Michael R. Davidson

Nuclear reactors are often modeled as inflexible baseload generators with fixed downtimes and restrictive ramping limits. In practice, however, operational flexibility for a reactor is coupled with its fuel-cycle. A critical physics constraint arises from xenon poisoning, in which the buildup of the neutron absorbing xenon following a power ramp-down suppresses core reactivity, thereby limiting power maneuvers and reactor restarts. Existing power system models represent these effects using static constraints, neglecting the crucial dependence of xenon-induced inflexibility on the instantaneous state of a reactor core. This study offers a physics-informed unit commitment framework that embeds fuel-cycle dynamics into dispatch modeling. Our approach tracks the reactivity margin and enforces core-state-dependent constraints on allowable minimum generation and restart downtimes, while endogenously scheduling refueling outages. To demonstrate the formalism, we apply the framework to a representative nuclear fleet operating alongside high shares of renewable energy and storage. The results show that operational mode significantly affects system outcomes; flexible nuclear operation slows reactivity degradation, extends fuel-cycles by up to 10%, reduces renewable curtailment, lowers production costs, and delays the onset of refueling outages. These findings highlight the importance of fuel-cycle-aware flexibility modeling and provide a computationally tractable approach for assessing the role of flexible nuclear power in decarbonized grids.

China’s coal sector is the largest contributor to the country’s greenhouse gas emissions and a major cause of air pollution, which claims over 1 million lives annually. Long-term climate commitments include a target to achieve carbon neutrality by 2060, which entails between a 60–90 percent reduction in coal use by mid-century. Of the many challenges associated with this transition, addressing the dislocation of coal and fossil fuel workers is perhaps the most challenging. While there are recognized efforts to achieve a “just transition” that works for fossil communities and workers, practices to date in China do not fully prioritize these groups. The closest analogue to what will need to be achieved is the supplyside restructuring of mostly state-owned coal and other heavy industries concentrated in 2016–2018 and continuing to this day. The coal sector has shed over 2 million jobs since its recent peak of 2013—and, in contrast to earlier state-owned enterprise (SOE) reforms, did not result in widespread social unrest. Lessons were learned from earlier periods, and the increased institutionalization of labor contract law is a possible argument for the relatively smoother workforce reduction mandated by overcapacity reduction. In this brief commentary, we explore the workforce reduction strategies during this period and the legal basis for various practices adopted by SOEs.

Acute temporal impacts and subnational limitations can hinder a country’s decarbonization pathway, despite national planning efforts. China, as the world’s largest greenhouse gas (GHG) emitter, has announced an ambitious climate policy goal of achieving carbon neutrality by 2060, which will require an unprecedented scale-up of low-carbon energy technologies. China’s variable renewable energy (VRE) deployment is historically imbalanced with large geographic concentrations driven by resource endowment and institutional heterogeneities. If continued, this pattern can run into deployment limits and exacerbate challenges associated with socio-economic benefits distribution, threatening the ability to timely integrate VRE. We develop a capacity expansion model with grid operational detail and high spatial resolution to examine decadal pathways to carbon neutrality by 2060 considering localized and temporal impacts. Over these four decades, we find that all regions will increase deployment rates of renewable energy, first driven by the use of high-quality resources, and later by coal retirement and electricity demand growth. The share of provinces with high deployment pressure, where deployment requirements exceed historical rates, increases from around 45% to 100% by the final decade. If carbon capture and storage (CCS) is not available, maximum annual average deployment rates will increase by 33% and occur a decade earlier. A more stringent 1.5 °C emission target leads to more acute temporal and spatial deployment pressures in the first decade, with VRE concentrated in regions with high-quality resources and demand centers and a doubling of new transmission capacity in the first decade. Effective national and subnational policy support is necessary to coordinate VRE deployment and facilitate transitions in impacted regions. • Built a power system optimization framework with high spatial–temporal resolutions. • Reaching carbon goals requires increasing renewable deployment rates. • Technology uncertainty exacerbates the temporal limitation issue. • Cost-efficient pathways may face acute deployment pressures at subnational levels. • Identified region-specific policy priorities over time to coordinate the transition.

To meet global climate targets, countries aim to triple renewable energy capacity and rapidly deploy other low-carbon technologies by 2030. We assess the critical mineral demand required to meet these goals using a bottom-up, scenario-based approach and examine how mineral bottlenecks affect sub-technology choices. Our analysis yields three key findings. First, annual demand for critical minerals is projected to rise 6-fold, from 4.7 million tons in 2022 to 30 million tons by 2030. Second, minerals such as natural graphite, cobalt, lithium, tellurium, indium, silver, aluminum, copper, and rare earth elements may face supply constraints. Third, specific sub-technologies depend heavily on certain minerals: cadmium and tellurium shortages could limit thin-film photovoltaics; indium scarcity may hinder perovskite tandem cells; rare earths are vital for permanent-magnet wind turbines; and lithium is a key for all-solid-state batteries. Improving material efficiency and advancing mineral-efficient technologies will be essential for a resilient energy transition.

Nuclear reactors are often modeled as inflexible baseload generators with fixed downtimes and restrictive ramping constraints. In practice, however, a reactor's operational flexibility is closely tied to its fuel cycle and associated reactivity margin. A key physical constraint for power maneuverability is xenon poisoning, caused from the transient buildup of neutron-absorbing xenon following a power reduction. This transient can delay or prevent subsequent power ramp-up due to suppressed core reactivity. Additionally, if a reactor is shutdown during periods of low reactivity, restart times can vary significantly, leading to prolonged downtimes. This work introduces a physics-informed modeling framework that embeds fuel cycle dynamics within a unit commitment (UC) formulation. The framework tracks reactivity margin, dynamically enforces xenon induced constraints, and endogenously schedules refueling outages based on core conditions. By capturing intracycle reactivity evolution, the model enables operation dependent nuclear dispatch that reflects both techno-economic requirements and irreducible nuclear physics limits. Application to a representative reactor fleet shows that flexible operation can slow reactivity degradation and extend fuel cycles. Results further demonstrate that different operational modes substantially affect VRE utilization, curtailment, and nuclear fleet capacity factors. These findings highlight the importance of fuel cycle aware flexibility modeling for accurate reactor scheduling and integration of nuclear power into energy system models.

Characterizing the morphology of lithium (Li) is crucial for developing long-lasting lithium metal batteries. It is well established that more uniform Li deposition correlates with better cell performance. Li morphology is often characterized through qualitative analysis of scanning electron microscopy (SEM) images; however, there are no widely accepted metrics to quantitatively describe deposition uniformity. Here, we propose a framework to quantify uniformity through SEM image analysis via the index of dispersion ( ID ) metric, which is defined and presented in the context of Li metal batteries. We also explore experimental impacts of sampling protocols on ID measurements. Our results demonstrate that the ID metric is highly sensitive to variations in deposition uniformity, including the coexistence and uniformity of multiple morphologies, uniformity within a single morphology, and particle size distribution uniformity. Furthermore, it is demonstrated that uniformity, as measured by the ID , can be related to the average potential of Li||Li symmetric cells over cycling. Higher capacity cycling leads to more pronounced changes in both ID and average cell potential. Local minima/maxima are found consistently in both ID and average cell potential immediately before cells short-circuit, which we suggest may indicate a collapse of the microstructure prior to failure. We put forward this framework as a more robust approach to quantify Li deposition uniformity, advancing the development of Li metal batteries that are safer and longer lasting.

As countries are releasing their 2035 nationally determined contributions (NDCs), we examine the renewable deployment requirements for China to meet its climate targets. We develop a power system model with high spatial and temporal resolutions to make optimal capacity expansion decisions for China's power sector through 2035. We find that 2,350–2,780 gigawatts (GW) of wind and solar will need to be deployed by 2030 and 2,910–3,800 GW by 2035 to be consistent with a 2°C global temperature rise target. Wind and solar shares in the generation mix can increase from 17.9% in 2024 to 41%–46% in 2030 and 49%–56% in 2035. A more robust climate action framework is proposed for China's power sector in its 2035 NDC.

Effective climate policy requires coordination among political jurisdictions with large differences in institutional make-up and interest group structures. We model the effects of realistic coordination barriers and policy heterogeneity on optimal low-carbon electricity pathways in the western United States using a capacity expansion model with operational and coordination mechanism details. We estimate the aggregate costs of fully coordinated power systems (ignoring extreme conditions) to be just a few percent lower than baseline (i.e., current) levels, noting that state-level differences can be an order of magnitude larger. Key infrastructure of a decarbonized power system that facilitates trade such as larger transmission networks is enhanced under multiple complementary coordinating policies, while select location-dependent investments such as energy storage and renewable energy respond more to the degree of market coordination. Degrees of regional coordination have limited impacts on firm low-carbon capacities, whose deployment is driven more by policy stringency, cost and availability assumptions. Climate and energy models should consider the political feasibility of different levels of market and policy coordination when evaluating optimal policy pathways.

This study explores how demand-side flexible resources (DSFR) contribute to China's power system transition toward carbon neutrality. We find that approximately 20% of the costs associated with the carbon neutrality transition would be reduced by incorporating DSFR accounting for one-fourth of peak load capacity. Such reduction mainly comes from substituting costly energy storage and flexible generation units with diversified low-carbon demand resources and reducing the lock-in of thermal units in the medium term. We find that flexible electric vehicle charging and power-to-hydrogen load contribute the most to flexible load demands among DSFR, assisting regional power balancing and renewable energy accommodation. Controllable power reserve capacity of demand response resources benefits less operating expensive power reserve resources, reducing the need for gas generation by 42% in 2060. We further find load demand potentials show great influences on transition costs and system morphology development, most notably flexible power-to-hydrogen load demands.

Public safety power shutoffs (PSPS) are a common pre-emptive measure to reduce wildfire risk due to power system equipment failure. System operators use PSPS to de-energize electric grid elements that are either prone to failure or located in regions at a high risk of experiencing a wildfire. Successful power system operation during PSPS involves coordination across different time scales. Adjustments to generator commitments and transmission line de-energizations occur at day-ahead intervals while adjustments to load servicing occur at hourly intervals. Generator commitments and operational decisions have to be made under uncertainty in electric grid demand and wildfire potential forecasts. This paper presents deterministic and twostage mean-CVaR stochastic frameworks to show how the likelihood of large wildfires near transmission lines affects generator commitment and transmission line de-energization strategies. The optimal costs of commitment, operation, and lost load on the IEEE 14-bus and 24-bus test systems are compared to the costs generated from prior optimal power shut-off (OPS) formulations. The proposed mean-CVaR stochastic program generates less total expected costs evaluated with respect to higher demand scenarios than costs generated by risk-neutral and deterministic methods.

Abstract Pressures to address climate change are eroding the privileged role coal has held in China throughout its modernization. Phasing down coal requires a suite of supply‐ and demand‐side tools to both reduce production (and therefore, maintain sufficiently high prices) and shift to coal alternatives across diverse consumption sectors. This review outlines contours of the coming coal transition by documenting coal's rise in modern China, its status in key debates of today's energy system, and the range of modeling scenarios of coal's future to mid‐century. In addition, through an analysis of current efforts and impacts in four transition policy areas—supply‐side, demand‐side, employment and social impacts, and stranded assets and fiscal revenues—it identifies gaps and future recommendations. Emerging scholarship on enhancing political feasibility and fostering a “just transition” needs to move beyond global cases and address highly localized and temporally bound impacts. This article is categorized under: The Carbon Economy and Climate Mitigation > Future of Global Energy Policy and Governance > National Climate Change Policy

China’s distinctive resource endowment, characterized by abundant coal, limited oil, and scarce gas, shapes the long-term development focus on coal power. However, the requirement for stable combustion imposes limitations on the deep peak-shaving flexibility of coal power. While previous studies have explored the technical feasibility of deep peak shaving for coal power, there is a scarcity of research addressing the delicate balance between the costs of flexibility retrofitting and the benefits of promoting renewable energy integration in power system. To this end, in this paper, we develop an optimal planning technology for the flexibility retrofitting of coal plants. The proposed technology employs a capacity expansion and dispatch model which formulate a flexibility retrofitting plan for coal-fired power plants and a configuration scheme for battery energy storage. Incorporating the proposed model and real-world data from power grid in Yunnan, China, we assess the decarbonization potential of coal fleet flexibility. The experimental results demonstrate that coal fleet flexibility effectively reduces renewable energy curtailment by 3.0% and decreases carbon emissions by more than 6 megatons compared to the coal fleet without retrofitting. Furthermore, the flexibility retrofitting of coal-fired power plants is projected to yield a 12.5% total cost reduction and a 11.1% decrease in coal power generation.

Lessons from the CubeSat and Mars Exploration programs may guide the infusion of robotics for planetary science and exploration.

Solar photovoltaics (PV) are a promising candidate for utility-scale use due to the low cost per mWh of silicon-PV and large available solar resource in the continental United States. At the utility scale, the last few years have seen a massive increase in deployment of Single Axis Trackers (SATs), which rotate a PV module east-west so that it always faces towards the Sun. However, the effect of SAT failure on the costs of utility scale PV is not well studied, mainly because data on tracker failure in field is either nonexistent or proprietary. We simulate tracker failure in a capacity expansion model to quantify the costs of theoretical failure modes, including tracker stall, improper backtracking, and other reasonable failure conditions. This work presents a capacity expansion model that quantifies the losses incurred by these failures, in terms of costs of required additional generation to meet future demand. We rank failures from most costly to least, and provide operational recommendations to operators to mitigate potential failure.

Public safety power shutoffs (PSPS) are a common pre-emptive measure to reduce wildfire risk due to power system equipment. System operators use PSPS to de-energize electric grid elements that are either prone to failure or located in regions at a high risk of experiencing a wildfire. Successful power system operation during PSPS involves coordination across different time scales. Adjustments to generator commitments and transmission line de-energizations occur at day-ahead intervals while adjustments to load servicing occur at hourly intervals. Generator commitments and operational decisions have to be made under uncertainty in electric grid demand and wildfire potential forecasts. This paper presents deterministic and two-stage mean-CVaR stochastic frameworks to show how the likelihood of large wildfires near transmission lines affects generator commitment and transmission line de-energization strategies. The optimal costs of commitment, operation, and lost load on the IEEE 14-bus test system are compared to the costs generated from prior optimal power shut-off (OPS) formulations. The proposed mean-CVaR stochastic program generates less total expected costs evaluated with respect to higher demand scenarios than costs generated by risk-neutral and deterministic methods.

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches- integrated assessment modeling, engineering-economic optimization, and agent-based modeling-with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

The politics of international finance for coal power plants have intensified since the 2015 Paris climate agreement was negotiated. Over the past few years, Japan and South Korea have signaled their intent not to fund new coal projects overseas, leaving China and its Belt and Road Initiative as the ‘financier of last resort.’ In September 2021, China too announced its intent to stop providing finance for overseas coal projects. What accounts for their decision to cease financing overseas coal projects despite prominent differences in political systems, degree of internationalization of their financial systems, and economic size? Drawing on datasets of coal projects and financing supplemented by case material and interviews, this paper explores the dynamics of coal export finance and how the combination of international reputational pressures and declining demand for coal finance diminished the domestic support for incumbent coal exporters in all three countries.