Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost $5.7–$7.3/W to gain an initial market share (assuming 30 year asset life and 3.5 %–6.5 % real weighted average cost of capital), while those that include thermal storage in their designs can cost up to $6.0/W–$7.7/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall ~32 % at 100 GW of cumulative capacity and ~51 % at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. These findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.
In 2022, the U.S. government passed unprecedented climate and social equity legislationthe Inflation Reduction Act (IRA) -designed to incentivize renewable and low-carbon energy deployment, promote domestic supply chains, and address labor and environmental justice concerns. In this study, we model the effects of the IRA on renewable energy manufacturing and development costs and deployment. We find that tax credits to encourage expansion of U.S. manufacturing are likely to generate comparative cost advantages for domesticallyproduced components across the utility-scale solar and wind supply chain relative to imported components. We also show that the bonus rate tax credits for renewable developers will decrease the U.S. average levelized cost of utility-scale solar (26-65%), land-based wind (43-61%), and offshore wind (16-19%) projects, even when accounting for uncertainty in inflation, domestic content of renewable components, and pass-through of component cost savings associated with the manufacturing tax credit to developers. Additional tax credits available to developers meeting energy community and domestic content share requirements further reduce costs for qualifying projects. We find that tax credits for renewable developers collectively have the potential to substantially increase the deployment of renewable infrastructure, drive demand for domestically-produced components, and foster workforce access, higher wages, and the retention of workers. A large share of renewable investments and capacity may flow to disadvantaged communities (27 and 46% for utility-scale solar and land-based wind projects, respectively), although inframarginal changes in development costs associated with place-based incentives, such as the energy community tax credit, may be insufficient to influence project siting decisions, given transmission constraints, spatial proximity to electricity demand, and renewable resource potential.
Climate change is expected to increase the severity of hurricanes and tropical storms, posing significant risks to the electricity grid. These include downed power lines, damaged solar panels, and impaired wind turbines from high winds. New York (NY) and New Jersey (NJ) are not spared from these vulnerabilities and must strengthen their infrastructure and mitigate social and technical impacts. Clean energy mandates, such as NJ’s Executive Orders No. 315 and 307 (100% clean energy by 2035 and 11 GW of offshore wind by 2040), and NY’s Executive Order No. 166 (40% emissions reduction by 2030), add urgency to ensuring grid resilience under extreme weather. This study demonstrates the Power System Cyclone Impact Model (PCIM), used alongside the GenX electricity system planning tool, to assess grid resilience under hurricane-induced high wind speeds in the NY and NJ region. Results reveal that onshore and offshore wind could contribute additional power during storms, provided transmission and storage systems remain operational. This output helps offset outages elsewhere in the grid across all storm categories. In contrast, solar emerges as a vulnerability due to combined impacts from wind stress and cloud cover, significantly reducing generation during and after storms. Thermal generators show the lowest failure rates, though this may partly reflect current model limitations, as only wind stress and cloud cover are considered, excluding hazards like flooding. Non-served energy costs vary with electricity demand and fluctuations in wind and solar output. July stands out as the most vulnerable month, due to high demand and limited wind generation, leading to higher NSE. This research provides a first step toward understanding storm-related grid resilience in NJ and NY. The PCIM model is designed to be generalizable, with future work focused on expanding its scope to include additional hazards like storm surge and flooding, and more storm-prone regions.
While electrifying transportation eliminates tailpipe greenhouse gas (GHG) emissions, electric vehicle (EV) adoption can create additional electricity sector emissions. To quantify this emissions impact, prior work typically employs short-run marginal emissions or average emissions rates calculated from historical data or power systems models that do not consider changes in installed capacity. In this work, we use an electricity system capacity expansion model to consider the full consequential GHG emissions impact from large-scale EV adoption in the western United States, accounting for induced changes in generation and storage capacity. We find that the metrics described above do not accurately reflect the true emissions impact of EV adoption–average emissions rates can either under- or over-estimate emission impacts, and short-run marginal emissions rates can significantly underestimate emission reductions, especially when charging timing is flexible. Our results also show that using short-run marginal emission rates as signals to coordinate EV charging could increase emissions relative to price-based charging signals, indicating the need for alternative control strategies to minimize consequential emissions.
This report presents a brief summary of the REPEAT Project’s analysis of the impacts of changes in federal policy proposed by the administration of President Donald Trump and the Republican majority in Congress on the U.S. energy transition. President Trump and Congressional Republicans have vowed to repeal and replace many of the legislative and regulatory policies created by the Biden Administration and 117th Congress to accelerate the transition to a cleaner U.S. energy system. In this report, we summarize the impacts of these repeals, including a range of executive actions and the impact of enacting H.R.1, the One Big Beautiful Bill Act of 2025—Republicans’ partisan budget ‘mega-bill,’ which substantially repeals nearly all of the tax credits enacted by the IRA to support clean electricity, fuels, vehicles and manufacturing.
In this paper, we present modeling to generate continuous alternatives (MGCA), an MGA post-processing methodology. MGCA can be utilized once an initial MGA exploration has been completed and consists of the following steps: 1. Dimensional reduction of the MGA data to capacity decisions at the generator cluster level and metric evaluations (defined as affine transformations of variables e.g. cost, emissions, land use). 2. Formulation of an exploratory problem which includes MGCA’s novel constraint implicitly encoding the convex hull of MGA solutions. 3. Utilization of the exploratory problem to explore the convex hull of MGA solutions via introduction of additional constraints and new objectives, ideally in an interactive setting. 4. Export and evaluation of discovered interpolated solutions (later referenced as interpolates) to an operational model to evaluate operational metric values consistent with economic dispatch. The key innovation of MGCA lies in a recognition that a convex hull can be fully described by the set of convex combinations of exterior solutions which can be encoded as a constraint in an optimization problem. MGCA introduces several new capabilities compared to previous work: 1. Implicitly define the shape of the convex hull of all investment decisions with full spatial and technical resolution as discovered by a previous MGA exploration. 2. Create new feasible solutions with estimates of their associated outcome metric values (note, Vågerö et al [25] simultaneously invented a similar methodology for estimating metric values). 3. Allow users to define new optimization problems within the identified near-optimal feasible space which solve in fractions of a second. The MGCA approach enhances the usability of MGA results by allowing utility planners and stakeholders to rapidly generate feasible capacity mixes which optimize over their constraints and objectives of interest with generator cluster-level detail and estimated operational characteristics.
This study performs a detailed intercomparison of four open-source electricity capacity expansion models—Temoa, Switch, GenX, and USENSYS—to evaluate 1) how closely the results of these models align when inputs and configurations are harmonized, and 2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.
Enhanced geothermal power, an emerging technology with the potential to deliver round-the-clock carbon-free electricity across a wide range of geographies, is receiving increased public attention following several years of rapid advances and successful field demonstrations. However, due to both the technology’s nascency and its reliance on geothermal resources that vary in quality across the country, its near- and long-term potential to compete with established sources of power and contribute to the decarbonization of electricity systems across the United States (US) is poorly understood. In this paper we first leverage recent field data to produce the first empirically grounded near-term cost projections for enhanced geothermal in the US. We then use these geographically granular cost data as inputs to an electricity system planning model to assess where and under what conditions enhanced geothermal power could see near-term commercial deployment. We finally utilize an experience curves framework—which assumes that enhanced geothermal costs will fall with increasing deployment—to explore how the cost and adoption of enhanced geothermal could evolve over time following any initial deployments. These deployment pathways are sensitive to variations in assumed near-term technology costs, learning rates, resource availability, and policy, but we find under baseline assumptions that EGS could plausibly contribute up to a fifth of total US electricity generation by 2050 and drastically reduce the cost of electricity decarbonization even in lower-quality resource areas east of the Mississippi—a much larger role for the technology than has been previously assumed. Such a substantial role is contingent on significant reductions in the cost of enhanced geothermal wellfields, which in turn must come as a result of advances in reservoir design. These findings suggest that efforts to facilitate early deployment and better understand uncertainties in enhanced geothermal’s cost and learning potential should be key near-term policy and development priorities.
This report summarizes REPEAT Project’s analysis of the impacts of changes in federal policy proposed by the administration of President Donald Trump and the Republican majority in Congress on the U.S. energy transition. President Trump and Congressional Republicans have vowed to repeal and replace many of the legislative and regulatory policies created by the Biden Administration and 117th Congress to accelerate the transition to a cleaner U.S. energy system. If completed, these repeals may reshape the U.S. policy landscape once again. On May 22, 2025, the U.S. House of Representatives passed the partisan budget bill, H.R. 1, which substantially repeals nearly all of the tax credits enacted by the Inflation Reduction Act of 2022 (IRA) to support clean electricity, fuels, vehicles and manufacturing. The bill also rescinds all unobligated funding for clean energy and climate programs enacted by the IRA and the Infrastructure Investment and Jobs Act of 2021. The potential impact of H.R. 1 is thus substantively similar to the Full Repeal scenario assessed in this report. We also assess the impact of executive actions alone and compare outcomes to a continuation of current Biden-era policies and a net-zero pathway benchmark scenario.