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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

DeepClimGAN: A High-Resolution Climate Data Generator

Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally inten- sive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a com- promise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demon- strate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint proba- bility distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.

Extreme events, Machine Learning, deep learning↗
DOE OSTI2024

Microgrid design and multi-year dispatch optimization under climate-informed load and renewable resource uncertainty

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Evolution of Scenario Generation Capabilities in the ExaSGD Project

High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.

economic dispatch↗
DOE OSTI2024

Techno-economic analysis of renewable energy generation at the South Pole

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. Here, this study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all -diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

In this paper, we present mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Preventive Power Outage Estimation Based on A Novel Scenario Clustering Strategy: Preprint

The increasing occurrence of extreme weather events is challenging the power grid operation. In front of the extreme weather, the system operator is responsible for estimating the power outage and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The predicted vulnerable lines of an outage prediction model tool are utilized to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers the schedule of repair crews and mobile energy resources. Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative ones for straightforward analysis. Finally, case studies on a distribution system evaluate the damage level brought by extreme weather and verify the effectiveness of the proposed scenario clustering strategy.

mobile energy resources↗
DOE OSTI2020

Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks

Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we use deep learning in a proof of concept that lays the foundation for emulating global climate model output for different scenarios. We train two "loosely conditioned" Generative Adversarial Networks (GANs) that emulate daily precipitation output from a fully coupled Earth system model: one GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are trained to produce spatiotemporal samples: 32 days of precipitation over a 64x128 regular grid discretizing the globe. We evaluate the generator with a set of related performance metrics based upon KL divergence, and find the generated samples to be nearly as well matched to the test data as the validation data is to test. We also find the generated samples to accurately estimate the mean number of dry days and mean longest dry spell in the 32 day samples. Our trained GANs can rapidly generate numerous realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events.

climate emulation, extreme climate, impacts, machi↗
DOE OSTI2024

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant

We report as wind and solar technologies improve and their costs decrease, the share of power produced by these sources will increase. As the market penetration increases, these power sources will need to provide grid services, such as dispatchability, in addition to providing energy. One way to reduce variability, provide higher quality power to the grid, and address local grid stability issues is through colocating wind and solar power plants. In addition to operating reliably during normal operating conditions, in scenarios with high penetrations of renewable generation, it is important that these hybrid plants can withstand production disruptions and continue to supply power despite prolonged resource reduction, extreme weather events, or other disruptions. In this paper, we present a methodology to optimize a wind-solar-battery hybrid power plant down to the component level that is resilient against production disruptions and that can continually produce some minimum required power. We introduce the models and assumptions we used to simulate a hybrid power plant as well as the design variable parameterization and specific methods we used to optimize the plant. We demonstrate the performance of our method by comparing a plant optimized for different objectives, generation outage durations, minimum power requirements, and power purchase agreements. Although the plant design is sensitive to model parameters and various other assumptions, our results demonstrate some of the optimal designs that occur in different scenarios and what one should expect when designing a hybrid wind-solar-storage power plant.

14 SOLAR ENERGY↗
NASA NTRS2015

Swamp Works: A New Approach to Develop Space Mining and Resource Extraction Technologies at the National Aeronautics Space Administration (NASA) Kennedy Space Center (KSC)

The first steps for In Situ Resource Utilization (ISRU) on target bodies such as the Moon, Mars and Near Earth Asteroids (NEA), and even comets, involve the same sequence of steps as in the terrestrial mining of resources. First exploration including prospecting must occur, and then the resource must be acquired through excavation methods if it is of value. Subsequently a load, haul and dump sequence of events occurs, followed by processing of the resource in an ISRU plant, to produce useful commodities. While these technologies and related supporting operations are mature in terrestrial applications, they will be different in space since the environment and indigenous materials are different than on Earth. In addition, the equipment must be highly automated, since for the majority of the production cycle time, there will be no humans present to assist or intervene. This space mining equipment must withstand a harsh environment which includes vacuum, radical temperature swing cycles, highly abrasive lofted dust, electrostatic effects, van der Waals forces effects, galactic cosmic radiation, solar particle events, high thermal gradients when spanning sunlight terminators, steep slopes into craters / lava tubes and cryogenic temperatures as low as 40 K in permanently shadowed regions. In addition the equipment must be tele-operated from Earth or a local base where the crew is sheltered. If the tele-operation occurs from Earth then significant communications latency effects mandate the use of autonomous control systems in the mining equipment. While this is an extremely challenging engineering design scenario, it is also an opportunity, since the technologies developed in this endeavor could be used in the next generations of terrestrial mining equipment, in order to mine deeper, safer, more economical and with a higher degree of flexibility. New space technologies could precipitate new mining solutions here on Earth. The NASA KSC Swamp Works is an innovation environment and methodology, with associated laboratories that uses lean development methods and creativity-enhancing processes to invent and develop new solutions for space exploration. This paper will discuss the Swamp Works approach to developing space mining and resource extraction systems and the vision of space development it serves. The ultimate goal of the Swamp Works is to expand human civilization into the solar system via the use of local resources utilization. By mining and using the local resources in situ, it is conceivable that one day the logistics supply train from Earth can be eliminated and Earth independence of a space-based community will be enabled.

Resource Extraction↗
DOE OSTI2023

Emergency Asset Positioning for Resilient Transmission Grid Operation

In this paper we study a problem of hurricane emergency preparedness via placement of emergency generation assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency generation assets placed prior to an extreme event.

emergency preparedness↗
DOE OSTI2023

Emergency Generation Asset Positioning for Resilient Transmission Grid Operation: Preprint

In this paper we study a problem of hurricane emergency preparedness via placement of emergency generation assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency generation assets placed prior to an extreme event.

emergency preparedness↗
DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗
DOE OSTI2023

Topological Guided Detection of Extreme Wind Phenomena: Implications for Wind Energy

Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive, limited to specific coordinate. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.

MATHEMATICS AND COMPUTING,WIND ENERGY↗
DOE OSTI2023

Topological Guided Detection of Extreme Wind Phenomena: Implications for Wind Energy: Preprint

Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive, limited to specific coordinate. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.

MATHEMATICS AND COMPUTING,WIND ENERGY↗
DOE OSTI2021

Post-extreme-event restoration using linear topological constraints and DER scheduling to enhance distribution system resilience

In this paper, a post-extreme-event restoration (PEER) algorithm is proposed to improve distribution system resilience. Linear topological constraints are proposed to ensure radial topology after N-k contingencies, possibly in multiple islands. The approach is made comprehensive by considering dispatchable distributed energy resources (DERs), non-dispatchable DERs, and demand responses, as well as on-load tap changers (OLTCs) and shunt capacitors. The goal is to minimize the accumulative expense caused by load reduction payment or penalty, as well as DER operation cost. As a result, the overall system will survive longer with higher resilience during an extreme event. To verify the effectiveness of the PEER algorithm, we proposed a resilience evaluation algorithm using Monte Carlo simulation (MCS) with reduced scenarios. This is based on a probabilistic model for generating random scenarios which consider the uncertainty of line faults and solar irradiance. Combined with the proposed PEER algorithm, this reduced-scenario MCS can evaluate the expected energy not served (EENS) which is an essential index for distribution system resilience. Case studies of the IEEE 33-bus and 123-bus test systems validate the proposed algorithm in reducing EENS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Value of Nuclear Energy to the Reliability of the North American Power System: Results for Western and Eastern Interconnections

This report documents the fulfillment of a milestone for the United States (U.S.) Department of Energy Office of Nuclear Energy Light Water Reactor Sustainability Program: completing a baseline study of regional impact of nuclear power plants and hydrogen production in maintaining grid services and power quality. Models have been comprehensively demonstrated for the Western Interconnection, or Western Electric Coordinating Council area, and in the Eastern Interconnection for scenarios representative of past extreme events (e.g., drought and heat waves). Understanding the impact on the reliability of the bulk electric system of any reduction in generation capacity from nuclear power, for any reason, is the motivation of this work. Factors that might lead to the premature or unplanned closure of nuclear plants, extended outages, or repurposing of nuclear power include: • Aging infrastructure: Many nuclear power plants in the U.S. are nearing the end of their designed operating lives. Upgrading aging infrastructure can be expensive, and some utilities may choose to retire plants rather than invest in costly upgrades. • Low wholesale electricity prices: The deregulation of the electricity market in many states has led to increased competition and driven down wholesale electricity prices, causing nuclear power operators to seek other revenue sources for their heat and power such as clean hydrogen production. • Renewables growth: The rapid growth of renewable energy sources like solar and wind power is posing a challenge to traditional generation sources like nuclear. While many see renewables as a key part of the clean energy transition, their intermittent nature requires additional grid solutions for reliable power supply. • The potential for regulatory decisions to be in conflict: In its 2021 rulemaking, EPA rule (86 FR 880), the Environmental Protection Agency (EPA) set a compliance date for the ban on processing and distribution in commerce of Decabromodiphenyl Ether (DecaBDE). Since DecaBDE is in many components, particularly wiring, of nuclear power plants which are deemed safety-related or important to safety, three plants would not have been able to restart after their 2023 spring outages, and numerous others would have faced issues in the near future. Fortunately, in this case, the EPA provided relief to the nuclear energy industry. The report provides a summary of the significant role nuclear energy plays in the United States’ power generation mix, supplying around 20% of the nation’s electricity generation, spread across 28 U.S. states. Nuclear power is reliable, mostly unaffected by weather and seasonal changes, and provides a consistent source of baseload power. In terms of capacity, nuclear power plants account for as much as 26% of balancing area power generation capacity. Nuclear power also provides a substantial contribution (e.g., 10% of the inertia in the Eastern Interconnection) of the synchronous spinning mass/inertia that buffers the rate at which frequency changes when a load and generation imbalance occurs (e.g., a large plant trips or a load is suddenly shed due to a transmission outage). This contribution is critical for maintaining grid stability during sudden changes in load or generation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2021

Intergenerational Inequities in Exposure to Climate Extremes: Young Generations Are Severely Threatened By Climate Change

Under continued global warming, extreme events such as heat waves will continue to rise in frequency, intensity, duration, and spatial extent over the next decades. Younger generations are therefore expected to face more such events across their lifetimes compared with older generations. This raises important issues of solidarity and fairness across generations that have fueled a surge of climate protests led by young people in recent years and that underpin issues of intergenerational equity raised in recent climate litigation. However, the standard scientific paradigm is to assess climate change in discrete time windows or at discrete levels of warming, a “period” approach that inhibits quantification of how much more extreme events a particular generation will experience over its lifetime compared with another. By developing a “cohort” perspective to quantify changes in lifetime exposure to climate extremes and compare across generations (see the first figure), we estimate that children born in 2020 will experience a two- to sevenfold increase in extreme events, particularly heat waves, compared with people born in 1960, under current climate policy pledges. Our results highlight a severe threat to the safety of young generations and call for drastic emission reductions to safeguard their future. Meteorological extremes, hazards, or climate change impacts are mostly studied as they evolve over time under varying emission scenarios and socioeconomic pathways. For example, applying a heat wave indicator (see table S1) to four bias-adjusted global climate models indicates that the land area annually affected by such heat waves will increase from ~15% around 2020 to ~22% by 2100 under a scenario compatible with limiting global warming to 1.5°C, and to ~46% under a scenario in line with current emission reduction pledges (see the first figure). Recent studies extended this approach, studying aspects of climate change as a function of global mean temperature (GMT) increments, highlighting the scenario-independence of several extreme event indicators but remaining, in essence, a comparison of time windows. By contrast, we performed a birth cohort analysis by combining a collection of multimodel extreme event projections with country-scale life expectancy information, gridded population data, and future global temperature trajectories from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C (see supplementary materials). By integrating the exposure of an average person in a country or region to extreme events across their lifetime, we encapsulate spatiotemporal changes in climate hazards, population density, cohort size, and life expectancy (see the first figure).

global warming↗