The journey toward a clean energy economy is complex and filled with technical, economic, and social challenges. Electricity models are key tools for driving this transformation, providing precision and insight into the potential outcomes of energy policies and technological shifts. At the Environmental Defense Fund (EDF), we are working to make these tools more accessible through the U.S. Model Intercomparison Project (MIP), which brings together leading developers of open-source planning models to help steer the nation toward a sustainable energy future.
What is Electricity Modeling
Solar panels on a residential house in Florida with the sun just starting to rise
Electricity capacity planning models use mathematical optimization to answer key questions about how to design a reliable power system under various scenarios. These models are crucial for understanding how changes in electricity demand, policy implementation, and technological advancements can affect everything from electricity costs to environmental health.
For example, we can learn more about how changes in electricity generation, one of the largest sources of air pollution in the U.S., might impact a community’s exposure to air pollution. They provide a foundation for advocates and decision-makers in government and industry to identify what power system design would be most cost-effective under a particular set of policies, such as business-as-usual or proposed renewable energy subsidies or targets. Utilities and regulators can then identify investments that take best advantage of the clean energy policies that are already in place, and policymakers can assess whether additional policies are needed to meet the challenge of climate change.
The Impact of the U.S. Model Intercomparison Project
You’ve probably seen many different studies of the U.S. power sector, all touting different findings about the preferred technologies, cost of decarbonization, need for transmission lines, etc. Different study results and conclusions can spring from inherent differences in how the models are written, or from different choices researchers make about how to configure the models. For example, two different modelers could make two different assumptions about which energy technologies will be available in future scenarios. Faced with divergent results, it can be difficult or impossible to assess whether they arise from the assumptions used or from fundamental differences in the mathematics of the models themselves. But that’s just what U.S. MIP aimed to explore.
The authors of several cutting-edge power system planning models—Switch, GenX, TEMOA and USENSYS—conducted a rigorous intercomparison between them, looking for areas where they agree or disagree about key electricity planning questions. In areas where they disagreed, the teams worked together intensively to understand why and to assess whether the disagreement reflected genuine uncertainty about policy questions or whether it was just due to different assumptions in setting up the model.
We’re still tabulating the results, but one thing is clear: on almost every point, differences were due to different input assumptions, rather than different models taking a fundamentally different view of the optimal design of the grid. Put another way, when these models are run with the same data—assumptions about technologies available, future costs of equipment and fuel, future policies, etc.—they generally agree very closely about the best path forward for the power sector and what it will cost.
Knowing that all models are created more-or-less equal, we see several benefits:
- These electricity models are “fit for purpose,” in the sense that they reach the same conclusions from the same data, despite different histories and mathematical formulations. Choosing one model vs. another is not likely to skew the findings from a study.
- On the other hand, the project showed that model configurations—assumptions and choices made when setting up and running the models—are important and can strongly affect the outcome of studies with these models.
- Modelers worked hard to link all four models to PowerGenome, an open framework for national electricity data, and to verify that this arrangement worked correctly. These links and the models themselves are all available for free download. This creates a suite of turnkey, peer-reviewed, transparent solutions that any stakeholder, from the smallest advocacy group to the largest regulatory commission, can use and adapt to help understand the best way forward for their power system.
The U.S. MIP is pivotal in identifying how best to use electricity planning models to advise on energy transition strategies. With this analysis, EDF is committed to advancing these tools and the insights they provide—and making them accessible to everyone. Our efforts to build and support a network of modelers, policymakers, and planners are designed to cultivate a comprehensive understanding of the challenges and opportunities inherent in decarbonizing the U.S. energy grid.
Case Study: Using Electricity Modeling for a 100% Renewable Hawaiian Grid
Through practical applications, EDF’s modeling tools have a history of helping to shape effective policies and strategies.
After seeing how the Switch model (and RESOLVE, a proprietary model based on Switch) could provide better power system designs for renewable and storage capacity planning, the Public Utilities Commission of Hawaii instructed Hawaiian Electric to use this type of next-generation optimization model for their planning. This has helped put them on the path to achieve their ambitious climate goal of making their grid 100% renewable at a reasonable cost by 2045. These tools have informed significant decisions in the energy sector, helping to devise solutions that are not only environmentally sound but also equitable and economically viable.
Broadening the Horizon: From Grids to Communities
One of the key expansions of the MIP’s scope is to enhance our understanding of how energy transitions affect diverse communities, particularly those historically underserved. This aspect of the project highlights the social implications of energy decisions, ensuring that the move toward renewable sources benefits all sectors of society.
For example, out of the box, these models don’t necessarily prioritize equity in employment or health outcomes. However, we are finding that with extra attention, it’s possible to use them to find solutions that make pollution and employment impacts more equitable (and better overall), at minimal extra cost. In addition, by democratizing access to these powerful modeling tools, we open a realm of possibilities for greater community involvement and informed public discourse on energy planning.
The Road Ahead
As we face the formidable challenge of climate change, the need for innovative solutions to transform our energy systems becomes ever more pressing. However, with continued innovation and collaboration in energy modeling, we can craft strategies that are just, efficient and cost-effective. EDF remains dedicated to enhancing these tools, advocating for open access to modeling resources, and promoting stakeholder engagement across the energy landscape.
To learn more, join our Climate Week NYC virtual event on September 26 at 3:00 PM ET. We’ll explore how to use these models effectively for advocacy and planning.
Later this year, we will release papers highlighting U.S. MIP’s methodology, energy policy findings, and conclusions about configuration choices or model features that are most important for answering key questions about the future of the power sector.
This blog was authored by Aurora Barone, senior economics and policy analyst, and Matthias Fripp, Associate Vice President, Modeling, Mapping and Analytics.