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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 433 records · Page 24
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DOE OSTI2026

Microgrid Hardening Design Toolkit: Puerto Rico Use Case

This document provides a comprehensive example of the microgrid hardening framework and the Sandia developed Microgrid Hardening Design Toolkit v0.27 using a census tract in a coastal area of southern Puerto Rico as a case study. The census tract (72123953100) is located in the municipality of Salinas. Currently, the La Margarita neighborhood within this census tract is part of the Department of Energy’s Cohort 5 of the Energy Technology Innovation and Partnership Program (ETIPP). The neighborhood’s local energy cooperative, Abeyno Coop, has been operating several residential solar photovoltaic (PV) and battery energy storage system (BESS) installations (with around 30 rooftop solar systems as of 2026). As part of the ETIPP project, Abeyno Coop is planning to integrate a larger microgrid into the existing distribution feeder in the area, including solar PV and BESS to supply energy for homes and critical loads such as the medical facilities and the community center that provides emergency shelter and backup power during outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

Rapid deployment of residential photovoltaic (PV) systems helps decarbonize our electricity supplies, but under certain circumstances, high-penetration PV may pose challenges to the electrical distribution grid. In a project funded by the U.S. Department of Energy's Solar Energy Technologies Office and Building Technologies Office, the National Renewable Energy Laboratory and its partners studied how flexible building loads and battery storage, when coordinated at home-level and community-level scales, can be used to address those challenges and enhance grid resilience. In this webinar, we will discuss the methodology, simulation and field pilot results, insights from partners, and lessons learned from the project.

artificial intelligence↗
DOE OSTI2023

Cybersecurity Assessment for a Behind-the-Meter Solar PV System: A Use Case for the DER-CF

The world's energy production is shifting toward lower-cost, cleaner, more efficient, and sustainable sources. The increasing numbers of distributed energy resources (DERs) are allowing for the rapid transformation of electric grids toward achieving the goal of energy decarbonization. Along with cleaner and more efficient energy, however, we must also aim for a secure energy future. Solar photovoltaic (PV) systems are an important part of this transition. This paper discusses a cybersecurity risk assessment for behind-the-meter DERs using a solar PV system as a use case of the Distributed Energy Resource Cybersecurity Framework (DER-CF) developed by the National Renewable Energy Laboratory. This poster presents a conference paper on the risk assessment processes and summarizes the DER-CF's use case recommendations to strengthen the cybersecurity posture of the electric grid.

cybersecurity↗
DOE OSTI2025

Impact of molten salt inflow on the temperature distribution in thermal energy storage tanks at startup for central receiver concentrating solar power plants

Concentrating Solar Power (CSP) systems with molten salt thermal energy storage (TES) tanks are one of the most promising, renewable-based energy conversion technologies for larger-scale power generation. The TES tank is one of the most critical components in CSP plants due to its high-temperature operation (up to 565 °C), daily thermal cycling, and intermittent solar radiation conditions. The plant startup is one of the most challenging operation conditions that could lead to damaging thermal gradients due to low salt inventory levels. In this study an analytical model for the sparger ring was developed and integrated with a detailed computational fluid dynamics model of a commercial-scaled molten salt tank. The integrated model allows an accurate representation of the tank operation to evaluate the effect of molten salt inflow on the mixing process. The tank filling process during plant startup was analyzed considering sparger rings with variations in design features, including inlet orifice configurations, number of orifices, direction of the inlets, and orifice diameter. The results demonstrated that higher temperature gradients are obtained in the tank floor during the plant startup. A sparger ring configuration with a predetermined orifice inlet inclination (30°, 45° and 60°) leads to significant temperature differences in the floor, between 57 °C and 62 °C, but better homogeneity in the temperature of the salt inventory. Lower salt inflow velocities result in a more homogeneous floor temperature, with maximum temperature differences under 38 °C. The sparger ring configuration with 52 orifices of 1-in. diameter and vertical flow showed better homogeneity in the temperature differences as a function of the salt level and lower temperature gradients in the tank floor. Because large temperature gradients in the tank's floor have been identified as one of the main factors contributing to tank failures, assessing various sparger ring design features is fundamental to determining proper inflow conditions that lead to low-temperature gradients and reducing failure susceptibility.

14 SOLAR ENERGY↗
DOE OSTI2022

The National Solar Radiation Database (NSRDB) Fiscal Years 2019-2021(Final Report)

The National Solar Radiation Database (NSRDB) is the leading public source of high-resolution solar resource data in the United States, with more than 166,000 users annually. This database represents the state of the art in satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that enables improvements in accuracy with the deployment of the next-generation geostationary satellites. Making the highest quality, state-of-the-art, regularly updated data sets available on a timely basis for users reduces costs of solar deployment by providing accurate information for siting studies and system output prediction, and thereby reduces levelized cost of energy. Also, high-resolution information from the NSRDB enables moving beyond levelized cost of energy when valuing the impact of renewables on the grid. Additionally, the NSRDB enables the integration of large amounts of solar on the grid by providing critical information about solar availability and variability that is used to enhance grid reliability and power quality.

14 SOLAR ENERGY↗
DOE OSTI2022

Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile

Power system simulations with long-term data typically have large time steps varying from one second to a few minutes. However, for PV inverter semiconductors, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be around 100 µs. This small time step requires long computation times to process yearly power production profiles. This paper proposes a fast fatigue simulation for inverter semiconductors using the quasi-static time series (QSTS) simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature by using a Fast Fourier Transform (FFT). The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 minutes, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. A PV inverter that responds to a Transactive Energy System (TES) is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to co-simulate with system level simulation tools that also adopt the QSTS concept.

Liu, Yunting↗
DOE OSTI2022

Visualizing Fault Induced Traveling Waves in Medium Voltage Systems: Preprint

Traveling waves are induced in power systems during most transient events in the grid. These waves travel close to the speed of light in overhead lines and 50% to 60% the speed of light in underground cables. Even though traveling wave-based protection schemes for transmission systems are available commercially, traveling waves in medium voltage distribution networks are still in research space. Compared to transmission system, medium voltage distribution systems contain more reflections and refractions. Thus, visualization is challenging and is critical in locating faults in distribution network. To address this visualization challenge, this paper presents an open-source tool to visualize the traveling waves using Bewley lattice approach. The developed visualization tool will be useful for the protection and safety engineers to detect and triangulate fault locations in the medium voltage systems and isolate the faults.

Bewley Lattice↗
DOE OSTI2024

Securing Solar for the Grid: Spring 2024 IAB Meeting

The Spring 2024 IAB meeting will focus on updates from the research team and collective feedback and inputs for an updated Roadmap for Solar Cybersecurity. Researchers from the four DOE National Laboratories will present with industry counterparts for the major research tasks within the S2G program, including: Solar Cybersecurity Standards and Certifications, Solar Risk Assessments & Mitigation, Solar Supply Chain Assessment, Network Monitoring Tools and Analysis, and Training and Workforce Development. Sandia National Laboratories developed an original Roadmap for PV Cybersecurity in 2017. This year, we are updating that roadmap to reflect the current state of research and industry and identify gaps and priorities still to be addressed. We look forward to the IAB’s input on key topics for the roadmap.

14 SOLAR ENERGY↗
DOE OSTI2025

Hybrid Power Plants for Energy Resilience: A Case Study

As renewable energy technologies are increasingly adopted, they pose an opportunity to improve the sustainability and resilience of distributed grids, especially when their design and operation is coordinated as a hybrid power plant. When included in hybrid power plants, distributed wind turbines in particular have the potential to enhance the resilience of distributed grids in areas with good wind resource, due to their ability to provide more consistent generation and ancillary services as compared to photo-voltaic (PV) solar panels. Despite this benefit, U.S. distributed wind adoption is lower than other comparable renewable energy technologies. In this study, we seek to demonstrate how hybrid power plants that include distributed wind turbines can contribute to distribution grid resilience by meeting loads (especially critical loads) more consistently, increasing reserve capacity, and providing value to customers during outages. To demonstrate these contributions, we integrate three separate frameworks and apply them to a case study in a rural electric cooperative in Iowa. Through this case study, we simulate and compare hybrid power plant design and operation during two hazard events: a tornado that causes a 48-hour distribution outage and a winter weather event that causes a 6-hour generation outage. The inclusion of a hybrid power plant that leverages 1) increased battery duration and 2) advanced forecasting and dispatch strategies that reserve capacity leading up to a hazard event best reduce lost loads as well as diesel consumption that would otherwise be used to meet those loads during short- and long-duration hazard events. Depending on the hybrid power plant capacity and operation, we find that the outage mitigation value of a hybrid power plant (measured in value to customers to avoid an outage and avoided lost revenues for the utility) is significant in both hazard events; adding wind, solar, and battery assets to the existing system adds about $50-$100M in avoided lost load and at least $4-$8k in utility value in the tornado hazard event, and $570k-$2.2M in avoided lost load and at least $220-$650 in utility value in the winter hazard scenario. In both the tornado and winter hazard scenarios, optimizing the operation of the hybrid system for resilience can lend similar value as increasing battery duration by 5 MWh for the lower capacity systems considered.

17 WIND ENERGY↗
DOE OSTI2021

A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Grid Value Analysis of Medium-Voltage Back-to-Back Converter on DER Hosting Enhancement

This paper presents an analysis of the value that can be realized by medium-voltage back-to-back (MVB2B) converters in terms of the increased utilization rate of distributed energy resources (DERs) and the improvement in operational conditions. A systematic, transferrable, and scalable methodology has been designed to analyze and quantify the increased DER value from three perspectives: 1) curtailment reduction of the DER generation, 2) size reduction of the energy storage needed to otherwise realize DER hosting levels, and 3) hosting capacity improvement of the DERs compared to the base distribution circuit capability. In the case study, the proposed methodology is applied to two utility distribution systems for analysis and quantification of the grid value of the MVB2B converter, installed in the distribution circuit, and provided to the solar photovoltaic (PV) DERs. Here, the analysis results demonstrate that the MVB2B converter can deliver significant value to the PV hosting enhancement of two adjacent distribution systems when they are connected by the MVB2B converter. Based on this case study, this paper analyzes and summarizes the approximate realized grid value of the MVB2B converter for distribution systems dominated by different shares of customer classes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2025

OEDI—Solar Grid Integration Data and Analytics Library

As a part of the Open Energy Data Initiative, this effort aims to develop and demonstrate novel distribution state estimation, control optimization, and transient analysis as well as provide access to data, data integration, and mapping information. More specifically, the focus of the effort will be on physics-based distribution system state estimation, hybrid (physics-based and machine learning) distribution optimal power flow, and event detection/analysis for solar integration and analytics. This work will enable reproducible, robust, replicable, and generalizable R&D in simulation and emulation of solar system integration. These test models and datasets will provide an integrated library for developing and testing power system operation technologies. To make the library user-friendly, this project will provide data curation tools such as data translators, mapping scripts and APIs, database schemas and metadata, interfaces and user dashboard, source code for the reference algorithms, description of the use-cases/scenarios, and comprehensive information on all the assumptions.

14 SOLAR ENERGY↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2020

Coastal Community Resiliency Enhancement Using Marine Hydrokinetic (MHK) Resources and Networked Microgrids

Enormous challenges are inherent in the operation of modern distribution grids. In particular, distribution feeders can be severely compromised under extreme conditions such as large-scale weather events (e.g., hurricanes, floods, and other types of natural disasters). To combat this problem, they turn to local energy support from distributed energy resources, such as solar and wind, marine hydrolinetic (MHK), and other types of energy. In this report, we investigate the actual characteristics of MHK resources to enhance coastal community resiliency. Two typical types of MHK resources (i.e., wave and tidal resources) illustrate the coastal resilience enhancement framework. By identifying their operational characteristics, such as target time-scales and potential power generation variations, we detail a holistic coastal community resiliency enhancement framework that integrates both optimization and control efforts. The optimization problem is formulated to significantly minimize the power outage time at the critical infrastructures in coastal communities by implementing multiple autonomous and dynamic microgrids around MHK resources. Therefore, during severe power outages caused by natural disasters (e.g., floods, hurricanes), the coastal community load profiles can be satisfied using local MHK resources in each microgrid with limited or no power support from upstream power grids.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Interconnection and Interoperability Requirements of Hydrogen Assets to Enable Grid Integration

Traditionally, the inverters used for distributed energy resources (DERs) including solar, energy storage and hydrogen fuel cells are grid following assets. Grid following assets track real power and reactive power setpoints decided by either a local autonomous controller or a remote controller like a microgrid controller. In the past five years, multiple challenges have increased the need for grid forming inverters that can interface to a wide variety of energy resources. These grid forming inverters are becoming critical assets for power systems to enable the replacement of existing rotational machines and provide grid services such as grid resiliency services and black-start. Hydrogen assets (fuel cells) have the potential to successfully replace rotating machines and act as voltage and frequency master by leveraging the long-duration and seasonal energy storage capabilities of hydrogen technologies. This poster presents the progress made in budget period 1 of the proposed work. We will leverage an existing ARIES platform experimental setup to run power-hardware-in-the-loop and controller-hardware-in-the-loop experiments with grid forming fuel cell inverters to develop and document the standardization requirements.

electrolyzer↗
DOE OSTI2022

North Carolina Water Utility Builds Resilience with Distributed Energy Resources

As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗