Advanced geothermal systems have the potential to deliver significant U.S clean electricity by 2050, using innovative drilling and well stimulation. While traditionally providing continuous “baseload” power, these systems are shifting towards flexible generation to compete in markets with increasing variable renewable energy (VRE). This study explores the potential of future geothermal plants with engineered geothermal reservoirs for flexible, load-following generation and energy storage. Using a linear optimization model based on reservoir simulations, the study evaluates plant operations and investment decisions against electricity price trends. Findings reveal that geothermal plants with operational flexibility and in-reservoir energy storage can significantly increase market value, up to 60% more than conventional baseload plants. These reservoirs provide large, efficient energy storage, enabling both short and long-duration storage, ideally during high-price periods. The study’s sensitivity analysis across various subsurface and cost scenarios underscores the enhanced value of flexible geothermal energy in markets with high VRE penetration.
A recent article by Sovacool et al. used cross-sectional regression to examine the relationship between clean energy deployment and national carbon dioxide (CO2) levels. They reported that nuclear energy deployment isn’t significantly linked to lower CO2 emissions, unlike renewable energy, questioning nuclear power’s effectiveness in reducing emissions from fossil fuels. This study critically reviews Sovacool et al.’s claims and methods, identifying several limitations. It conducts a reanalysis using the same data and time frames but with revised cross-sectional and more robust panel data analyses. The findings contradict Sovacool et al., showing that both nuclear power and renewable energy are associated with lower per capita CO2 emissions, with similar magnitude and significance. Sensitivity analysis confirms this association is resilient to potential omitted variables, indicating that both nuclear power and renewable electricity contribute significantly to reducing CO2 emissions.
The Inflation Reduction Act (IRA) significantly impacts the U.S. energy sector, particularly the PJM Interconnection. It’s projected to increase PJM’s clean electricity to 60% by 2030, up from 48% without the IRA, necessitating faster renewable energy and transmission growth. The IRA could reduce PJM’s CO2 emissions by 37% from 2019/2021 levels, but emissions may rise post-2032 without extended policy support. The Act also lowers electricity costs in PJM. Deeper decarbonization in PJM will require more rapid expansion of low-carbon resources, possibly including advanced technologies like carbon capture and storage (CCS) or long-duration storage by 2035. Implementing a clean electricity standard and CO2 emissions cap and trade could help achieve up to a 90% CO2 reduction by 2035, while maintaining or lowering electricity costs.
This report assesses the role of electricity transmission in enabling the full emissions reduction potential of the Inflation Reduction Act (IRA). Previously, REPEAT Project estimated that IRA could cut U.S. greenhouse gas emissions by roughly one billion tons per year in 2030 and reduce cumulative greenhouse gas emissions by 6.3 billion tons of CO2-equivalent over the decade (2023-2032).1 That outcome depends on more than doubling the historical pace of electricity transmission expansion over the last decade in order to interconnect new renewable resources at sufficient pace and meet growing demand from electric vehicles, heat pumps, and other electrification. While our modeling finds this outcome makes economic sense, current transmission planning, siting, permitting and cost allocation practices can all potentially impede the real-world pace of transmission expansion. We thus model the impact of constrained growth in U.S. electricity transmission on emissions outcomes and the pace of renewable electricity expansion under IRA.
This study aims to assess the most cost-effective pathways for New Jersey to achieve 100% carbon-free electricity, in line with its current laws and policy objectives. It explores the potential roles of in-state solar PV, offshore wind, nuclear power, and imported electricity in meeting the state’s future electricity needs. The study provides an independent analysis of the costs and trade-offs associated with different strategies, offering insights for decision-makers. Using the advanced GenX electricity system optimization model, the study plans investment and operational decisions to meet future electricity demand within engineering, reliability, and policy constraints at minimal cost. It models the electricity system of New Jersey, the PJM Interconnection, and neighboring regions, covering 15 zones in total, to evaluate various policy, technology, and fuel price scenarios. The goal is to find feasible options for New Jersey to achieve a completely carbon-free electricity supply by 2050.
In electricity systems with many variable renewables, flexible operation of natural gas combined cycle (NGCC) power plants with carbon capture and sequestration (CCS) can increase their economic value. This study evaluates NGCC-CCS plants with solvent storage for such operation, using a modular modeling framework for accuracy and efficiency. The model divides NGCC-CCS plants into subcomponents, applying linear constraints for energy and mass balances, and addressing unit commitment (UC) constraints in thermal power plants. This approach employs linear relaxation of UC decision variables alongside a generator clustering method for flexible CCS modeling. Integrated into a power system model, it shows the hourly operations of NGCC-CCS and impacts on system performance. The results indicate faster computational times than traditional binary UC methods, with minimal errors. Flexible NGCC-CCS plants reduce operating costs, especially during peak demand, proving beneficial in grids with increasing renewable energy.
GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape. GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints. GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning.
The Biden administration aims to cut U.S. greenhouse gas emissions by 50%-52% below 2005 levels by 2030 and achieve net-zero emissions by 2050. These goals are attainable but require a collaborative, nation-building effort involving federal, state, local governments, the private sector, and communities to rapidly transform the U.S. energy system. While macro-scale energy modeling studies inform net-zero planning, they often lack the granularity needed to address real-world challenges. The Net-Zero America (NZA) study stands out for its detailed approach, offering spatial, temporal, and sector-specific pathways. These pathways rely on highly technologically ready solutions (Technology Readiness Level 6 or greater) and can achieve emissions targets akin to the Biden administration’s goals by 2030 and net-zero emissions by 2050. The study reveals similarities among pathways over the next decade, with annual spending on energy services as a percentage of GDP remaining relatively constant throughout the transition.
Achieving net-zero greenhouse gas emissions in the U.S. by mid-century requires a transformation of the energy workforce. This study examines the impact of increased labor compensation and domestic manufacturing on renewable energy technology costs, the overall cost of transitioning to net-zero emissions, and labor outcomes related to utility-scale solar photovoltaics (PV) and wind power. This study reveals that labor cost premiums and higher domestic content in wind and solar PV supply chains result in relatively minor increases in capital and operating costs. These cost increases can potentially be offset by improved labor productivity. Additionally, the study finds that technology cost premiums associated with labor-friendly policies have a minimal impact on renewable energy deployment and the overall transition cost to a net-zero emissions economy. Public policies, including tax credits and workforce development support, can help redistribute technology cost premiums to benefit both firms and workers in the renewable energy sector.
In decarbonized electricity systems with significant variable renewables, having at least one firm electricity generation technology is essential for reliability and cost reduction. These firm resources operate year-round and include low- and zero-carbon options like flexible resources (e.g., biogas or hydrogen combustion), capital-intensive resources (e.g., nuclear and geothermal), and intermediate resources (e.g., natural gas with CCS). This study explores nuclear, CCS, and zero-carbon fuel combustion roles in decarbonized electricity systems, showing their unique contributions. It analyzes data from three long-term electricity system models covering California and the U.S. Western Interconnection. Each firm technology significantly lowers costs compared to portfolios relying solely on renewables and energy storage. Having all these options optimizes utilization rates, reducing system costs by up to 10% compared to single firm resource scenarios. This analysis highlights diverse technology’s value in achieving emissions reduction goals while ensuring power sector reliability and affordability.