Energy industry's transformation hinges on modeling: A look at how modeling impacts the shift towards sustainable energy solutions.
Got a heads-up from Joe DeCarolis, the boss man at the U.S. Energy Information Administration (EIA)? He's got some golden wisdom for you energy folks out there.
"Wanna know my secret? Don't fall for the numbers," DeCarolis said during MIT Energy Initiative's (MITEI) new speaker series, MITEI Presents: Advancing the Energy Transition. "Models spit out these pretty numbers for ya, makes you feel all confident and such. But don't get too comfy, keep those skeptic goggles on."
This event brings the MIT community together with gurus and leaders who got the lowdown on what needs to be done to speed up the energy transition.
DeCarolis's talk, titled "Stay humble and prepare for surprises: Lessons for the energy transition," wasn't about downplaying models' importance. No way! Models provide a framework that helps stakeholders weigh up today's decisions against potential future scenarios. It's just that he emphasized the need for acknowledging uncertainty and not treating these projections as crystal balls.
"We can use models to help make decisions, but we know the future ain't set in stone," DeCarolis said. "We account for that uncertainty, making adjustments to keep us on the right path."
Now, you might think, why don't we just use models to make some sweet energy predictions? Well, DeCarolis thinks otherwise. "When models are used to make forecasts, the results are usually disappointing," he said.
For proof, look no further than the false past projections about the growth of nuclear energy in the United States. But there's still lots of cool uses for energy models. Instead of relying on them to tell us the precise future of energy consumption and prices, DeCarolis suggested using them to spark dialogue and support decision-making.
"Models can help us think and speculate about the energy future," DeCarolis said. "They can start engaging stakeholders in conversations about complex issues. This way, we can start exploring the model, recognizing key source of uncertainties, and highlighting knowledge gaps."
To better understand the uncertainty involved, DeCarolis suggested looking at past projections and their differences from actual outcomes. Big changes like the exponential growth of shale oil and gas production or the rapid increase in wind and solar energy caught modelers off guard. These surprises led to inaccurate projections, with estimated CO2 emissions turning out to be higher than what actually happened.
To illustrate the unpredictability of energy outcomes, the 2023 edition of the AEO introduced "cones of uncertainty." These ranges of outcomes represent different levels of historical errors, with the goal of capturing any bias in the projections. But DeCarolis warned that past errors don't necessarily apply to the future.
Looking ahead, DeCarolis said he's got a ton of things keeping him awake at night as a modeler. These include the impacts of climate change, how renewable energy demand will evolve, industry and government obstacles to clean energy infrastructure, technological innovation, and data center energy demand.
"What about hydrogen? Geothermal? Fusion?" DeCarolis asked. "Should those be in the model? We're working on a next-generation energy system model, Project BlueSky, to make our models more adaptable, modular, and accessible."
Key takeaways? Remember that there's always uncertainty in energy modeling, and models shouldn't be treated as crystal balls. Incorporating probabilistic modeling, diversifying data sources, using advanced analytics, addressing uncertainty, avoiding over-reliance on model forecasts, and utilizing cloud computing are all best practices for energy modelers to keep in mind. Stay wise, stay humble, and prepare for surprises!
- Joe DeCarolis, the head of the U.S. Energy Information Administration (EIA), encourages energy professionals not to give excessive weight to numerical model projections.
- DeCarolis's talk at MIT Energy Initiative (MITEI) stressed the importance of acknowledging uncertainty in models and not treating them as predictors of the future.
- Models provide a framework for assessing today's energy decisions in light of potential future scenarios, DeCarolis clarified, but they should not be considered as crystal balls.
- DeCarolis suggested that energy models could be used to stimulate dialogue and support decision-making, rather than make precise predictions about energy consumption and prices.
- The lack of accuracy in energy models can be seen in the historical underestimation of nuclear energy growth in the United States.
- As we look to the future, factors such as climate change impacts, renewable energy demand evolution, industry and government obstacles, technological innovation, and data center energy demand are keeping DeCarolis up at night.
- The 2023 edition of the AEO introduced "cones of uncertainty" to better represent the range of possible outcomes in energy modeling, but past errors do not guarantee future accuracy.
- Best practices for energy modelers include incorporating probabilistic modeling, diversifying data sources, employing advanced analytics, addressing uncertainty, minimizing over-reliance on model forecasts, and utilizing cloud computing.
