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.
Clean hydrogen can play a pivotal role in decarbonizing ‘hard-to-abate’ sectors. However, it cannot be universally adopted given energy-intensive production, high costs and limited near-term supply. It is therefore critical to prioritize clean hydrogen demands that deliver the most cost-effective emission reductions. Here we evaluate the decarbonization potential and economic costs of clean hydrogen in ten carbon-intensive sectors around 2035. Steelmaking, oil refining and ammonia production emerge as ‘no-regret’ sectors, offering the lowest levelized carbon mitigation costs, whereas hydrogen use in on-road and rail transport is economically unfavourable compared with direct electrification. Although policy support is needed to make clean hydrogen cost-competitive, production incentives are not an effective policy lever for prioritizing its use in ‘no-regret’ sectors. Uniform supply-side subsidies risk misallocating limited clean hydrogen supply and public funds. We conclude that sector-specific demand-side policies are required to align priorities and fully realize the decarbonization potential of clean hydrogen.
Imminent blast furnace relinings create a consequential investment window for U.S. primary steel decarbonization. Using plant-level capacity planning and stochastic operations modeling, we quantify the costs, infrastructure, and fiscal requirements to transition legacy blast furnace sites to direct reduction under supply-side incentives. Under baseline assumptions, post-subsidy premiums above average blast furnace costs, including relining, are 272 to 427 dollars per ton of steel for near-zero-emission hydrogen-based direct reduction and 115 to 181 dollars for natural-gas-based production. Representative facilities producing two million tons of steel annually at legacy locations require approximately 4 to 6.5 and 0.7 to 1.1 GW of renewable generation and battery storage, respectively. This translates to 65 and 12 GW across the unrelined fleet for hydrogen- and natural gas-based pathways, respectively. These findings connect replacement timing to costs, infrastructure, and public support, motivating a sequenced transition through clean-powered natural-gas-based direct reduction to avert blast furnace relining, reduce emissions by 57 percent relative to blast furnaces, and preserve options for deeper decarbonization through hydrogen substitution or carbon capture.
Net-zero transitions are expected to reduce ambient fine particulate matter (PM2.5) concentrations and the associated mortality burden. The magnitude and distribution of these health effects are substantially affected by energy technology choices and the resulting precursor emissions that contribute to ambient PM2.5. On the basis of a multi-model analysis using three leading energy system models for the United States (US-REGEN, GCAM-USA, and REPEAT), we demonstrate that a net-zero transition yields robust nationwide reductions in ambient PM2.5 concentrations and associated mortality. Under the net-zero scenarios, national PM2.5-attributable mortality rates in 2050 declined by 28–73% compared to 2017 across the three models. This result confirms substantial health benefits from reducing energy-sector pollution, despite concurrent population growth and aging trends that place upward pressure on mortality. Differences across the models result from how CO2 mitigation efforts are allocated across sectors and associated energy technology choices. For instance, the scale of carbon dioxide removal deployment in 2050 varies by a factor of three across the net-zero scenarios developed by the three models, influencing the amount of fossil fuel use that remains in a deeply decarbonized future and, consequently, the precursor emissions contributing to ambient PM2.5. By exploring sensitivity analyses that vary the projected emissions from non-energy activities such as wildfires, we also find that potential future increases in non-energy emissions can offset energy-sector health benefits.
High shares of distributed energy resources (DERs) transform distribution systems into active participants in integrated transmission and distribution (ITD) operations. Linear models enable scalable distribution-level flexibility aggregation but can misclassify AC feasible operating points, whereas direct nonlinear aggregation becomes costly, especially in multiperiod ITD coordination. This paper reformulates transmission-distribution coordination within a hierarchical optimization framework and introduces a non-iterative predictor-corrector aggregation method. By leveraging path-following techniques from real-time optimal control, the approach achieves tractable computation with guaranteed error bounds. Across 24 radial distribution-network cases and seven meshed variants, including the real KIT Campus North grid, the proposed method yields substantially lower sampled false- and lost-flexibility rates than linear surrogates and a convex relaxation. On two 24-period ITD testcases, the formulation reduces end-to-end wall-clock time by factors of 6 relative to the corresponding centralized formulation, primarily through dimensionality reduction.
In many countries declining demand in energy-intensive industries (EIIs) such as cement, steel and aluminium is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource wasteful, it could unlock EIIs’ flexibility in electricity use. Here using China’s aluminium smelting industry as a case study, we evaluate the system-level cost–benefit of retaining EII overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminium smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China’s decarbonized electricity system by 23–32 billion CNY per year (11–15% of the aluminium smelting industry’s product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also create labour complementarities between the aluminium and thermal power sectors.
Technical Report, National Laboratory of the Rockies, 2026
Dasun Perera, Greg Schivley, Jesse Jenkins, Qian Luo
This report describes a U.S. Department of Energy (DOE) Office of Geothermal-funded project to investigate how data center cooling loads can be managed by Cold Underground Thermal Energy Storage (Cold UTES). The project was led by the National Laboratory of the Rockies (NLR) in collaboration with Lawrence Berkeley National Laboratory (LBNL), the University of Chicago, Princeton University, and industrial advisors. Several modeling methodologies were used to encompass a range of modeling techniques and assumptions. The intent of this work is to describe the methods and results of the analyses and provide a broad consensus on the value and opportunity for Cold UTES for data center cooling.
Expanding domestic production and implementing demand-side strategies can substantially narrow material shortages for US electric vehicle batteries, but these efforts still fall short of meeting projected 2035 demand. To assure US battery supplies, the USA must support investments across all domestic supply chain stages, reduce and shift battery material demand, and continue to secure international supplies.
To address supply shortfalls in the electric vehicle battery supply chain, US policy has prioritized expanding domestic production. However, we find that this strategy alone cannot meet future material demand. If announced projects materialize, domestic production can meet 2035 demand for raw lithium, lithium carbonate, lithium hydroxide, electrolytes and separators used in major battery chemistries. However, many other materials still face substantial and persistent gaps even with demand-side strategies, including improved material efficiency, enhanced battery recycling and battery chemistry shifts. Shortfalls of ~30–70% persist for upstream cobalt, graphite, nickel and their midstream refined materials. For some of these materials, gaps may widen because ~30–100% of projected domestic supplies rely on early-stage projects, which may not advance. Downstream, cathode and anode active materials face ~15–75% shortfalls. Therefore, securing US battery materials will require investment across the domestic supply chain, reductions and shifts in material demand and international sourcing.
We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the abstraction is flexible and can effectively capture complex hierarchical connectivity that arises from decision-making over multiple spatial and temporal scales (e.g., integration of planning, scheduling, and operations in manufacturing and infrastructures). We also show that the graph abstraction facilitates the conceptualization and implementation of decomposition and approximation schemes. Specifically, we propose a graph-based Benders decomposition (gBD) framework that enables the exploitation of hierarchical (nested) structures and that uses graph aggregation/partitioning procedures to discover such structures. In addition, we provide a Julia implementation of gBD, which we call PlasmoBenders.jl. We illustrate the capabilities using examples arising in the context of energy and power systems.