This Research Digest summarizes key findings and implications of the recent publication, “Uncertainty-Aware Grid Planning in the Real World: A Method Enabling Large-Scale, Two-Stage Adaptive Robust Optimization for Capacity Expansion Planning.”
Capacity expansion models are frequently used to inform multi-billion dollar grid infrastructure decisions, a context in which there is significant uncertainty surrounding the future need for and performance of such infrastructure. However, despite much academic literature on the topic, virtually no grid planning processes use capacity expansion models that endogenously consider uncertainty, an oversight which frequently leads to short-sighted infrastructure decisions. This is partially due to a technology transfer gap, but it is also due to a lack of methods that work at large scale. In this paper we introduce a method for endogenizing uncertainty into capacity expansion models, a variant of adaptive robust optimization, that addresses this gap. We apply the method to a real-world capacity expansion planning problem, that of the State of California, and compare its performance to that of traditional adaptive robust optimization. We find that both the traditional method and our method identify increased transmission investment as a key lever for increasing robustness and adaptability, while helping to avoid downside risks that current deterministic planning processes may be exposing ratepayers to. Our method performs similarly to the traditional method in terms of outcomes, while significantly reducing computational complexity, making it scalable to real-world planning problems.
Scheduled maintenance is likely to be lengthy and therefore consequential for the economics of fusion power plants. The maintenance strategy that maximizes the economic value of a plant depends on internal factors such as the cost and durability of the replaceable components, the frequency and duration of the maintenance blocks, and the external factors of the electricity system in which the plant operates. This paper examines the value of fusion power plants with various maintenance properties in a decarbonized United States Eastern Interconnection circa 2050. Seasonal variations in electricity supply and demand mean that certain times of year, particularly spring to early summer, are best for scheduled maintenance. Seasonality has two important consequences. First, the value of a plant can be 15% higher than what one would naively expect if value were directly proportional to its availability. Second, in some cases, replacing fractions of a component in shorter maintenance blocks spread over multiple years is better than replacing it all at once during a longer outage, even through the overall availability of the plant is lower in the former scenario.
Industrial sectors such as cement and chemicals are central to economic development but account for a growing share of global CO₂ emissions. Green hydrogen and carbon capture, utilization and storage (CCUS) are widely viewed as essential options for deeply decarbonizing these “hard-to-abate” industries, yet their high costs and infrastructure needs remain major barriers to deployment at scale. New opportunities emerge when green hydrogen production is coupled with oxy-fuel cement kilns and CO₂ capture, so that by-product oxygen and captured CO₂ are reused within an integrated co-production system. Here we show how industrial symbiosis between cement production and methanol synthesis can create bidirectional molecular flows and lower costs. We combine an hourly resolved plant-level optimization model with CCS source–sink matching to explore system configurations, flexible operation under variable renewable electricity, and plant-by-plant deployment pathways for China’s cement fleet. Our results show that co-producing clinker and green methanol cuts costs significantly relative to decarbonizing cement and methanol separately, while reshaping the national CO₂ network. Beyond this specific case, the framework shows how coupling hydrogen, CO₂ and O₂ flows across sectors can inform infrastructure planning and circular-economy policy for industrial decarbonization.
Improved modeling approaches to support decision making are needed to help accelerate the transition to low-carbon energy systems. We offer insights into improving energy transition decision support modeling, drawing on our experience with the Net-Zero America (NZA) project and analogously designed ongoing projects led by researchers in Australia, Brazil, China, India, Republic of Korea, and Poland. The NZA study was impactful because of 1) uniquely high spatial, temporal, sectoral, technological, and socio-economic modeling and visualization using objective, transparent, and scientifically rigorous methods and 2) complementary policy-actionable bridging analyses informed by transparent and unusually extensive engagement with a broad set of stakeholders. The NZA approach established a new standard for high-resolution energy-transition decision support modeling, but further improvements in methods and tools are needed. A foremost need is improving optimization modeling so that it better reflects on-the-ground realities and risks associated with efforts to deploy energy technologies and infrastructure at the scales and pace needed to achieve mid-century net-zero emissions goals.
Large-scale deployment of direct air capture (DAC) will lead to significant demand for heat and electricity. Supplying heat and electricity can result in significant emissions if served by carbon-intensive sources of energy. This is a particular concern because DAC is capital intensive and likely to be run at close to maximum output. This makes it challenging for DAC plants to be powered solely by cheap, intermittent, clean sources of power such as wind and solar.We undertake an interdisciplinary study combining process engineering with a detailed macro-energy system optimization model to evaluate the site and system-level costs of combining high-temperature thermal energy storage (TES) with DAC. TES has the ability to decouple the timing of thermal consumption and power generation, allowing DAC’s thermal loads to be served through electricity from intermittent renewable energy. We compare solid sorbent-based DAC plants combined with TES to solid sorbent-based DAC facilities with grid-powered heat pumps. We use the region of Texas as a case study. We find that DAC plants with TES are roughly 3% more expensive but incentivize greater investment in clean electricity sources on the power grid, resulting in substantially lower indirect emissions. As a result, the net cost of carbon removal for DAC with TES, after accounting for indirect emissions, is up to 30% cheaper than DAC facilities with grid- powered heat pumps. Overall, we find that the indirect power system emission impacts from deployment of DAC are not trivial and can range from 10%–25% of gross DAC removals. Coupling DAC with TES can eliminate these indirect emissions.
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.
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.
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.