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At least 883 records · Page 49

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 –7 .

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

The Preservation of PPARγ Genome Duplicates in Some Teleost Lineages: Insights into Lipid Metabolism and Xenobiotic Exploitation

Three peroxisome proliferator-activated receptor paralogues (PPARα, -β and -γ) are currently recognized in vertebrate genomes. PPARγ is known to modulate nutrition, adipogenesis and immunity in vertebrates. Natural ligands of PPARγ have been proposed; however, the receptor also binds synthetic ligands such as endocrine disruptors. Two paralogues of PPARα and PPARβ have been documented in teleost species, a consequence of the 3R WGD. Recently, two PPARγ paralogue genes were also identified in Astyanax mexicanus. We aimed to determine whether the presence of two PPARγ paralogues is prevalent in other teleost genomes, through genomic and phylogenetic analysis. Our results showed that besides Characiformes, two PPARγ paralogous genes were also identified in other teleost taxa, coinciding with the teleost-specific, whole-genome duplication and with the retention of both genes prior to the separation of the Clupeocephala. To functionally characterize these genes, we used the European sardine (Sardina pilchardus) as a model. PPARγA and PPARγB display a different tissue distribution, despite the similarity of their functional profiles: they are unresponsive to tested fatty acids and other human PPARγ ligands yet yield a transcriptional response in the presence of tributyltin (TBT). This observation puts forward the relevance of comparative analysis to decipher alternative binding architectures and broadens the disruptive potential of man-made chemicals for aquatic species.

Páscoa, Inês↗

DOE OSTI2022

Characterization of a Human Platelet Lysate-Loaded Keratin Hydrogel for Wound Healing Applications In Vitro

One of the promising approaches to facilitate healing and regenerative capacity includes the application of growth-factor-loaded biomaterials. Human platelet lysate (hPL) derived from platelet-rich plasma through a freeze-thaw process has been used as a growth factor rich therapeutic in many regenerative applications. To provide sustained local delivery of the hPL-derived growth factors such as epidermal growth factor (EGF), the hPL can be loaded into biomaterials that do not degrade rapidly in vivo. Keratin (KSO), a strong filamentous protein found in human hair, when formulated as a hydrogel, is shown to sustain the release of drugs and promote wound healing. In the current study, we created a KSO biomaterial that spontaneously forms a hydrogel when rehydrated with hPL that is capable of controlled and sustained release of pro-regenerative molecules. Our study demonstrates that the release of hPL is controlled by changing the KSO hydrogel and hPL-loading concentrations, with hPL loading concentrations having a greater effect in changing release profiles. In addition, the 15% KSO concentration proved to form a stable hydrogel, and supported cell proliferation over 3 days without cytotoxic effects in vitro. The hPL-loaded keratin hydrogels show promise in potential applications for wound healing with the sustained release of pro-regenerative growth factors with easy tailoring of hydrogel properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Tibbetts, Jake↗

DOE OSTI2021

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Real-Time Observation of Nanoscale Kink Band Mediated Plasticity in Ion-Irradiated Graphite: An In Situ TEM Study

Graphite IG-110 is a synthetic polycrystalline material used as a neutron moderator in reactors. Graphite is inherently brittle and is known to exhibit a further increase in brittleness due to radiation damage at room temperature. To understand the irradiation effects on pre-existing defects and their overall influence on external load, micropillar compression tests were performed using in situ nanoindentation in the Transmission Electron Microscopy (TEM) for both pristine and ion-irradiated samples. While pristine specimens showed brittle and subsequent catastrophic failure, the 2.8 MeV Au2+ ion (fluence of 4.378 × 1014 cm−2) irradiated specimens sustained extensive plasticity at room temperature without failure. In situ TEM characterization showed nucleation of nanoscale kink band structures at numerous sites, where the localized plasticity appeared to close the defects and cracks while allowing large average strain. We propose that compressive mechanical stress due to dimensional change during ion irradiation transforms buckled basal layers in graphite into kink bands. The externally applied load during the micropillar tests proliferates the nucleation and motion of kink bands to accommodate the large plastic strain. The inherent non-uniformity of graphite microstructure promotes such strain localization, making kink bands the predominant mechanism behind unprecedented toughness in an otherwise brittle material.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Pistia stratiotes L. Biochar for Sorptive Removal of Aqueous Inorganic Nitrogen

Biochar has proven effective in the remediation of excess nitrogen from soil and water. Excess nitrogen from agricultural fields ends up in aquatic systems and leads to reduced water quality and the proliferation of invasive species. This study aimed to assess the efficiency of chemically surface-modified biochar produced from invasive Pistia stratiotes L. for the adsorption of inorganic nitrogen (NH4+ and NO3−). Biochar structure was investigated using scanning electron microscopy, energy-dispersive X-ray analysis, X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, and inductively coupled plasma mass spectrometry. The results from adsorption experiments indicate that NH4+ removal was optimal (0.8–1.3 mg N g−1) at near-neutral pH levels (6.0–7.5), while NO3− removal was optimal (0.4–0.8 mg N g−1) under acidic pH conditions (4.8–6.5) using the modified biochar. These findings highlight the significance of solution pH, biochar morphology, and surface chemistry in influencing the adsorption of NH4+ and NO3−. However, further studies are necessary to assess the potential oxidative transformation of NH4+ to NO3− by biochar, which might have contributed to the reduction in NH4+ in the aqueous phase.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Cu Homeostasis in Bacteria: The Ins and Outs

Copper (Cu) is an essential trace element for all living organisms and used as cofactor in key enzymes of important biological processes, such as aerobic respiration or superoxide dismutation. However, due to its toxicity, cells have developed elaborate mechanisms for Cu homeostasis, which balance Cu supply for cuproprotein biogenesis with the need to remove excess Cu. This review summarizes our current knowledge on bacterial Cu homeostasis with a focus on Gram-negative bacteria and describes the multiple strategies that bacteria use for uptake, storage and export of Cu. We furthermore describe general mechanistic principles that aid the bacterial response to toxic Cu concentrations and illustrate dedicated Cu relay systems that facilitate Cu delivery for cuproenzyme biogenesis. Progress in understanding how bacteria avoid Cu poisoning while maintaining a certain Cu quota for cell proliferation is of particular importance for microbial pathogens because Cu is utilized by the host immune system for attenuating pathogen survival in host cells.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Mechanistic Insights into Peptide Binding and Deactivation of an Adhesion G Protein-Coupled Receptor

Adhesion G protein-coupled receptors (ADGRGs) play critical roles in the reproductive, neurological, cardiovascular, and endocrine systems. In particular, ADGRG2 plays a significant role in Ewing sarcoma cell proliferation, parathyroid cell function, and male fertility. In 2022, a cryo-EM structure was reported for the active ADGRG2 bound by an optimized peptide agonist IP15 and the Gs protein. The IP15 peptide agonist was also modified to antagonists 4PH-E and 4PH-D with mutations of the 4PH residue to Glu and Asp, respectively. However, experimental structures of inactive antagonist-bound ADGRs remain to be resolved, and the activation mechanism of ADGRs such as ADGRG2 is poorly understood. Here, we applied Gaussian accelerated molecular dynamics (GaMD) simulations to probe conformational dynamics of the agonist- and antagonist-bound ADGRG2. By performing GaMD simulations, we were able to identify important low-energy conformations of ADGRG2 in the active, intermediate, and inactive states, as well as explore the binding conformations of each peptide. Moreover, our simulations revealed critical peptide-receptor residue interactions during the deactivation of ADGRG2. In conclusion, through GaMD simulations, we uncovered mechanistic insights into peptide (agonist and antagonist) binding and deactivation of the ADGRG2. These findings will potentially facilitate rational design of new peptide modulators of ADGRG2 and other ADGRs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Mixture Effects of Per- and Polyfluoroalkyl Substances on Embryonic and Larval Sheepshead Minnows ( Cyprinodon variegatus )

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous and persistent environmental contaminants originating from many everyday products. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) are two PFAS that are commonly found at high concentrations in aquatic environments. Both chemicals have previously been shown to be toxic to fish, as well as having complex and largely uncharacterized mixture effects. However, limited information is available on marine and estuarine species. In this study, embryonic and larval sheepshead minnows (Cyprinodon variegatus) were exposed to several PFAS mixtures to assess lethal and sublethal effects. PFOS alone was acutely toxic to larvae, with a 96 h LC50 of 1.97 mg/L (1.64–2.16). PFOS + PFOA resulted in a larval LC 50 of 3.10 (2.62–3.79) mg/L, suggesting an antagonistic effect. These observations were supported by significant reductions in malondialdehyde (105% ± 3.25) and increases in reduced glutathione concentrations (43.8% ± 1.78) in PFOS + PFOA exposures compared to PFOS-only treatments, indicating reduced oxidative stress. While PFOA reduced PFOS-induced mortality (97.0% ± 3.03), perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA) did not. PFOS alone did not affect expression of peroxisome proliferator-activated receptor alpha (pparα) but significantly upregulated apolipoprotein A4 (apoa4) (112.4% ± 17.8), a downstream product of pparα, while none of the other individually tested PFAS affected apoa4 expression. These findings suggest that there are antagonistic interactions between PFOA and PFOS that may reduce mixture toxicity in larval sheepshead minnows through reduced oxidative stress. Elucidating mechanisms of toxicity and interactions between PFAS will aid environmental regulation and management of these ubiquitous pollutants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Effects of Perfluorinated Alkyl Substances (PFAS) on Amphibian Body and Liver Conditions: Is Lipid Metabolism Being Perturbed throughout Metamorphosis?

Per- and polyfluoroalkyl substances (PFAS) may interact with peroxisome proliferator activated receptors (PPARs) and alter lipid homeostasis. Using Xenopus laevis, we investigated the effect of PFAS on (a) lipid homeostasis and whether this correlated to changes in body and hepatic condition; (b) the expression of hepatic genes regulated by PPAR; and (c) the hepatic lipidome. We chronically exposed tadpoles to 0.5 µg/L of either PFOS, PFHxS, PFOA, PFHxA, a binary mixture of PFOS and PFHxS (0.5 µg/L of each), or a control, from NF stage 52 through metamorphic climax. Growth, development, and survival were not affected, but we detected a sex-specific decrease in body condition at NF 66 (6.8%) and in hepatic condition (16.6%) across metamorphic climax for male tadpoles exposed to PFOS. We observed weak evidence for the transient downregulation of apolipoprotein-V (apoa5) at NF 62 in tadpoles exposed to PFHxA. Acyl-CoA oxidase 1 (acox1) was downregulated only in males exposed to PFHxS (Ln(Fold Change) = −0.54). We detected PFAS-specific downregulation of structural glycerophospholipids, while semi-quantitative profiling detected the upregulation in numerous glycerophospholipids, sphingomyelins, and diglycerides. Overall, our findings indicate that PFAS can induce sex-specific effects that change across larval development and metamorphosis. We demonstrate that PFAS alter lipid metabolism at environmentally relevant concentrations through divergent mechanisms that may not be related to PPARs, with an absence of effects on body condition, demonstrating the need for more molecular studies to elucidate mechanisms of PFAS-induced lipid dysregulation in amphibians and in other taxa.

Bushong, Anna (ORCID:0000000249630578)↗

DOE OSTI2022

Soft X-ray Tomography Reveals HSV-1-Induced Remodeling of Human B Cells

Upon infection, viruses hijack the cell machinery and remodel host cell structures to utilize them for viral proliferation. Since viruses are about a thousand times smaller than their host cells, imaging virus-host interactions at high spatial resolution is like looking for a needle in a haystack. Scouting gross cellular changes with fluorescent microscopy is only possible for well-established viruses, where fluorescent tagging is developed. Soft X-ray tomography (SXT) offers 3D imaging of entire cells without the need for chemical fixation or labeling. Here, we use full-rotation SXT to visualize entire human B cells infected by the herpes simplex virus 1 (HSV-1). We have mapped the temporospatial remodeling of cells during the infection and observed changes in cellular structures, such as the presence of cytoplasmic stress granules and multivesicular structures, formation of nuclear virus-induced dense bodies, and aggregates of capsids. Our results demonstrate the power of SXT imaging for scouting virus-induced changes in infected cells and understanding the orchestration of virus-host remodeling quantitatively.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). Additionally, we also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Evaluation of sterol-o-acyl transferase 1 and cholesterol ester levels in plasma, peritoneal fluid and tumor tissue of patients with endometrial cancer: A pilot study

Endometrial cancer (EC) is the most prevalent gynecological malignancy. Abnormal accumulation of sterol-O-acyl transferase 1 (SOAT1) and SOAT1-mediated cholesterol ester (CE) contributes to cancer progression in various malignancies, including ovarian cancer. Therefore, it was hypothesized that similar molecular changes may occur in EC. The present study aimed to evaluate the diagnostic and/or prognostic potential of SOAT1 and CE in EC by: i) Determining SOAT1 and CE levels in plasma, peritoneal fluid and endometrial tissue from patients with EC and control subjects; ii) performing receiver operating characteristic curve analysis to determine diagnostic performance; iii) comparing SOAT1 and CE expression to that of the tumor proliferation marker Ki67; and iv) assessing the association between SOAT1 expression and survival. Enzyme-linked immunosorbent assay was used to determine the levels of SOAT1 protein in tissue, plasma and peritoneal fluid. The mRNA and protein expression levels of SOAT1 and Ki67 in tissues were detected by reverse transcription-quantitative polymerase chain reaction and immunohistochemistry, respectively. CE levels were determined colorimetrically in plasma and peritoneal fluid. SOAT1-associated survival data from the cBioPortal cancer genomics database were used to assess prognostic relevance. The results revealed that SOAT1 and CE levels were significantly elevated in tumor tissue and peritoneal fluid samples collected from the EC group. By contrast, the plasma levels of SOAT1 and CE in the EC and control groups were similar. Significant positive associations between CE and SOAT1, SOAT1/CE and Ki67, and SOAT1/CE and poor overall survival in patients with EC suggested that SOAT1/CE may be associated with malignancy, aggressiveness and poor prognosis. In conclusion, SOAT1 and CE may serve as potential biomarkers for prognosis and target-specific treatment of EC.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning

With the proliferation of mobile devices and smart cameras, detecting anomalies and predicting their mobility are critical for enhancing safety in ubiquitous computing systems. Due to data privacy regulations and limited communication bandwidth, it is infeasible to collect, transmit, and store all data from mobile devices at a central location. To overcome this challenge, we propose FedADMP, a federated learning based joint Anomaly Detection and Mobility Prediction framework. FedADMP adaptively splits the training process between the server and clients to reduce computation loads on clients. To protect the privacy of user data, clients in FedADMP upload only intermediate model parameters to the cloud server. We also develop a differential privacy method to prevent the cloud server and external attackers from inferring private information during the model upload procedure. Extensive experiments using real-world datasets show that FedADMP consistently outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Data Interfaces for Automated Vehicle Services - A Municipality Perspective

As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗