{"id":1880,"date":"2024-09-17T15:56:31","date_gmt":"2024-09-17T20:56:31","guid":{"rendered":"https:\/\/blogs.edf.org\/markets\/?p=1880"},"modified":"2026-04-28T08:45:22","modified_gmt":"2026-04-28T13:45:22","slug":"the-power-of-electricity-modeling-in-the-u-s-clean-energy-transition","status":"publish","type":"post","link":"https:\/\/blogs.edf.org\/markets\/2024\/09\/17\/the-power-of-electricity-modeling-in-the-u-s-clean-energy-transition\/","title":{"rendered":"The Power of Electricity Modeling in the U.S. Clean Energy Transition"},"content":{"rendered":"

The journey toward a clean energy economy is complex and filled with technical, economic, and social challenges. Electricity models<\/a> 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)<\/strong><\/a>, which brings together leading developers of open-source planning models to help steer the nation toward a sustainable energy future.<\/span>\u00a0<\/span><\/p>\n

What is Electricity Modeling<\/span><\/b>\u00a0<\/span><\/h3>\n
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Solar panels on a residential house in Florida with the sun just starting to rise<\/figcaption><\/figure>\n

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. <\/span>\u00a0<\/span><\/p>\n

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\u2019s 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.<\/span>\u00a0<\/span><\/p>\n

The Impact of the U.S. Model Intercomparison Project<\/span><\/b>\u00a0<\/span><\/h3>\n

You\u2019ve 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\u2019s just what U.S. MIP aimed to explore. <\/span>\u00a0<\/span><\/p>\n

The authors of several cutting-edge power system planning models\u2014Switch<\/a>, GenX, TEMOA and USENSYS\u2014conducted 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. <\/span>\u00a0<\/span><\/p>\n

We\u2019re 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\u2014assumptions about technologies available, future costs of equipment and fuel, future policies, etc.\u2014they generally agree very closely about the best path forward for the power sector and what it will cost.<\/span>\u00a0<\/span><\/p>\n

Knowing that all models are created more-or-less equal, we see several benefits:<\/span>\u00a0<\/span><\/p>\n