Climate change poses significant challenges for policymakers, whether their focus is on national defense or agriculture. But it raises particularly thorny questions related to energy policy. Where should we focus our efforts in order to limit the effects of climate change while still providing reliable energy to users? Where should we invest our infrastructure dollars?
The answers to these policy questions will obviously affect the near-term future of the energy system in the United States, which encompasses everything from power generation to transportation, fuel supply and manufacturing. But these policy decisions will also shape the course of global climate change for generations. That makes it critical for policymakers to make informed decisions. What results might a given policy have? What would happen if they invest in Option A instead of Option B?
Computational models can project the likely outcomes associated with various policy decisions. But these models – and the information that goes into them – have their own limitations. And it is important for these tools to be well-studied in order to give policymakers the best possible information about the likely impact of different policies.
An international team of researchers from a range of disciplines has launched a project called the Open Energy Outlook that aims to address these challenges. To raise the visibility of their effort, the researchers recently published an open access article in the journal Joule. The paper describes the benefits of distributed and collaborative energy modeling teams, which is the approach Open Energy Outlook is taking. To learn more, we spoke with Joe DeCarolis, co-founder of Open Energy Outlook and a professor of civil, construction and environmental engineering at NC State.
The Abstract: Broadly speaking, what is the Open Energy Outlook project hoping to accomplish?
Joe DeCarolis: The ultimate goal of the Open Energy Outlook is to inform U.S. energy and climate policy efforts aimed at reducing carbon emissions. To do so, we plan to utilize computer models to rigorously examine technology pathways that reduce or eliminate carbon emissions across the whole U.S. energy system. A novel feature of our effort is a focus on models, tools, and datasets that are all open source and thus freely available to the public. With this transparent approach to modeling, we hope to build a community of scholars around this effort.
TA: Can you lay out what modeling means in this context? How it’s used, and how it might be used?
DeCarolis: We don’t have a way to run real-world experiments on the global energy system, so we use computer models to project the deployment and utilization of energy technologies across the energy system under different future scenarios. By examining similarities and differences across many different scenarios, we can derive insights that can inform policy.
Climate scientists tell us the increase in global average temperature change should be limited to a maximum of 1.5-2 degrees Celsius relative to the pre-industrial era. In order to meet that target, we need to achieve net neutral carbon emissions from the global energy system sometime around the middle of this century. How are we going to meet this massive challenge? We can extrapolate existing market trends into the near future without formal models. For example, we can expect to see higher deployments of wind, solar and battery storage, along with increasing electrification across the energy system, including battery electric vehicles. But there’s a big gap between these short-term expectations and the end-game of carbon neutral energy systems. That’s where the models come in handy – they help us explore detailed future pathways that get us to zero emissions.
As an example, electricity systems with a high share of renewables will need to be able to store energy for a long time, since there’s a lot of variability in energy production from season to season. That is well beyond the short-term duration (i.e., 2-8 hours) provided by lithium-ion batteries.
One option for addressing this challenge would be to electrolyze water to make hydrogen, which we can store in large amounts. But then we’d need to use hydrogen directly as fuel, or cost-effectively convert it back into electricity or into other fuels for use across the energy system.
Another option might involve a high share of nuclear power generation in the electric sector, which doesn’t require storage. That would have to be coupled with a high degree of electrification across the transportation, building and industrial sectors. We can run energy system models under different assumptions to observe how these different options affect energy technology deployment, cost and carbon emissions.
TA: And this type of modeling is your area of expertise, right?
DeCarolis: Yes. My research focuses on the development and application of energy system models. At NC State, our team developed Tools for Energy Model Optimization and Analysis (Temoa), an open source energy system model. We developed Temoa for two reasons. First, we wanted to enable repeatable analysis. Anyone should be able to download our source code and data and replicate published results – it’s a fundamental tenet of science. Second, the model is designed to perform different types of sensitivity and uncertainty analysis. This aspect is crucially important, as future uncertainty in the energy space is very large, and we need to take it into account when thinking long-term about policy, technology deployment, emissions, and costs. Temoa is serving as the key model underpinning the Open Energy Outlook.