Uncertainty Quantification in Planning for Potentially Hazardous Objects (PHOs) Mitigation
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Plesko, Catherine Suzanne [Los Alamos National Lab↗
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Binary Quantum Control Optimization with Uncertain Hamiltonians
Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a discretized binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with two gradient-based algorithms using sum-up-rounding approaches. Furthermore, we discuss the differentiability of the objective function and prove upper bounds of the gaps between the optimal solutions to binary control problems and their continuous relaxations. We conduct numerical simulations on various sized problem instances based on two applications of quantum pulse optimization; we evaluate different strategies to mitigate the impact of uncertainties in quantum systems. In conclusion, we demonstrate that the controls of our stochastic optimization model achieve significantly higher quality and robustness compared with the controls of a deterministic model.
conditional value-at-risk (CVaR)↗
Lattice QCD Calculation of the Subtraction Function in Forward Compton Amplitude
The subtraction function plays a pivotal role in calculations involving the forward Compton amplitude, which is crucial for predicting the Lamb shift in muonic atoms, as well as the proton-neutron mass difference. In this Letter, we present a lattice QCD calculation of the subtraction function using two domain wall fermion gauge ensembles near the physical pion mass. We utilize a recently proposed subtraction point, demonstrating its advantage in mitigating statistical and systematic uncertainties by eliminating the need for ground-state subtraction. Our results reveal significant contributions from Nπ intermediate states to the subtraction function. Incorporating these contributions, we compute the proton, neutron, and nucleon isovector subtraction functions at photon momentum transfer Q 2 ϵ[0,2] GeV 2 . For the proton subtraction function, we compare our lattice results with chiral perturbation theory prediction at low Q 2 and with the results from the perturbative operator-product expansion at high Q 2 . Finally, using these subtraction functions as input, we determine their contribution to two-photon exchange effects in the Lamb shift and isovector nucleon electromagnetic self-energy.
74 ATOMIC AND MOLECULAR PHYSICS↗
A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads
Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.
33 ADVANCED PROPULSION SYSTEMS↗
Large Ensemble Exploration of Global Energy Transitions Under National Emissions Pledges
Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long-term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO 2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO 2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Measuring the Muon Neutrino Magnetic Moment in the NOvA Near Detector
Measuring an enhanced neutrino magnetic moment would be a clear indication of physics beyond the Standard Model, shedding light on the correct beyond Standard Model theory or the potential Majorana nature of neutrinos. It would manifest in the NOvA near detector as an excess of neutrino-on-electron elastic scattering interactions at low electron recoil energies. Leveraging an intense and highly pure muon neutrino beam, along with a finely segmented liquid scintillator detector technology specifically designed for electromagnetic shower separation, enables NOvA to achieve a potentially world-leading sensitivity in probing the effective muon neutrino magnetic moment. This analysis, based on neutrino data collected between 2014 and 2021, corresponding to an exposure of $13.8\times10^{20}$ protons-on-target, yields a result consistent with the Standard Model-only hypothesis with a p-value of 0.31. An upper limit on the effective muon neutrino magnetic moment is set at $\mu_{\nu_\mu}<19.1\times 10^{-10}\mu_B$ at $90\%$ confidence level. Despite facing statistical limitations stemming from low cross section of the signal process, systematic uncertainties have a significant impact on this result. To address these challenges, the NOvA Test Beam experiment focuses on mitigating some of the largest systematic uncertainties within NOvA by investigating particle interactions and energy deposition in a small-scale replica NOvA detector. This thesis describes the calibration of the NOvA Test Beam detector, which is a crucial step in analysing the Test Beam data before they can be utilised to reduce NOvA systematic uncertainties.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Induced Magnetic Fields in LBNF Horns and DUNE-PRISM Off-Axis Neutrinos
Deep Underground Neutrino Experiment (DUNE) is a next-generation, long-baseline neutrino oscillation experiment that will utilize an intense neutrino beam from Fermilab to measure neutrino oscillation parameters with unprecedented precision. The DUNE-PRISM near detector concept employs an off-axis measurement strategy to mitigate neutrino-nucleus interaction uncertainties; however, this approach relies critically on the accurate characterization of the neutrino flux. As statistical uncertainties are suppressed in the high-intensity Long Baseline Neutrino Facility (LBNF) beam, beamline focusing uncertainties, specifically those arising from focusing horn geometry, become an important systematic uncertainty source. This work presents Geant4 LBNF beam simulations quantifying one of the most important systematic uncertainties for off-axis fluxes: the impact of manufacturing tolerances in the LBNF horns. The focusing horns consist of coaxial inner and outer conductors. Ideally, the region inside the inner conductor is field-free, while the region between the inner and outer conductors serves as the focusing region, where the magnetic field follows a nominal $1/r$ dependence. However, inner conductor deformations such as eccentricity and ellipticity introduce asymmetry, inducing unintended magnetic fields inside the field-free region. Crucially, while the on-axis neutrino flux remains largely unaffected by these induced fields, the resulting flux fractional shifts become pronounced over a particular range of off-axis positions where the DUNE-PRISM program will perform measurements. Consequently, these inner conductor deformations can impact the precision of flux predictions, which could potentially degrade the sensitivity to oscillation parameters. Possible mitigation strategies for these effects will also be presented.
Bae, Yuri [U. Minnesota, Minneapolis (main)] (ORCI↗
DetSuM: Detector Surrogate Model for LArTPC
In large neutrino experiments such as the Deep Underground Neutrino Experiment (DUNE), estimating detector response uncertainties typically requires simulation samples that consume substantial computing resources and time. To mitigate this challenge, we present DetSuM, an uncertainty-aware surrogate model designed to capture the detector response variations with reduced computing load compared to full simulations. This poster describes the construction and evaluation of DetSuM using simulation and reconstruction datasets in a rare-event search at DUNE. We assess DetSuM's ability to predict key detector-response variations and their associated uncertainties, discuss current limitations, and outline improvements to extend its validity in systematics studies of DUNE physics.
Li, Aobo [UC, San Diego]↗
Conceptual Designs for Irradiation Creep Testing of SiC in HFIR
Understanding irradiation creep of nuclear fuel cladding is important to properly size the initial fuel-cladding gap and understand when pellet-cladding contact is expected to occur due to a combination of fuel swelling and cladding creep-down. Irradiation creep also plays a role in relaxing stresses that develop in-pile. Silicon carbide fiber–reinforced silicon carbide matrix (SiC/SiC) composites are the leading long-term accident-tolerant fuel cladding concept for light-water reactors (LWRs). Although some limited data are available regarding irradiation creep of the individual constituents (fibers, matrix), data regarding irradiation creep of SiC/SiC composites are currently insufficient. Additional data regarding irradiation creep compliance and the rupture lifetime (combination of creep and slow crack growth) are needed to understand material limitations. This work describes the design and development of two irradiation vehicles that are being pursued for testing SiC/SiC concepts in the High Flux Isotope Reactor (HFIR). The first is a passive experiment, referred to as the PRECISE experiment, that leverages the constant coolant pressure of HFIR to compress a metallic bellows and provide a well-characterized load to drive creep in a SiC/SiC dog bone specimen. The total creep strain would be quantified post-irradiation by measuring dimensional changes of the specimen length as well as local dimensional changes within the gauge region. Non-stressed specimens would also be irradiated under the same conditions to provide an indication of dimensional changes due to radiation-induced swelling in the absence of creep. A second, more complex experiment, referred to as the INSITE experiment, is being designed in parallel that would use pneumatics to pressurize a metal bellows and linear variable differential transformers (LVDTs) to measure the specimen displacement in situ during irradiation. Such an experiment would provide significantly more data regarding the evolution of the creep compliance as a function of dose and applied stress within a single experiment but would require significantly more development time and cost to execute. The primary concern with the INSITE experiment is the accuracy, reliability, and expected lifetime of the LVDTs during irradiation at elevated temperatures. Efforts are being made to adjust the experiment design and operating procedure to limit LVDT temperatures and mitigate or otherwise compensate for uncertainties due to factors such as temperature fluctuations, creep in the surrounding structural materials, and drift of the LVDTs. This work describes the experiment designs, thermal and structural analysis that were performed to ensure that the desired temperature and stress conditions can be achieved, some initial sensitivity analyses to predict the evolution of the radiation-induced specimen displacements, and potential sources of uncertainty in the measurements. Out-of-pile testing is being performed in parallel to confirm that the test trains achieve the expected stress states in the specimens and do not result in prohibitive stress concentrators (e.g., in the grip regions) that might risk pre-mature failure. The PRECISE experiments are proceeding toward fabrication and assembly with HFIR insertion planned during fiscal year 2026. The INSITE experiment is progressing toward out-of-pile demonstrations, which will provide more conclusive evidence regarding the feasibility of executing these tests in HFIR or whether alternative displacement monitoring techniques may need to be considered.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Dark Energy Survey Year 6 Results: Cosmological Constraints from Cosmic Shear
We present legacy cosmic shear measurements and cosmological constraints using six years of Dark Energy Survey imaging data. From these data, we study ~140 million galaxies (8.29 galaxies/arcmin$^2$) that are 50% complete at i=24.0 and extend beyond z=1.2. We divide the galaxies into four redshift bins, and obtain cosmic shear measurement with a signal-to-noise of 83, a factor of 2 higher than the Year 3 analysis. We model the uncertainties due to shear and redshift calibrations, and discard measurements on small angular scales to mitigate baryon feedback and other small-scale uncertainties. We consider two fiducial models to account for the intrinsic alignment (IA) of the galaxies. We conduct a blind analysis in the context of the $Λ$CDM model and find $S_8 \equiv σ_8(Ω_m/0.3)^{0.5}=0.798^{+0.014}_{-0.015}$ (marginalized mean with 68% CL) when using the non-linear alignment model (NLA) and $S_{8} = 0.783^{+0.019}_{-0.015}$ with the tidal alignment and tidal torque model (TATT), providing 1.8% and 2.5% uncertainty on $S_8$. Compared to constraints from the cosmic microwave background from Planck 2018, ACT DR6 and SPT-3G DR1, we find consistency in the full parameter space at 1.1$σ$ (1.7$σ$) and in $S_8$ at 2.0$σ$ (2.3$σ$) for NLA (TATT). The result using the NLA model is preferred according to the Bayesian evidence. We find that the model choice for IA and baryon feedback can impact the value of our $S_8$ constraint up to $1σ$. For our fiducial model choices, the resultant uncertainties in $S_8$ are primarily degraded by the removal of scales, as well as the marginalization over the IA parameters. We demonstrate that our result is internally consistent and robust to different choices in calibrating the data, owing to methodological improvements in shear and redshift measurement, laying the foundation for next-generation cosmic shear programs.
Abbott, T. M.C. [Cerro-Tololo InterAmerican Obs.]↗
Combined Meteorological and Hydrologic Uncertainties Shape Projections of Future Soil Moisture in the Eastern United States
Physical hazards pose risks to many critical systems. Designing adaptive measures to mitigate these risks is challenging due to large uncertainties in modeling future hazards and the associated sectoral responses. Here, we help address this challenge in a hydrologic context by examining the combined role of meteorological forcing and hydrologic parameter uncertainties in shaping projections of future soil moisture. By encoding a simple conceptual water balance model in a differentiable programming framework, we facilitate fast runtimes and an efficient calibration, enabling an improved uncertainty analysis. We characterize uncertainty in model parameters by calibrating against different target data sets and by using several loss functions. We then convolve the resulting parameter ensemble with a set of Earth system model projections to produce a large ensemble (2,340 members) of daily soil moisture simulations. Focusing on the eastern United States, we find that most ensemble members project a drying of soils across the region, although some simulate wetter conditions throughout this century. Our ensemble shows an increase in the frequency and intensity of dry extremes while there is less agreement for wet extremes. We conduct sensitivity analyses on several soil moisture signatures to measure the relative influence of meteorological and hydrologic uncertainties across space and time. Both meteorological and hydrologic factors contribute consistently to uncertainty surrounding long-term trends, while changes to both wet and dry soil extremes are typically more sensitive to hydrologic parameter uncertainty. Our results underscore the need to account for varied sources of uncertainty when developing long-term hydrometeorological projections.
Lafferty, David C. [University of Illinois Urbana‐↗
The Simons Observatory: Combining cross-spectral foreground cleaning with multitracer B -mode delensing for improved constraints on inflation
The Simons Observatory (SO), due to start full science operations in early 2025, aims to set tight constraints on inflationary physics by inferring the tensor-to-scalar ratio r from measurements of cosmic microwave background (CMB) polarization B-modes. Its nominal design including three small-aperture telescopes (SATs) targets a precision σ(r = 0) ≤ 0.003 without delensing. Achieving this goal and further reducing uncertainties requires a thorough understanding and mitigation of other large-scale B-mode sources such as Galactic foregrounds and weak gravitational lensing. We present an analysis pipeline aiming to estimate r by including delensing within a cross-spectral likelihood, and demonstrate it for the first time on SO-like simulations accounting for various levels of foreground complexity, inhomogeneous noise and partial sky coverage. As introduced in an earlier SO delensing paper, lensing B-modes are synthesized using internal CMB lensing reconstructions as well as Planck-like cosmic infrared background maps and LSST-like galaxy density maps. We then extend SO’s power-spectrum-based foreground-cleaning algorithm to include all auto- and cross-spectra between the lensing template and the SAT B-modes in the likelihood function. This allows us to constrain r and the parameters of our foreground model simultaneously. Within this framework, we demonstrate the equivalence of map-based and cross-spectral delensing and use it to motivate an optimized pixel-weighting scheme for power spectrum estimation. We start by validating our pipeline in the simplistic case of uniform foreground spectral energy distributions. In the absence of primordial B-modes, we find that the 1σ statistical uncertainty on r, σ(r), decreases by 37% as a result of delensing. Tensor modes at the level of r = 0.01 are successfully detected by our pipeline. Even when using more realistic foreground models including spatial variations in the dust and synchrotron spectral properties, we obtain unbiased estimates of r both with and without delensing by employing the moment-expansion method. In this case, uncertainties are increased due to the higher number of model parameters, and delensing-related improvements range between 27% and 31%. These results constitute the first realistic assessment of the delensing performance at SO’s nominal sensitivity level.
79 ASTRONOMY AND ASTROPHYSICS↗
The need for carbon-emissions-driven climate projections in CMIP7
Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).
54 ENVIRONMENTAL SCIENCES↗
Uncertainty guided online ensemble for non-stationary data streams in fusion science
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.
AI↗
Measurements of the thermal Sunyaev-Zel’dovich effect with ACT and DESI luminous red galaxies
Cosmic Microwave Background (CMB) photons scatter off the free-electron gas in galaxies and clusters, allowing us to use the CMB as a backlight to probe the gas in and around low-redshift galaxies. The thermal Sunyaev-Zel’dovich effect, sourced by hot electrons in high-density environments, measures the thermal pressure of the target objects, shedding light on halo thermodynamics and galaxy formation, and providing a path toward understanding the baryon distribution around cosmic structures. We use a combination of high-resolution CMB maps from the Atacama Cosmology Telescope and photometric luminous red galaxy catalogs from the Dark Energy Spectroscopic Instrument to measure the thermal Sunyaev-Zel’dovich signal in four redshift bins from 𝑧 = 0.4 to 𝑧 = 1.2, with a combined detection significance of 19𝜎 when stacking on the fiducial CMB Compton-𝑦 map. We discuss possible sources of contamination, finding that residual dust emission associated with the target galaxies is important and limits current analyses. We discuss several mitigation strategies and quantify the residual modeling uncertainty. Furthermore, this work complements closely related measurements of the kinematic Sunyaev-Zel’dovich and weak lensing of the same galaxies.
79 ASTRONOMY AND ASTROPHYSICS↗
Fire weather compromises forestation-reliant climate mitigation pathways
Forestation can contribute to climate change mitigation. However, increasing frequency and intensity of climate extremes are posed to have profound impact on forests and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different integrated assessment models (IAMs) by showcasing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently, many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation.
54 ENVIRONMENTAL SCIENCES↗
Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants
The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Furthermore, our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Hybrid power plants: An effective way of decreasing loss-of-load expectation
Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. Here, this study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.
14 SOLAR ENERGY↗