SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Grid Enhancing Technologies Modeling Methodology Introduction -Grid Enhancing Technology -PLEXOS and TARA Modeling Methodology Overview -PLEXOS/TARA agreement -Flowgate identification -DLR and PFC Location Identification Key Lessons Learned and Possible Improvements
Power System is a complex system, with an objective to provide power to the customers. The objective is driven by a complex consideration of the secure, economic and resilient operation of the system. With the growing occurrence of extreme events emphasis has been shifted towards making the grid more resilient. However, an economic operation is still a priority for the utilities as well as its customers. Many utilities especially small isolated micro-grids do not emphasize much on an economic and resilient operation, but more on reliability. In this research we focus on striking a balance between economic and resilientoperation, ensuring reliability.
Grid-forming assets are required in microgrids to act as voltage-frequency masters. These grid-forming assets can operate in two modes of operation: grid-following mode and grid-forming mode. In grid-following mode of operation, these assets will follow real power and reactive power setpoints and in grid-forming mode of operation these assets will follow voltage and frequency setpoints. Traditionally, diesel generators or natural gas-based generators are widely used to act as a voltage-frequency master. However, many utilities are aiming to replace generators with grid forming-inverters supplied by solar photovoltaics (PV), batteries or fuel cells. Since grid-forming assets need a long-term reliable energy source, fuel cells are a reasonable and viable choice to supply the grid-forming inverters, but some of the challenges facing the wide deployment of grid-forming fuel cell inverters need to be addressed. Specifically, in our proposed work, we aim to focus on the interconnection and interoperability requirements of grid-forming fuel cell inverters. Currently, state-of-the-art fuel cell inverters follow the general interconnection requirements of distributed energy resources (DERs) and general interoperability requirements of DERs, but these requirements were built with PV and battery systems in mind. Fuel cells have different operational requirements, and therefore these requirements need to be appropriately modified for the grid operators to use. These additional steps add to the investment and operational cost to the grid operators. Through the ARIES platform, this proposed project aims to bridge this gap and use power hardware-in-the-loop (PHIL) and controller hardware-in-the-loop (CHIL) experiments to inform the creation of open-source interconnection and interoperability information that can aid in faster and cheaper installation and operation of grid-forming fuel cell inverters.
Significant recent research has investigated the potential for loads to provide balancing services to the grid. However, this research has not addressed key issues that may arise when such schemes are applied at scale including: 1. Distribution Network Issues. Coordination of large numbers of loads could result in power flows that violate distribution network constraints; 2. Stability Issues. Certain strategies to control loads can exhibit nonlinearity in the form of period-adding bifurcations and chaos. Other control strategies can potentially synchronize the behavior of large numbers of loads. In both cases, the outcome can be power oscillations and instability; 3. Communication Network Issues. Bidirectional low-latency communication channels between a central controller (or several distributed controllers) and each resource are expensive and likely not necessary for effective coordination. Our research questions were: What network, stability, and communication issues might arise in practice when we coordinate large aggregations of loads? How can we coordinate loads to achieve performance objectives in a cost effective manner while avoiding these issues? The ultimate technical goal of the project was the development of network-aware, communication-constrained, non-disruptive load control strategies with stability guarantees that achieve the performance requirements of typical balancing services at a sufficiently low cost to enable the load aggregator and customer to profit. The overall goal was to establish credibility for load control at scale and contribute to U.S. energy security and environmental goals. The team succeeded in answering these research questions and developing these control strategies. The overall approach was based on the development of three testing environments: a simulation testbed, an experimental testbed (20 physical model houses with window-box air conditioners) coupled with the simulation testbed, and a field testbed (100 actual homes in Austin, TX) coupled with the simulation testbed, which enabled controller testing, identification of issues, controller development, and controller validation. The resulting controller was used to demonstrate fast timescale grid balancing (frequency regulation) by aggregations of physical and virtual air conditioners, with sufficient quality to participate in the electricity market. Cost benefit analysis showed overall benefits to the participating households, load aggregators, and the grid, especially if the control technology was integrated directly into existing programmable communicating thermostats. The project provides a variety of wider benefits. Our technology transfer and outreach activities lead us to choose an open-source licensing commercialization pathway, enabling the project results to be available to researchers, industry, and the public. Furthermore, new grid balancing technologies will increase grid flexibility and will enable higher penetrations of intermittent renewable energy resources, such as wind and solar, to be connected to the grid, reducing its environmental impact, and mitigating climate change to the benefit of society.
The integration of behind-the-meter distributed energy resources (DERs) into distribution systems brings transformative changes to power systems. This requires operators and planners to find solutions to modernize electric grids and to effectively manage DERs for grid services. NREL developed novel DER management algorithms (referred to as Real-Time Optimal Power Flow, RT-OPF) through U.S. Department of Energy (DOE)-funded efforts, including Advanced Research Projects Agency–Energy (ARPA-E) Network Optimized Distributed Energy Systems (NODES) funding. This cutting-edge control technology aims to modernize distribution systems with large amounts of DER integration, which will help utilities solve issues brought by renewable integration and build resilient and renewable-based electric grids nationwide. Utilidata worked with NREL to investigate the commercialization opportunity of this RT-OPF-based distributed energy resource management system (DERMS). In this project, NREL performs the technology transfer of the RT-OPF to Utilidata to help them fully understand the RT-OPF solution, to identify potential engineering hurdles, and to assess the expected commercial value of various RT-OPF use cases and deployment. The technology transfer work includes two major tasks. First, NREL performs an in-depth knowledge transfer of the entire RT-OPF solution to Utilidata to help them gain an extensive and detailed understanding of the complete RT-OPF solution. In this task, NREL provides exhaustive information (e.g., documentation, code packages, laboratory and field trial data, performance results) while conducting in-depth training sessions to provide a thorough explanation of the entire solution. NREL hosts meetings to present different topics related to the RT-OPF solution, and question-and-answer sessions are included in each meeting to better explain the RT-OPF-related work. Second, the RT-OPF simulations are performed in a laboratory environment. The main objectives of this task are to walk through with Utilidata engineers how to set up a simulation of RT-OPF, identifying each RT-OPF code block/component in operation, learning how these components interact with each other, and eventually running RT-OPF simulations under various system conditions. This task helps Utilidata engineers understand performance limitations and constraints while also quantifying the commercial value of the RT-OPF for different use cases. Based on these two tasks, Utilidata engineers should be able to define and prioritize the next steps of RT-OPF implementation with an eye toward commercial success and scalability of the solution. The next steps are expected to be part of a new project following the conclusion of this project.
Abstract A power system is a complex cyber‐physical system whose security is critical to its function. A major challenge is to model, analyse and visualise the communication backbone of the power systems concerning cyber threats. To achieve this, the design and evaluation of a cyber‐physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) are presented to capture realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental platform for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE), which acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using Power World Dynamic Studio (PWDS). The protection components are modelled with both PWDS and physical devices including the SEL Real‐Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, the design is exemplified and the tools are validated. This work presents four case studies on cyberattack and defence using RESLab, where we demonstrate false data and command injection using Man‐in‐the‐Middle and Denial of Service attacks and validate them on a large‐scale synthetic electric grid.
Existing regulatory approaches to DER lack the precision and granularity necessary to ensure that DER can continue to scale in a cost-effective manner that is aligned with the public interest. To address this need, with the support of the U.S. Department of Energy’s Office of Electricity, Berkeley Lab and Current Energy Group developed an illustrative regulatory framework. By adopting a technology-neutral and modular approach, the framework enables flexibility and scalability for DER providers, utilities, and regulators. Clear price signals and incentives encourage the provision of valuable grid services, while equitable cost allocation promotes efficient use of distribution capacity and interconnection resources. This approach mirrors traditional ratemaking principles for importing customers and positions DERs as integral components of a dynamic and cost-effective energy future.
Assess Grid Enhancing Technologies in transmission planning by coupling economic and reliability analysis over an entire time horizon rather than a few dispatch conditions for ISO-NE.
This presentation was presented at the Aggregation and Grid Security Workshop - held on June 17-18, 2025, at NREL in Golden, Colorado. The goal of the two-day workshop was to address the critical cybersecurity challenges for the future electric grid. This presentation outlines a framework for assessing the roles and responsibilities of virtual power plant stakeholders in mitigating cyber risk to virtual power plant projects and the overall power grid.
The imperative for strong and rapid emissions reductions to mitigate global warming and enhance air quality necessitates a transition to net-zero emissions. This shift requires significant changes throughout the entire energy supply-demand ecosystems, tailored to various sectors. Transportation stands as the least-diversified energy sector and the largest source of U.S. GHG emissions. As the primary enabler for vehicle decarbonization when paired with clean electricity, electric vehicles (EVs) will play a pivotal role in the future. This talk summarizes current status of EV technologies and markets, future projections, and opportunities associated with a conversion to EV for all on-road vehicles. Moreover, EVs are poised to drive substantial growth in electricity demand and presents a unique opportunity to provide demand-side flexibility that is crucial for future renewable-dominated electricity systems. Smart integration of EVs can strengthen the grid, reducing costs and enhancing resilience.
Photovoltaic (PV) cables facilitate the distribution of electricity collected from modules to the energy grid. Durable cabling enables continuous operation of PV installations, whereas cables with a lifetime less than the modules must be replaced - reducing electricity generation and adding to the operating expense. This study primarily focusses on the aging of the key cable types using the combined-accelerated stress testing (C-AST) protocol. Representative cables for utility, building, and control/auxiliary applications were examined. Cable jacket materials examined include: polyolefin, polyethylene, polyamide, poly(vinyl chloride), chlorinated polyethylene, thermoplastic elastomer, and ethylene propylene diene monomer rubber. Specimen characterizations applied include: optical microscopy, mechanical profilometry, instrumented indentation, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). A variety of performance and durability characteristics were observed, depending on the base material, polymer formulation, and jacket color. The results from C-AST are analyzed and discussed relative to a recent industry survey on electronic balance of system components in addition to a recent study where similar cables were aged using steady state ultraviolet weathering (International Electrotechnical Commission Technical Specification 62788-7-2). Recommendations are made for the screening, industry qualification, and service life prediction of PV cable jackets.
The National Renewable Energy Laboratory (NREL) is analyzing the rapidly increasing role of energy storage in the electrical grid through 2050 through its Storage Futures Study. In one phase of the study, NREL used the laboratory's Distributed Generation Market (dGen) model to examine the various future distributed storage capacity adoption scenarios, results, and implications.
As power systems evolve, becoming increasingly renewable, distributed, dynamic, and digital, managing them becomes ever more challenging and complex. System operators serve as the "air traffic controllers" for electricity grids, working to balance supply and demand, manage evolving electricity markets, and ensure the safety and reliability of electricity systems. To adapt to the rapidly changing energy landscape and support the transition to modern, zero carbon power systems, system operators must develop and adopt new technologies, approaches, and frameworks. The Global Power System Transformation (G-PST) Consortium is a network of system operators, research institutions, and industry partners working to accelerate and scale the technical solutions required to transform our world's power systems. By developing and sharing robust models and tools, research and demonstration results, knowledge, and innovation, the G-PST Consortium is empowering system operators to transition to 100% renewable energy while ensuring the reliability, safety, and cost-effectiveness of grid operations.
The quickly-changing generation resource mix in the US grid, with large additions of variable resources, retirements of traditional thermal generation, distributed generation and demand response, are creating new challenges on the traditional operation of both the generation and transmission systems. The ability to perform intra-hour high fidelity production cost modeling (PCM) is needed to allow grid operators and grid planners to integrate high penetration (more than 50%) of variable energy resources, and to make prompt well informed decisions in market operations and planning. In the last decade, PNNL has developed several stand-alone tools to enable grid operators and planners to understand the impact of high variable generation on their systems. These tools have been used is studies such as: (1) Evaluation the benefits of WECC balancing authorities coordination under high variable generation penetration , (2) Benefits of Energy Imbalance Market in the North West Power Pool and (3) Duke Energy and NV Energy solar integration studies. ABB’s GridView is a widely used commercial PCM tool. It is the tool used by WECC and their stakeholders to develop the WECC PCM planning model on bi-annual basis. This proposal is focused on the integrating of PNNL intra-hour uncertainty analysis tools to GridView.
Weather events cause most grid failures. Often, we even get notifications on our phones to take cover or be prepared for an imminent event. If electric grid utilities had a similar warning that also included probable scenarios and the equipment involved, they could prepare and minimize the effects. Recent research at Idaho National Laboratory into electric grid risk analysis methods resulted in a tool that allows for the development of the most likely scenarios given failure probabilities of grid components. INL has a project with the U.S. Department of Energy’s Cybersecurity, Energy Security, and Emergency Response (CESER) program to develop a Grid Alert application that receives messages from the existing emergency alert system, filters and determines components possibly affected by the emergency event, calculates probable scenarios uses MASTERRI and then notifies the utility if there is significant risk. Historical failure data of elements that comprise the U.S. electric grid have been compiled by utilities and organizations such as the international regulatory body North American Electric Reliability Corporation (NERC). Nominal failure rates are obtained from this data. To make this tool possible, estimated failure rates are needed for different component types given the alert type, severity, and location. Historic weather-related grid element failures are correlated with historic weather events from Integrated Public Alert & Warning System (IPAWS). These correlated events and failures are used along with Bayesian updates from the historical norms to provide a modified failure rate for grid elements in the alert areas and calculate probable scenarios. This discusses the Grid Alert project plan but focuses on the data gathered and process used in determining failure rates for possible grid failure scenarios.
Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.
WIND Toolkit Long-term Ensemble Dataset (WTK-LED), an updated version of the meteorological WIND Toolkit, is a meteorological dataset providing high-resolution time series, including interannual variability and model uncertainty of wind speed at every modeling grid point to indicate ranges of possible wind speeds. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: (1) Numerical simulations of wind speed and other meteorological variables covering the contiguous United States (CONUS) and Alaska, with high-resolution (5-minute [min], 2-kilometer [km]) data for 3 years (2018-2020): WTK-LED CONUS, WTK-LED Alaska. (2) Climate simulations from Argonne National Laboratory covering North America, including Alaska, Canada, and most of Mexico and the Caribbean islands. These simulations complement the new WTK-LED to offer a 4-km, hourly dataset covering 20 years (2001-2020): WTK-LED Climate. (3) Specific long-term, high-resolution offshore simulations have been conducted separately for the U.S. coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind dataset: NOW-23. The data for Hawaii include land-based data and are part of WTK-LED Hawaii. Because the accuracy of simulations from a mesoscale model, such as WRF, varies depending on the location and weather situation, and can reach up to several m/s for wind speed, we provide simulated wind speed uncertainty estimates to the community to be used in conjunction with the deterministic model simulations. This dataset was developed to satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have. Based on our validation results to date, we suggest use cases and applications for each dataset of the WTK-LED as shown in "WTK-LED Use Cases" resource below.
Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.