewables, with 40% of the nation’s electricity (as of Q1 2020) supplied by greener sources, pushing fossil fuels further along in its eventual decline on the isles. With a stormy (literally) start to the year, the Britons found themselves with overwhelming output from their wind farms, raising concerns about a potentially overloaded grid. The solution to this is greater flexibility and more reliable storage solutions. With the help of data, this is fully possible to achieve.
Data from the grid gives utility companies glimpses into patterns.
However, too much data — from too many sources, in multiple formats — is of no use. The data simply isn’t in shape to provide the flexibility and predictability that renewables crave. With disconnected data sources, looking into your operations is much like peeking into a house, window by window. You only get a glimpse at what is right in front of you. But with the smarter use of data, you can see inside the entire house at once.
Imagine that same house as your data platform. The roof is removed, and you have a bird’s-eye view of all that’s going on inside. Having this information in one place, from multiple sources, allows you to make the right decisions at the right time. This helps you better predict the potential issues that may arise in the house.
On top of this, now imagine that you connect the data from your house to everything that is happening in the neighborhood around you. This external data puts everything in context, taking into account external factors that can alter patterns within your own house in the near future.
We call this flexible grid operations, and it’s essential as you transition to renewables.
When you bring renewables into your power mix, you can no longer simply peek through the windows to see your operations. You need to see everything at once and in context. One example of this concept is being pioneered by Statnett, one of my company's clients and a Norwegian state-owned operator of the nation’s power grid, in a country with a huge portion of its power supplied from renewable sources. Statnett found that it’s possible to reduce the time needed to extract and process grid data by at least half.
Statnett saw the futility in making decisions in a vacuum. After extensive research and development work, software packages now help Statnett workers retrieve data in one-twentieth of the time it took in the past, enabling them to mitigate outages, connect power sources to the grid faster and operate the grid more efficiently.
Adani is another good example of a company that is simultaneously pivoting to an immense commitment to renewable energy sources while executing on an aggressive digital and data-driven strategy to optimize its operations not only at the asset level, but throughout its complex interconnected network.
Today, we have the technology at our disposal to make renewables a viable option for any power grid.
I'll leave you with three to-do items for companies looking to digitalize their operations and begin preparing their systems for data platforms.
1. Take control over your operational data.
First, ensure that your data can be liberated from its current silos. In modern systems, you need a well-documented programming interface (API) to connect to your operations and ensure that you're not blocked from accessing the data that your organization generates by licenses or hidden egress fees. In traditional systems, this might mean upgrading licenses or requesting development kits. After this, identify where value can be created, and develop use cases to roll out.
2: Look for a flexible, open and approachable solution.
An operational data management solution needs to be flexible to grow with your operations. It needs to be open to allow you to work with partners, suppliers and a whole ecosystem. And it should be approachable to satisfy the many data creators, curators and consumers within your organization.
3: Prioritize speed, and avoid lock-ins.
There are several solutions available to you, depending on your volume of data, your type of data and the size of your organization. Ultimately, you should prioritize a software product over bespoke solutions to ensure a speedy evolution and avoid costly lock-ins.
By fusing data sources, taking a more contextualized approach and giving the end users a single platform to use, the threat of an overwhelmed grid is much less palpable. Renewables are on the rise, and it’s just a matter of time before they will play an even larger role in the world’s power supply. It’s time to preempt the unpredictability this may cause and get ready. The data is there; we just need to take the roof off the house to truly understand what’s going on inside.
By Team Cognite
Cognite is a global industrial SaaS company that supports the full-scale digital transformation of asset-heavy industries around the world. Our core Industrial DataOps platform, Cognite Data Fusion™, enables data and domain users to collaborate to quickly and safely develop, operationalize, and scale industrial AI solutions and applications. Cognite Data Fusion codifies industrial domain knowledge into software that fits into your existing ecosystem and enables scale from proofs of concept to truly data-driven operations to deliver both profitability and sustainability.