Wednesday, February 12, 2025

Modeling is the key to navigating the energy transition

David Wilson, CEO, Energy Exemplar

The energy sector is in flux. The war in Ukraine has caused European gas and coal prices to surge, and there is the added pressure of facilitating a green energy transition. Organizations must meet international targets, such as those outlined in the Paris Climate Agreement, and domestic ones, such as the UK’s 2050 Net Zero. With every year the climate crisis intensifies, and with it, the pressure to accelerate the transition to green energy. An organization’s main challenge, in the face of such challenges, is navigation. How to chart a course towards a green future without sinking the ship in the process?

The answer is modeling. But not just any modeling. The traditional approach won’t cut it. Static modeling, which emphasizes the state of things at a fixed moment in time, is vulnerable to human error, and generating multiple scenarios, showing how this or that decision or change will play out, can take weeks. The data itself isn’t nearly reliable enough to give an organization the confidence to move forward. And that means organizations that rely on static modeling will struggle to arrive at the right mix of solar, wind, nuclear and hydrogen energy sources necessary for the green transition.

But there’s good news. We have something much better than static modeling, something that’s as close to a map of the future as it’s possible to get: fundamental modeling. It uses the physics of a material asset to predict how changing circumstances will affect it over time. Take a car. IF you understand its structure and workings—its shape, fuel consumption, and top speed, for instance—you can make predictions about how it will react in different scenarios. The physical generates a digital twin—a replica of itself in a virtual world.

Digital twins have been used in manufacturing and engineering for decades. But thanks to relatively recent advancements they can now be rendered with pinpoint accuracy. Entire physical energy systems can be converted into mathematical problems, and the best course of action can be chosen from a range of options. You can gather a wealth of insights by changing the inputs—adding more assets to the equation, for example. You have something like a crystal ball.

Fundamental modeling allows us to study such complex things as the interaction of weather patterns, the impact they have, the duration they take place over, and the ability of the storage and flexibility of the systems to respond. Thanks to it, variations in supply, demand and pricing over ten minutes or even ten years can be predicted. As the planet hots up and the global energy infrastructure adapts, this kind of modeling lets you make the kinds of calls that don’t leave you stranded or adrift, uncertain of what move to make next as you look towards the green horizon.

This kind of advanced modeling is vital to onboarding new energy sources, too. Take hydrogen, which now needs to be adopted in sectors where it has traditionally been almost completely absent, such as transport. Previously ignored in discussions around sustainability, the most common element in the universe is rapidly getting greener. “Green hydrogen”—created through electrolysis, which separates it from water—has been touted as the fuel of the future. Such predictions might be premature. But the fact is that in just a few years, our grids and infrastructure will have to be prepared for hydrogen and other sources to enter the energy mix. Modeling is therefore key.

Georgia provides us with a useful case study. The country, for which roughly 70% of gross energy demand has been supplied by imports for decades, adopted Energy Exemplar’s PLEXOS modeling software so it could assess how the introduction of renewables would affect its transmission network. The success of its green energy transition is an example to other countries looking to increase their renewable portfolio and, like Germany, decrease their dependence on foreign imports.

It’s not just Georgia. Countless other countries and organizations are using advanced simulation technology to figure out the way forward. The UK’s National Grid, now reliant on modeling to make sound decisions in a messy world, is just one example. They understand, as more and more people are, that with the climate crisis intensifying, and the energy sector in flux, instinct or what worked before isn’t going to cut it. Advanced, fundamental modeling is key. With a crystal ball at our disposal, the future doesn’t look so uncertain after all.

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