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DOE OSTI2025

Enabling Strong Neutrino Self-Interaction with an Unparticle Mediator

Recent explorations of the cosmic microwave background and the large-scale structure of the universe have indicated a preference for sizable neutrino self-interactions, much stronger than what the standard model offers. When interpreted in the context of simple particle-physics models with a light, neutrinophilic scalar mediator, some of the hints are already in tension with the combination of terrestrial, astrophysical, and cosmological constraints. We take a novel approach by considering neutrino self-interactions through a mediator with a smooth, continuous spectral density function. We consider Georgi’s unparticle with a mass gap as a concrete example and point out two useful effects for mitigating two leading constraints. (i) The Unparticle is “broadband’—it occupies a wide range of masses which allows it to pass the early universe constraint on effective number of extra neutrinos ( Δ N eff ) even if the mass gap lies below the MeV scale. (ii) Scattering involving unparticles is less resonant, which lifts the constraint set by IceCube based on a recent measurement of ultra-high-energy cosmogenic neutrinos. Our analysis shows that an unparticle mediator can open up ample parameter space for strong neutrino self-interactions of interest to cosmology and serves a well-motivated target for upcoming experiments. Published by the American Physical Society 2025

Foroughi-Abari, Saeid (ORCID:0000000294061896)↗
DOE OSTI2024

Detecting Anomalies for Fire Prevention in Distribution Systems: Challenges and Analytical Techniques

Electric utilities in California have historically been linked to up to 10% of wildfires. To mitigate this risk, Southern California Edison has invested significantly in wildfire prevention strategies, including undergrounding cables and enhancing equipment inspections. This article explores a novel approach to fire prevention by detecting anomalies in the distribution system that may indicate potential fire hazards. The focus is on identifying arcing conditions through high-resolution point-on-wave (POW) measurements. Arcing, a precursor to fires, is challenging to detect due to its subtle transients and complex system topology. The article discusses the use of advanced signal processing and machine learning techniques, such as spectral correlation function and discrete wavelet transform, to extract features from POW data and accurately identify arcing events. The study demonstrates a high accuracy rate in detecting arcing, paving the way for improved fire prevention measures in electric distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
DOE OSTI2024

Demonstration of the rodeo algorithm on a quantum computer

The rodeo algorithm is an efficient algorithm for eigenstate preparation and eigenvalue estimation for any observable on a quantum computer. This makes it a promising tool for studying the spectrum and structure of atomic nuclei as well as other fields of quantum many-body physics. The only requirement is that the initial state has sufficient overlap probability with the desired eigenstate. While it is exponentially faster than well-known algorithms such as phase estimation and adiabatic evolution for eigenstate preparation, it has yet to be implemented on an actual quantum device. In this work, we apply the rodeo algorithm to determine the energy levels of a random one-qubit Hamiltonian, resulting in a relative error of 0.08% using mid-circuit measurements on the IBM Q device Casablanca. This surpasses the accuracy of directly-prepared eigenvector expectation values using the same quantum device. We take advantage of the high-accuracy energy determination and use the Hellmann-Feynman theorem to compute eigenvector expectation values for a different random one-qubit observable. For the Hellmann-Feynman calculations, we find a relative error of 0.7%. Here, we conclude by discussing possible future applications of the rodeo algorithm for multi-qubit Hamiltonians.

algorithm↗
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

Transforming the $v$ World: A New Multivariate Transformer Energy Estimator for NOvA

The NOvA Transformer Energy Estimator (Transformer_EE) is a universal machine learning tool currently used to infer the incoming beam neutrino energy and the outgoing lepton energy in both near andfar detectors. It uses a unique, highly flexible framework for simultaneous multivariate prediction that supports many possible loss functions. A spectral reweighting and flattening scheme lessens training bias. A feature noising subroutine enables adversarial-like training, mitigating sensitivities to certain systematic effects at marginal resolution loss at inference time. The state of the Transformer_EE will be reviewed, and its robustness with respect to several NOvA Near and Far Detector systematics highlighted.

Tong, Leon [Minnesota U.] (ORCID:0000000231625965)↗
DOE OSTI2025

The QPO in RE J1034+396 Originates in the Hot Corona

RE J1034+396 is one of the few active galactic nuclei (AGNs) with a significant quasiperiodic oscillation (QPO). The QPO has been observed in over 1 Ms of XMM-Newton observations spanning over a decade. We investigate the power spectral density function (PSD) of seven long (∼90 ks) XMM-Newton observations of the AGN RE J1034+396 in two energy bands. The soft (0.3–0.5 keV) band targets emission from the disk, while the hard (2–7 keV) band isolates the primary X-ray continuum emission from the corona. The QPO is significantly detected in the hard band of five of the seven observations. The best-fitting models indicate that the QPO detection in both bands is entirely attributable to the coronal emission with no additional contribution from the disk. This explains the strong coherence between the hard and soft bands at the QPO frequency. The covariance spectrum is consistent with this picture as the variability at QPO frequencies is attributed solely to fluctuations in the hot corona. The time lag as a function of energy is well described by a ∼2000 s intrinsic soft lag, resulting from the disk responding to emission from the corona, that undergoes phase wrapping at approximately the QPO frequency. By demonstrating that in this system the QPO arises in the corona, we provide new insights into the mechanisms generating QPOs.

Active galactic nuclei↗
DOE OSTI2025

Improving the Capabilities and Computational Efficiency of the RTE+RRTMGP Radiation Code (Final Report)

This report details progress on the RTE+RRTMGP radiation codes made during the period of performance. RTE+RRTMGP is a set of codes for computing radiative fluxes in planetary atmospheres. RRTMGP uses a k-distribution to provide an optical description (absorption and possibly Rayleigh optical depth) of the gaseous atmosphere, along with the relevant source functions, on a pre-determined spectral grid given temperatures, pressures, and gas concentration. RTE computes fluxes given spectrally-resolved optical descriptions and source functions. Spectrally-resolved fluxes are summarized (“reduced”) via a user extensible class. The initial release of the code and the design choices are described in Pincus et al. 2019; the codes are available on Github. Although RRTMGP was based on current (at the time) empirical spectroscopic data, RTE and RRTMGP were developed in large part to modernize software practices. The design focused on flexibility broadly interpreted: by separating code from data and allowing data to drive computation; in coupling to the host model (e.g. the coupling of clouds to radiative fluxes is user-controlled); with respect to programming languages (computational tasks are accessed via widely-compatible C interfaces); and with respect to hardware (the codes run on a range of CPU and GPU architectures). The code also puts an emphasis on modularity and clarity. RTE+RRTMGP v1.0 was released in September 20219. This award supported the evolution of the RTE+RRTMGP code base to support greater flexibility, accuracy, and efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Effect of Time Window and Spectral Measurement Options on Empirical Green’s Function Analysis Using DAS Array and Seismic Stations

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. Here, we focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

58 GEOSCIENCES↗
DOE OSTI2025

Bounds on spectral gaps of Hyperbolic spin surfaces

We describe a method for constraining Laplacian and Dirac spectra of two dimensional compact orientable hyperbolic spin manifolds and orbifolds. The key ingredient is an infinite family of identities satisfied by the spectra. These spectral identities follow from the consistency between 1) the spectral decomposition of functions on the spin bundle into irreducible representations of SL(2,R) and 2) associativity of pointwise multiplication of functions. Applying semidefinite programming methods to our identities produces rigorous upper bounds on the Laplacian spectral gap as well as on the Dirac spectral gap conditioned on the former. In several examples, our bounds are nearly sharp; a numerical algorithm based on the Selberg trace formula shows that the [0;3,3,5] orbifold, a particular surface with signature [1;3], and the Bolza surface nearly saturate the bounds at genus 0, 1 and 2 respectively. Under additional assumptions on the number of harmonic spinors carried by the spin-surface, we obtain more restrictive bounds on the Laplacian spectral gap. In particular, these bounds apply to hyperelliptic surfaces. We also determine the set of Laplacian spectral gaps attained by all compact orientable two-dimensional hyperbolic spin orbifolds. We show that this set is upper bounded by 12.13798; this bound is nearly saturated by the [0;3,3,5] orbifold, whose first non-zero Laplacian eigenvalue is λ^(0)_1 ≈ 12.13623.

Spectral theory↗
DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗
DOE OSTI2025

Operator dynamics in Floquet many-body systems

We study operator dynamics in many-body quantum systems, focusing on generic features of systems that are ergodic, spatially extended, and lack conserved densities. Quantum circuits of various types provide simple models for such systems. We focus on Floquet quantum circuits, comparing their behavior with what has been found previously for circuits that are random in time. Floquet circuits, which have discrete time-translation symmetry, represent an intermediate case between circuits that are random in time and lack any symmetry, and systems with a time-independent Hamiltonian and continuous time-translation invariance. By making this comparison, one of our aims is to identify signatures of time-translation symmetry in Floquet operator dynamics. To characterize behavior we examine a variety of quantities in solvable models and numerically: operator autocorrelation functions; the partial spectral form factor; the out-of-time-order correlator (OTOC); and the paths in operator space that make the dominant contributions to the ensemble-averaged autocorrelation functions. Our most striking result is that ensemble-averaged autocorrelation functions show behavior that is distinctively different in Floquet systems compared to systems in which successive time-steps are independent. Specifically, while average autocorrelation functions decay on a microscopic timescale for circuits that are random in time, in Floquet systems they have a late-time tail with a duration that grows parametrically with the size of the operator support. In the simplest models this tail is separated from the initial decay by a minimum, so that the average autocorrelation function has an intermediate-time peak. The existence of these tails provides a way to understand deviations of the spectral form factor from random matrix behavior at times shorter than the Thouless time. In contrast to this feature in autocorrelation functions, we find no new aspects to the behavior of OTOCs for Floquet models compared to random-in-time circuits. We show that this difference between averaged autocorrelation functions and OTOCs can be understood in terms of the paths in operator space that contribute to the two quantities: paths for the former retain a limited support at late times, while paths for the latter are dominated by operator spreading. Published by the American Physical Society 2025

Yoshimura, Takato (ORCID:0000000309159846)↗
DOE OSTI2024

Out-of-time-ordered-correlators for the pure inverted quartic oscillator: classical chaos meets quantum stability

Out-of-time-ordered-correlators (OTOCs) have been suggested as a means to diagnose chaotic behavior in quantum mechanical systems. Recently, it was found that OTOCs display exponential growth for the inverted quantum harmonic oscillator, mirroring the fact that this system is classically and quantum mechanically unstable. In this work, I study OTOCs for the inverted anharmonic (pure quartic) oscillator in quantum mechanics, finding only oscillatory behavior despite the classically unstable nature of the system. For higher temperature, OTOCs seem to exhibit saturation consistent with a value of –2< x 2 > T < p 2 > T at late times. I provide analytic evidence from the spectral zeta-function and the WKB method as well as direct numerical solutions of the Schrödinger equation that the inverted quartic oscillator possesses a real and positive energy eigenspectrum, and normalizable wave-functions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Machine-learned quantum molecular dynamics calculations of warm dense equation of state and ionic transport coefficients of deuterated water

White dwarf models require accurate equations of state and ionic transport coefficients in the warm dense matter regime, where kinetic theory models and tabulated equations of state are often inaccurate. In this work, spectral-partitioned density functional theory and machine-learned interatomic potentials are combined to perform large-scale, first-principles quantum molecular dynamics simulations of deuterated water (D 2 O) near the principal Hugoniot. This approach retains Kohn-Sham accuracy while achieving orders-of-magnitude speedup, yielding converged equation of state and transport properties over a broad pressure and temperature range. The results reveal the thermodynamic conditions under which ionic transport models for interdiffusivity and shear viscosity converge and identify those in closest agreement with density functional theory benchmarks at temperatures in the warm dense matter regime. The present framework extends first-principles transport calculations to higher temperatures than previously achieved, and provides an efficient, scalable, and general approach for studying transport properties in complex multicomponent mixtures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Temporal Properties of Compressible Magnetohydrodynamic Turbulence

Describing the temporal properties of compressible magnetohydrodynamic (MHD) turbulence is a fundamental problem that has important implications for particle acceleration and transport in astrophysical plasmas. Here, by carefully analyzing the spatial and temporal properties of compressible MHD turbulence, we derive a new spectral power density function that is supported by simulations. This new function reveals that the low-frequency fluctuations are dominated by modes with small parallel wavenumbers with respect to the mean background magnetic field. Furthermore, for fluctuations with dynamically significant parallel wavenumbers, broadening around their eigenfrequencies is described by this function, which is in close agreement with simulations. We use this formalism to present the scaling properties of individual MHD modes. Such broadening is a direct consequence of nonlinear processes and is different for the three fundamental MHD modes. Our results provide a new window to investigate the temporal properties of turbulence and will enable further studies on the interaction between compressible MHD turbulence and energetic plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Flavor diagonal nucleon charges using clover fermions on MILC HISQ ensembles

We present lattice results for the flavor diagonal charges of the proton from the analysis of eight ensembles generated using 2+1+1-flavors of highly improved staggered quarks by the MILC Collaboration. The calculation includes all the needed connected and disconnected contributions to nucleon three-point function. For extracting matrix elements using fits to the spectral decomposition of these correlation functions, two strategies to remove excited state contributions are employed and compared. To renormalize these charges, the 2+1-flavor mixing matrix is calculated in the regularization independent symmetric momentum subtraction intermediate scheme on the lattice. The final results are presented in the $\overline{MS}$ scheme at scale 2 GeV. The axial charges for the proton are $𝑔$$^{𝑢}_{𝐴}$ = 0.781⁢(25), $𝑔$$^{𝑑}_{𝐴}$ =−0.440⁢(39), and $𝑔$$^{𝑠}_{𝐴}$ = −0.055⁢(9); the tensor charges are $𝑔$$^{𝑢}_{𝑇}$ = 0.782⁢(28), $𝑔$$^{𝑑}_{𝑇}$ = −0.195⁢(16), and $𝑔$$^{𝑠}_{𝑇}$ = −0.0016⁢(12); and the scalar charges are $𝑔$$^{𝑢}_{𝑆}$ = 9.39⁢(88), $𝑔$$^{𝑑}_{𝑆}$ = 8.84⁢(93), and $𝑔$$^{𝑠}_{𝑆}$ = 0.37⁢(14). Results for the neutron are given by the 𝑢 ↔ 𝑑 interchange. Results for the sigma terms are 𝜎 𝜋⁢𝑁 | standard = 42⁢(6) MeV from a “standard” analysis and 𝜎 𝜋⁢𝑁 | 𝑁⁢𝜋 = 61⁢(6) MeV from an “𝑁⁢𝜋” analysis that includes the contributions of multihadron 𝑁⁢𝜋 excited states as motivated by chiral perturbation theory. Our preferred value 𝜎 𝜋⁢𝑁 | 𝑁⁢𝜋 is consistent with the phenomenological extraction from 𝜋 −𝑁 scattering data. The strangeness content of the proton, for which the standard analysis is appropriate, is 𝜎 𝑠 | standard = 35⁢(13) MeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Accessing Transient Isomers in the Photoreaction of Metastable‐State Photoacid

The photoreaction of a metastable‐state photoacid (mPAH) generally involves multiple isomers with various connected pathways of photoinduced structural changes during a single reaction cycle. However, only a limited number of isomers have been identified experimentally so far owing to the inherent complexity in combination with the presence of various competing electronic and vibrational processes, as well as the constantly varying interactions between mPAH isomers and solvent molecules. Here, in this work, an optical spectroscopic study on a benzimidazole‐based mPAH, a novel photoacid using benzimidazole as the structural moiety with the active proton, is reported. Through measurements of linear absorption and steady‐state fluorescence in neat solvents and binary mixtures, a pronounced effect of neat water and its binary mixture with glycerol is discovered on the photoreaction of this benzimidazole‐mPAH, manifested by the remarkably distinct spectral responses to irradiation from that observed for an organic solution under identical conditions. Measurements of time‐ and frequency‐resolved fluorescence emission further enable to access transient isomers and the associated spectral characteristics from other competing electronic excited‐state relaxation processes. Spectral deconvolution analysis and time‐dependent density functional theory calculations are applied to separate distinct spectral components and access their potential origin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Coherency-Constrained Spectral Clustering for Power Network Reduction

This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.

42 ENGINEERING↗