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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-Resolution Wind Resource Data Set of the Greater Puerto Rico Region

In February 2022, the U.S. Department of Energy and six national laboratories launched the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100). PR100 aims to provide a comprehensive analysis of possible pathways for Puerto Rico's energy future, with a goal of 100% renewable energy by 2050. As a part of the renewable energy potential assessment in this project, we developed 20 years (2001-2020) of data using a numerical weather prediction (NWP) model for onshore and offshore wind resource assessment for the Puerto Rico. The research steps in developing the long-term wind resource data sets based on the NWP model were: 1. Model wind resource based on the Weather Research and Forecasting (WRF) model. 2. Develop WRF model configurations for Puerto Rico. 3. Test WRF with 11 different physics parameterizations for planetary boundary layer (PBL). 4. Assess WRF output from the different PBL schemes against observations. 5. Select a final model configuration which can produce the modeled wind speed with sufficient accuracy. 6. Produce 20 years wind resource data sets for Puerto Rico region. In the first stage of our framework for developing wind resource data, we developed a WRF model configuration using two nested domains (9 km and 3 km) to cover Puerto Rico and U.S. Virgin Islands and downscale the ERA5 reanalysis data (0.25 degrees x 0.25 degrees; hourly interval) to a 3-km domain. For the second stage, we implemented one-year simulations focused on using 11 different PBL physics parameterizations to find a combination of WRF physics parameterizations that could provide accurately modeled wind speed for Puerto Rico. We also analyzed the sensitivity of the modeled wind speed to PBL schemes for onshore and offshore locations. The WRF output resulting from the 11 WRF experiments using different PBL parameterizations were evaluated against observations obtained from the National Data Buoy Center (NDBC) as well as at hub height for a location for which measurements were available. A final model setup selected through the validation with observational data was used to produce 20 years of data with 3-km spatial and 5-minute temporal resolution. The WRF model output was post-processed to include wind profiles and basic atmospheric variables in a format that can be easily used for downstream modeling. The 20 years of wind resource data will be made available through NREL and support the estimation of wind energy development costs for the PR100 study.

17 WIND ENERGY↗
DOE OSTI2021

Applying Utility's Advanced Grid Technologies to Improve Resiliency of a Critical Load

The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

BESS↗
DOE OSTI2021

Consensus International Solar Resource Standards and Best Practices Development

Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols adapted to various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM's radiometry subcommittee, NREL leads the development of various radiometric standards. These contribute to increased accuracy in the measurement of the solar resource available to conversion systems and boost the development and improvement of radiative models. These models can then provide the long-term geographic or climatic distribution of solar radiation for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.

ASTM↗
DOE OSTI2023

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Enhanced deep neural networks with transfer learning for distribution LMP considering load and PV uncertainties

As the flexibility of generation and demand increases in distribution systems, the residential loads are emerging as a promising means to participate in demand response and the transactive energy market. Market pricing is an instrumental mechanism for the distribution system operator to exploit the full potential of the flexible resources. The distribution locational marginal price (DLMP) can be used to guide the residential load consumption. This type of market signal helps the distribution system operator to optimize the scheduling of all resources while satisfying related network constraints through a day-ahead market. However, solving the optimization problem for large-scale systems can be computationally expensive. To address the scalability and practicability limitations of the DLMP framework, a learning-based approach is proposed in this paper to complement the day-ahead distribution market framework. Here, the proposed approach combines long short-term memory and transfer learning to develop deep neural network that can capture the spatial–temporal correlation of the input data. The model can determine the optimal DLMP for each node in a distribution system without the system parameters required to formulate the optimization problem. Testing results on IEEE 33-bus and 123-bus systems show that the proposed approach can generate a comparable DLMP against the optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Impacts of hybridization and forecast errors on the probabilistic capacity credit of batteries

Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1-2 h forecast errors - resulting in early or delayed battery discharging relative to the onset of a reliability event - lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery's availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Study of Imbalance Levels Attributed to Photovoltaic Penetration in Distribution Systems

Abstract— The number of renewable sources, namely photovoltaics (PV), continues to grow within distribution systems. How placements impact a system’s performance remains a concern within both the research and the utility communities. Beyond system node voltages and real power losses, this work looks to industry standard imbalance metrics and the changes experienced on these values with varied levels of PV. Specifically, four imbalance metrics are presented and studied for an actual 906-bus distribution system. Multiple loading conditions and photovoltaic placement schemes are explored to compare the changes seen on these metrics across a variety of scenarios experienced by the network.

14 SOLAR ENERGY↗
DOE OSTI2022

Large-scale Hydrogen Storage – Risk Assessment Seattle City Light and Port of Seattle [Abstract]

This CRADA presents the strategy that Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL) will take to support Seattle City Light (SCL), and the Port of Seattle (Port) in performing a risk assessment of large-scale hydrogen storage. Risk assessment is often used to ensure that adequate measures are taken to protect workers and the public, the environment, infrastructure, and assets. A detailed risk assessment can also be used to direct funding and upgrades, to specific components and sub-systems in order to mitigate risks to the larger system. In this way risk assessments are often employed as a part of a larger risk management strategy, with the goal of minimizing the occurrence of hazards and to identify means to limit their consequences. Risk assessment is often used to engage and inform regulators, and to communicate how specific regulations are being met. However, it is important to note that the proposed work is not intended for SCL and the Port to use in order to gain regulatory acceptance for their proposed activities. The work performed as a part of this effort will be a preliminary risk assessment for early-stage component and system designs and should be considered research and development (R&D). As such, the proposed work will be performed to a quality level and design maturity consistent with R&D and is not considered appropriate for final safety analysis and regulatory compliance purposes. Previous and on-going work at SCL and the Port demonstrated the utility of deploying hydrogen systems at the Port. The deployment of hydrogen at the Port is a part of a larger vision of using hydrogen to address a range of issues for SCL and the Port. These include large-scale fueling of MD/HD vehicles, cargo-handling equipment (CHE), and harbor vessels to reduce emissions; support of adjacent LD vehicles; support of critical port operations during extreme events (i.e., resiliency); deferral of more capital- and time-intensive electrical distribution system upgrades while still supporting evolving port operations and decarbonization efforts; facilitating electrification by establishing energy storage as a grid resource, starting at strategic port locations; creation of a flexible market resource that can be used by SCL to generate revenue via arbitrage; support of planned future maritime operations that involve heavy use of hydrogen for ocean-going vessels; and future end-use applications involving natural gas pipeline hydrogen injection. Ultimately, the success of these activities is underpinned by the deployed storage capacity. Large-scale deployment of hydrogen systems will require hydrogen storage at a scale that has not been demonstrated. In addition, the ideal location for such multi-use systems is near the end user which will often necessitate deploying into urban and/or industrial areas. A detailed risk assessment using the Port as a test case is necessary to ensure the deployment of large-scale hydrogen is successful. Many technologies have been proposed for hydrogen storage; however, these technologies need to be analyzed as they apply to an actual site. The physical infrastructure and hydrogen use cases for the Port will be analyzed, and a risk assessment for compressed hydrogen, liquified hydrogen, and Liquid Organic Hydrogen Carrier (LOHC) storage will be performed. These risk assessments will be useful for understanding how each of these technologies would perform in terms of facility and public safety. The operating states of the proposed hydrogen systems at the Port will be analyzed and incorporated into the storage risk assessment. Scalability will also be analyzed to understand how future port uses would affect the overall risk assessment. Finally, using the risk assessment as a tool to inform engagement and to gain stakeholder acceptance will be explored.

08 HYDROGEN↗
DOE OSTI2024

Maintaining Grid Reliability: Lessons From Renewable Integration Studies

The U.S. Department of Energy (DOE) and other organizations have sponsored numerous research studies over the last two decades to examine the effect of increased wind and solar deployment on grid reliability, including a number of studies that examine grids deriving more than 50% of annual energy from wind and solar. In this report, we discuss key findings from both the research body of knowledge and real-world practice related to grid reliability, and we demonstrate how to plan for and achieve continued reliability in the future as wind and solar deployment increase.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

Estimation of Fire Emissions from Satellite-Based Measurements

Biomass burning is a worldwide phenomenon affecting many vegetated parts of the globe regularly. Fires emit large quantities of aerosol and trace gases into the atmosphere, thus influencing the atmospheric chemistry and climate. Traditional methods of fire emissions estimation achieved only limited success, because they were based on peripheral information such as rainfall patterns, vegetation types and changes, agricultural practices, and surface ozone concentrations. During the last several years, rapid developments in satellite remote sensing has allowed more direct estimation of smoke emissions using remotely-sensed fire data. However, current methods use fire pixel counts or burned areas, thereby depending on the accuracy of independent estimations of the biomass fuel loadings, combustion efficiency, and emission factors. With the enhanced radiometric range of its 4-micron fire channel, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which flies aboard both of the Earth Observing System EOS) Terra and Aqua Satellites, is able to measure the rate of release of fire radiative energy (FRE) in MJ/s (something that older sensors could not do). MODIS also measures aerosol distribution. Taking advantage of these new resources, we have developed a procedure combining MODIS fire and aerosol products to derive FRE-based smoke emission coefficients (C(e), in kg/MJ) for different regions of the globe. These coefficients are simply used to multiply FRE from MODIS to derive the emitted smoke aerosol mass. Results from this novel methodology are very encouraging. For instance, it was found that the smoke total particulate mass emission coefficient for the Brazilian Cerrado ecosystem (approximately 0.022 kg/MJ) is about twice the value for North America, Western Europe, or Australia, but about 50% lower than the value for southern Africa.

Ichoku, Charles↗
DOE OSTI2023

HEPnOS: a Specialized Data Service for High Energy Physics Analysis

In this paper, we present HEPnOS, a distributed data service for managing data produced by high-energy physics (HEP) experiments. Using HEPnOS, HEP applications can use HPC resources more effciently than traditional fle-based applications. The fle-based model leads to a rigid, chunk-based allocation of computational resources and limits the number of cores that can be used concurrently by an HEP application. The fundamental problem is that organizing domain-specifc data into fles inadvertently introduces a single, artifcial, confated tuning parameter that puts key optimization goals into confict: larger fle sizes reduce metadata overhead and thus improve I/O effciency, but smaller fle sizes provide more opportunity for workfow parallelism and load balancing. In this work, we introduce a domain-specifc data service that decouples that constraint so that data can be accessed and processed in its natural granularity while still maintaining I/O effciency. By removing the constraints introduced by fle handling we are able to obtain better scaling and make effcient use of more cores for processing a fxed-sized data sample. We demonstrate the improved scalability by using an application developed in the fle-based paradigm and comparing it to a version modifed to use HEPnOS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Smart Inverters, Dumb Risk: Taking Control of IBR Security in the Digital Age

This presentation addresses the security challenges posed by Inverter-Based Resources (IBRs) in the modern energy landscape. The presentation highlights the vulnerabilities and risks associated with IBRs, including the potential for cyber-attacks, the impact of insecure defaults, and the systemic risks posed by supply chain dependencies. Key topics covered include: 1) The increasing digital transformation in energy systems and the associated security risks. 2) Specific vulnerabilities in IBRs, including weak passwords, hardcoded credentials, and insecure web application interfaces. 3) The implications of persistent connectivity and the strategic risks posed by foreign-manufactured components. 4) The role of regulatory frameworks, such as NERC CIP, in addressing these challenges and the limitations of current oversight. 5) Practical solutions for mitigating risks, including secure design practices, vendor risk assessments, and the importance of strong passwords and role-based access control. The presentation underscores the necessity of a comprehensive, system-of-systems approach to securing IBRs, emphasizing the need for collaboration across various stakeholders, including operators, developers, and regulators, to ensure the resilience and security of the energy grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

National Laboratory of the Rockies: Pennsylvania Public Utility Commission/ Technical Utility Services Bureau Distribution System Resilience Deep Dive 2025

The following is a summary of the National Laboratory of the Rockies (NLR) - Pennsylvania PUC/ Technical Utility Services Bureau (TUS) Distribution System Resiliency Deep-Dive project. This is work that NLR performed to provide the support that the TUS requested under the Resources and Support for State Energy Offices & Regulators (RASOR) program.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2022

Categorizing distributed wind energy installations in the United States to inform research and stakeholder priorities

Abstract Background Distributed wind energy adoption in the United States can contribute to the diverse portfolio of energy technologies needed to achieve ambitious decarbonization goals. However, with limited deployment to date, the current distributed wind market must be better understood; these efforts will support the range of stakeholders who will drive successful deployment. This article first distinguishes three categories of distributed wind from existing literature: (1) behind the meter, (2) intended for explicit local load, and (3) physically distributed. A novel methodology to classify individual wind installations into each of these categories is then presented and applied to two data sets of wind installations in the United States to categorize and illuminate distinct segments in the distributed wind market. Results Physically distributed installations, constituted by small to moderately sized projects serving local loads on distribution systems solely because of their proximity to them, account for the highest amount of capacity but the lowest number of installations out of the three categories. The inverse is true for behind-the-meter installations, which are used to serve on-site loads. Installations intended for explicit local load, which are interconnected on the utility side of the distribution system and intentionally built to provide energy to loads on the same distribution system, rank in the middle for both installed capacity and number of installations. Conclusions Distributed wind energy deployment in the United States is geographically widespread, but the extent to which a single category is developed in each state varies. Policies, wind resources, and broad energy technology trends contribute to these deployment patterns. By identifying the extent to which each category of installations exists, decision-makers are empowered with data necessary to tailor research and development programs and address stakeholder priorities through policy and other means, ultimately supporting future deployment.

17 WIND ENERGY↗