Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment, are widely used for technological cost forecasting. However, applying them to emerging technologies is challenging due to parametric uncertainty in learning rates, which are scarce and highly uncertain, and structural uncertainty stemming from multiple plausible learning frameworks. Using water electrolysis as a case study, we evaluate how different learning structures, from shared to fragmented learning across technology variants and regions, alter expected cost paths. We interrogate model assumptions that represent contrasting industrial realities, including competition among electrolyzer variants and supply chain fragmentation associated with protectionism and industrial policy. We find that plausible modeling choices generate widely different trajectories, with materially different implications for policy design and technology strategy. We argue for routinely applying multiple learning frameworks to explore decision spaces and stress-test conclusions for scale-up planning, national industrial strategy, and energy-systems modeling.
Green ammonia is taking center stage as a low-carbon marine fuel, driving the shipping sector’s net-zero transition. However, the ramifications of soaring demand for clean marine fuel on today’s carbon-intensive ammonia production and the spatiotemporal evolution of ammonia supply chains are far from clear. Here, we develop a technology-rich, multi-region model for China optimizing production routes, facility locations, and transportation under various policy and infrastructure scenarios. The results reveal that large-scale deployment of green hydrogen to ammonia could begin around 2030, cutting CO2 emissions by >90% by 2050, though supply costs will rise 6%–30% above 2020 levels. Pipelines could cut costs by 18% compared with trucks by improving supply-demand matching, and carbon prices above 100 USD/t CO2 are needed to make green ammonia viable as a marine fuel by 2060. Our findings offer a feasible green ammonia road map for China with strategic priorities for both policy and infrastructure development.
Generation and Transmission Expansion Planning (GTEP) problems co-optimize generation and transmission expansion, enabling them to provide better planning decisions than traditional Generation Expansion Planning or Transmission Expansion Planning problems, but GTEPs can be computationally complex or intractable. Benders Decomposition (BD) has been applied to expansion planning problems, with various methods applied to accelerate convergence. In this work, we test strategies for improving the performance of BD on GTEP models with nodal resolution and DCOPF constraints. We also present an alternative approach for handling the bilinear constraints that can result in these problems. These tests included combinations of using generalized Benders decomposition (GBD), hot-starting via a transport constrained model, using linear relaxations of the master problem, and using regularization. We test these methods on mixed-integer linear programming GTEP models with up to 146 buses (10 million continuous variables and 400 mixed-integer decisions). With selected accelerated Benders decomposition approaches, the problems can be solved to under a 1\% gap in as little as 5 hours where they were otherwise intractable. Results also suggest that using regularization on these initial hot-starting and relaxation steps and turning it off after they are complete was generally the best combination of strategies.
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.
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.”
Addressing climate change requires decarbonizing the iron and steel industry, which accounts for about 7% of global CO2 emissions. Hydrogen-based direct reduced iron with electric arc furnaces (H2-DRI-EAF) emerges as a promising pathway, yet its economic viability hinges on effectively managing the variability of renewable energy. However, heterogeneous flexibility potentials across electricity, hydrogen, iron, and steel production, together with the distinct characteristics of intermediate product storage, make coordinated capacity investment highly complex. This study develops a system-level modeling framework for renewable-centric, multi-stage H2-DRI-EAF that jointly optimizes capacity sizing and flexible operations across production stages under flexibility strategies in representative Chinese steelmaking cities. We find that fully deploying flexibility across all production stages could reduce the levelized cost of steel (LCOS) by 6–10% relative to baseline configurations relying on flexible electrolysis alone. By 2035, flexible H2-DRI-EAF can achieve near cost parity with conventional steelmaking under moderate carbon pricing ($38/t CO2) and 50% low-cost scrap addition. Flexibility systematically alters investment patterns through significantly reducing the required capacities of solar and energy storage while moderately inducing differentiated overcapacity in electrolyzers, DRI furnaces, and EAFs. For a 1 Mt/year H2-DRI-EAF plant, renewable deployment exceeds 100 km2; a PV-only configuration halves the land footprint but raises LCOS by $5–64/t. This study provides quantitative insights to support the scalable deployment of green steel pathways under high renewable penetration.
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.