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DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2022

Four decades ago, construction was just beginning on experimental turbines at the National Wind Technology Center (NWTC). Today, the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) facility is the centerpiece of the laboratory's Flatirons Campus, a world-class hub for renewable energy research. The nation's shift to 100% clean electricity by 2035 will require a mix of renewable energy sources and strategies - and together, wind and solar energy could account for 60% to 80% of that clean energy resource. In Fiscal Year (FY) 2022, NREL scientists, engineers, and analysts contributed to these visionary goals through their wind energy research. As wind innovations push into new areas, NREL continues to play a vital role in advancing technology and addressing deployment barriers in pursuit of more efficient, reliable, and predictable wind energy systems. FY 2022 wind research and development explored the potential for dramatic growth in land-based systems, the launch of the nation's first commercial-scale offshore installations, and transmission infrastructure buildout. Land-based wind energy is one of the most cost-effective electricity supply options - but utility-scale deployment will require up to 10 times the current number of turbines. An NREL plan addressed this need to accelerate U.S. wind technology rollout at distributed and utility scales. Another project conducted by NREL and the Pacific Northwest National Laboratory (PNNL) helps position the nation's first major offshore wind corridor for success. The WETO-funded Atlantic Offshore Wind Transmission Study is evaluating options for balancing electricity supply and demand, while supporting resilience of the grid and marine industries. WETO, NREL, and other partners are working to enable the enormous supply chain and workforce changes the U.S. wind energy industry will need to meet net-zero-carbon-emissions targets. As part of a seminal series of DOE-funded supply chain studies, NREL analysts reported on the trade-offs involved in manufacturing large volumes of wind technologies, while addressing cost, workforce, and logistics issues. All of this research is supported by NREL's outstanding research teams, tools, data, and facilities. A WETO-funded international wind energy field campaign, the American WAKE experimeNt (AWAKEN), has brought together experts from NREL, PNNL, and Sandia National Laboratories to create the world's most comprehensive set of high-resolution data on wind energy atmospheric phenomenon. This study could lead to more accurate predictions of losses from turbine-to-turbine wake interactions, eventually helping wind plants capture more energy and operators save millions of dollars. In addition, NREL researchers developed testing, modeling, and analysis tools to improve the security of power grids by identifying wind power plant dynamic stability problems. A new Stochastic Soaring Raptor Simulator (SSRS) protects golden eagles from turbine encounters by predicting flight paths. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2022 (between October 1, 2021, and September 30, 2022).

accomplishments↗

DOE OSTI2018

Use of Wind Turbine Kinetic Energy to Supply Transmission Level Services

This paper discusses the broad use of rotational kinetic energy stored in wind turbine rotors to supply services to the electrical power grid. The grid services are discussed in terms of zero-net-energy, which do not require a reduction in power output via pitch control (spill), but neither do they preclude doing so. The services discussed include zero-net-energy regulation, transient and small signal stability, and other frequency management services. The delivery of this energy requires a trade-off between the frequency and amplitude of power modulation and is limited, in some cases, by equipment ratings and the unresearched long-term mechanical effects on the turbine. As wind displaces synchronous generation, the grid's inertial storage is being reduced, but the amount of accessible kinetic energy in a wind turbine at rated speed is approximately 6 times greater than that of a generator with only a 0.12% loss in efficiency and 75 times greater at 10% loss. The potential flexibility of the wind's kinetic storage is also high. However, the true cost of providing grid services using wind turbines, which includes a potential increase in operations and maintenance costs, have not been compared to the value of the services themselves.

17 WIND ENERGY↗

DOE OSTI2016

Testbed and Simulation-based Evaluation of Privacy-preserving Algorithms for Smart Grid AMI Networks

Although smart meters are deployed in a lot of countries as a result of aiming to make the power grid more resilient and efficient, the data collection process from smart meters in Smart Grid (SG) has still challenges that relate to consumer privacy. Referred to as Advanced Metering Infrastructure (AMI), the data collected and transmitted through this AMI can leak sensitive information about the consumers if it is sent as plaintext. While a number of solutions have been proposed in the past, the deployment of these solutions in real-life was not possible since the actual AMIs were not accessible to researchers. Therefore, a lot of solutions relied on simulations which may not be able to capture the performance of these solutions. In this paper, we pick a widely used homomorphic-based aggregation and implement it in a realistic testbed so that it can be compared with a simulation-based solution. Specifically, we develop a system that provides privacy with the Paillier cryptosystem and twofactor authentication with ECDSA and OpenSSL certificates. In order to test the system, an IEEE 802.11s-based SG AMI network testbed is constructed with Beaglebone Black boards that imitate the behavior of smart meters. The same network is also simulated in widely used ns-3. The results showed that ns-3 simulation and testbed results are parallel in most cases and the proposed system can perform effectively. However, there are still many differences that need to be taken into account in deploying real systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Leveraging High-Fidelity Datasets for Machine Learning-based Anomaly Detection in Smart Grids

Data-driven intrusion detection systems are increasingly becoming essential for protecting critical cyber-physical infrastructure, such as the power grid, against the growing number of sophisticated cyber-attacks. The development of such tools is reliant on the availability of high-fidelity cyber-physical datasets that cover a diverse variety of potential cyber events. In this work, a high-fidelity smart grid platform is utilized to develop an extensive dataset, which is used to train and test a machine learning-based intrusion detection system. The evaluation of the developed IDS shows robust performance even when tested with statistically diverse test data not used in training.

Hyder, Burhan↗

DOE OSTI2024

Optimizing Grid Regulation With Gravity Storage Systems: A Comparative Analysis With Different Motor Inertias: Preprint

The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between the energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid generation-demand imbalances is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS systems provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 seconds from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS with a high-inertia motor may be able operate over a broader range of frequency variations whereas a low-inertia system may be limited by thermal constraints of the motor.

frequency regulation↗

NASA NTRS2009

Design of a Glenn Research Center Solar Field Grid-Tied Photovoltaic Power System

The NASA Glenn Research Center (GRC) designed, developed, and installed, a 37.5 kW DC photovoltaic (PV) Solar Field in the GRC West Area in the 1970s for the purpose of testing PV panels for various space and terrestrial applications. The PV panels are arranged to provide a nominal 120 VDC. The GRC Solar Field has been extremely successful in meeting its mission. The PV panels and the supporting electrical systems are all near their end of life. GRC has designed a 72 kW DC grid-tied PV power system to replace the existing GRC West Area Solar Field. The 72 kW DC grid-tied PV power system will provide DC solar power for GRC PV testing applications, and provide AC facility power for all times that research power is not required. A grid-tied system is connected directly to the utility distribution grid. Facility power can be obtained from the utility system as normal. The PV system is synchronized with the utility system to provide power for the facility, and excess power is provided to the utility for use by all. The project transfers space technology to terrestrial use via nontraditional partners. GRC personnel glean valuable experience with PV power systems that are directly applicable to various space power systems, and provide valuable space program test data. PV power systems help to reduce harmful emissions and reduce the Nation s dependence on fossil fuels. Power generated by the PV system reduces the GRC utility demand, and the surplus power aids the community. Present global energy concerns reinforce the need for the development of alternative energy systems. Modern PV panels are readily available, reliable, efficient, and economical with a life expectancy of at least 25 years. Modern electronics has been the enabling technology behind grid-tied power systems, making them safe, reliable, efficient, and economical with a life expectancy of at least 25 years. The report concludes that the GRC West Area grid-tied PV power system design is viable for a reliable, maintenance free, long life power system that is of significant value to NASA and the community.

Eichenberg, Dennis J.↗

DOE OSTI2018

A Guide to Implementing Concentrating Solar Power in Production Cost Models

Concentrating solar power (CSP) is a unique form of renewable energy because it can be integrated with thermal energy storage (TES). CSP-TES can provide value to the power grid by supplying a dispatchable source of renewable energy and potentially other grid services such as operating reserves. These characteristics of CSP-TES are often incompletely captured in utility-planning or technology-valuation analyses. We describe an approach for representing the dispatchability of CSP-TES accurately in production cost models, while considering the resource-dependent nature of the technology. We also discuss the challenge of properly representing the ability of CSP-TES to provide operating reserves because of the technology's energy-limited nature. We apply our modeling methods to illustrate how the value of providing dispatchable energy and ancillary services can be captured together and independently. This approach can be applied to most commercial, academic, and open-source production cost models to value CSP-TES appropriately in power system planning studies.

14 SOLAR ENERGY↗

DOE OSTI2012

Probability-Weighted LMP and RCP for Day-Ahead Energy Markets using Stochastic Security-Constrained Unit Commitment: Preprint

Variable renewable generation resources are increasing their penetration on electric power grids. These resources have weather-driven fuel sources that vary on different time scales and are difficult to predict in advance. These characteristics create challenges for system operators managing the load balance on different timescales. Research is looking into new operational techniques andstrategies that show great promise on facilitating greater integration of variable resources. Stochastic Security-Constrained Unit Commitment models are one strategy that has been discussed in literature and shows great benefit. However, it is rarely used outside the research community due to its computational limits and difficulties integrating with electricity markets. This paper discusses howit can be integrated into day-ahead energy markets and especially on what pricing schemes should be used to ensure an efficient and fair market.

17 WIND ENERGY↗

DOE OSTI2025

LLGoMAX : Enhancing Industry-Standard Tools for AC Optimal Unit Commitment

In 2018, the Advanced Research Project Agency – Energy (ARPA-E) launched the Grid Optimization Competition (GOC) [1], a series of competitive challenges intended to accelerate innovation in decision support software used to schedule power grid operations, making them as efficient as possible, while respecting operational constraints of power equipment and operational security. This report covers the participation of the LLGoMAX team—a collaboration of the Lawrence Livermore National Laboratory (LLNL) and ECCO International, Inc.—in Challenge 3 of the competition.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Modeling and Impact of Hyperloop Technology on the Electricity Grid

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for dis-cussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Preliminary analysis of market potential for a hydropneumatic ground-level integrated diverse energy storage system

With the increasing share of renewable energy in the power grid, energy storage is considered a viable and effective solution to mitigate intermittency and improve power system stability. To quantify and estimate market potential for a recently developed Ground-Level Integrated Diverse Energy Storage (GLIDES) technology, the first operation model is developed for this hydropneumatic system and the bilinear pressure-volume terms are then linearized using the binary McCormick Relaxation method to improve solving efficiency. Based on up-to-date 5-min price data in four different real-time markets, several sets of experiments have been designed with three objective scenarios: maximize arbitrage, maximize arbitrage + reserve, maximize arbitrage + reserve + regulation. The experimental results show that the monetary value of GLIDES could be boosted by participation in ancillary markets and that regulation service might contribute more than half of the total revenue, even as high as 90% in PJM and ISONE markets. In addition, a case study for 1-day operation is used to illustrate the volume control of the GLIDES system.

25 ENERGY STORAGE↗

DOE OSTI2023

Resilience assessment and planning in power distribution systems: Past and future considerations

High impact low probability (HILP) events such as hurricanes, heat waves, and floods have instigated widespread power outages and blackouts around the globe in the past decade. With the increasing challenges concerning the threats to the power distribution systems and the growing need to mitigate the impacts of the HILP events, resilience has become a crucial requirement for the power grid infrastructures. Numerous efforts have been made to define, measure, and characterize the resilience of power distribution systems. This study thoroughly reviews the state-of-the-art methods on the existing resilience evaluation framework and metrics. Here, the desirable characteristics of resilience metrics are highlighted, and the challenges associated with formulating, developing, and calculating such metrics are discussed. Next, we detail the state-of-the-art literature on planning solutions to ensure distribution system resilience. This paper aims to extract a deep insight into this challenging and critical research area and envision future opportunities that can guide the power distribution system operators/planners to formulate more effective mitigation strategies to enhance the resilience of power distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A Data-Driven Democratized Control Architecture for Regional Transmission Operators

As probably the most complicated and critical infrastructure system, U.S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users. This paper proposes a data-driven democratized control architecture considering two democratization pathways to assist transmission system operators, with a targeted use case of developing online proactive islanding strategies. Detailed discussions on load capability profiling at transmission buses and disaggregation of DER generations are provided and illustrated with real-world utility data. By Combining network and operational constraints, transmission system operators can be equipped with new tools built on top of this architecture, to derive accurate, proactive, and strategic islanding decisions to incorporate the wide range of dynamic portfolios and needs when facing extreme events or unseen grid contingencies.

Power System Reliability, Islanding, Democratized ↗

DOE OSTI2020

Initial Evaluation of Fuel-Reactor Concepts for Advanced LEU Fuel Development

The National Nuclear Security Administration (NNSA) Office of Defense Nuclear Nonproliferation R&D has commenced a project to evaluate and develop low-enriched uranium (LEU) fuel systems, materials, and fabrication technologies that could be used for compact, advanced LEU (aLEU) fuel, power-producing, high-burnup reactor applications. Potential applications are intended to be off-grid power sources for ship-borne systems and transportable systems to fixed locations, e.g., to a remote base or to a seawater desalination service location. Reactors with these qualities and fuel enrichments between 5 and 20% are atypical. The aLEU Fuel for Nonproliferation Applications Project addresses this technology gap.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations

Rapid and accurate quasi-static time series (QSTS) analysis is becoming increasingly important for distribution system analysis as the complexity of the distribution system intensifies with the addition of new types, and quantities, of distributed energy resources (DER). The expanding need for hosting capacity analysis, control systems analysis, photovoltaic (PV) and DER impact analysis, and maintenance cost estimations are just a few reasons that QSTS is necessary. Historically, QSTS analysis has been prohibitively slow due to the number of computations required for a full-year analysis. Therefore, new techniques are required that allow QSTS analysis to rapidly be performed for many different use cases. This research demonstrates a novel approach to doing rapid QSTS analysis for analyzing the number of voltage regulator tap changes in a distribution system with PV components. A representative portion of a yearlong dataset is selected and QSTS analysis is performed to determine the number of tap changes, and this is used as training data for a machine learning algorithm. The machine learning algorithm is then used to predict the number of tap changes in the remaining portion of the year not analyzed directly with QSTS. The predictions from the machine learning algorithms are combined with the results of the partial year simulation for a final prediction for the entire year, with the goal of maintaining an error <10% on the full-year prediction. Five different machine learning techniques were evaluated and compared with each other; a neural network ensemble, a random forest decision tree ensemble, a boosted decision tree ensemble, support vector machines, and a convolutional neural network deep learning technique. A combination of the neural network ensemble together with the random forest produced the best results. Using 20% of the year as training data, analyzed with QSTS, the average performance of the technique resulted in ~2.5% error in the yearly tap changes, while maintaining a <10% 99.9 th percentile error bound on the results. This is a 5x speedup compared to a standard, full-length QSTS simulation. These results demonstrate the potential for applying machine learning techniques to facilitate modern distribution system analysis and further integration of distributed energy resources into the power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Component level modeling of materials degradation for insights into operational flexibility of Existing Coal Power Plants

Increasingly, coal-fired power plants are required to balance power grids by compensating for the variable electricity supply from renewable energy sources. Fossil-fueled power plants, originally designed to be base loaded, will increasingly need to operate on a load following or cyclic basis. This demanding requirement for operational flexibility needs insights into accelerated material degradation arising due to the harsh operating conditions (e.g., fatigue, early oxide exfoliation due to stresses) along with current damage mechanisms (fireside corrosion, creep and erosion) observed in service. Our research objective is to develop component level modeling toolkit for materials-based degradation for two key mechanisms that can accelerate with cyclic operations. In more detail, this includes the fireside corrosion/steam oxidation/erosion/creep/fatigue of superheaters/reheaters and steam pipework and also the water droplet erosion/ fatigue of last stage steam turbine blades degradation mechanisms, that demand routine and sometimes unplanned maintenance and repair. The innovation is in developing a computational fluid dynamics/finite element (CFD/FE) modeling toolkit for the component level models of the boilers and low-pressure steam turbines in coal power plants that can tackle multidisciplinary failure mechanisms occurring concurrently for extreme environment materials. Lifetime assessment in such environments also needs to account for the unit-specific analyses, operational history and fuel feedstock; this can only be obtained by destructive analysis of components. This, in turn, enables validation of the model toolkits utilizing service feedback data, improving the probability of time/temperature dependent life prediction.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Environmental Validation of Materials and Design Concepts to Enable Operational Flexibility of Existing Coal Power Plants

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Real Time Phasor Analytics (RTPA) and RTPA-SCR System Strength Online Tool

This presentation showcases the Real-Time Phasor Analytics (RTPA) framework for monitoring inertia and assessing system strength in power grids. RTPA is an open-source tool designed to standardize access to data from Power Management Units (PMUs) and Phasor Data Concentrators (PDCs). It facilitates real-time connectivity to multiple PDCs in accordance with the IEEE C37.118-2 standard and supports asynchronous data stream integration. Additionally, RTPA can simulate a PDC server streaming C37.118-2 data and provides Python bindings for seamless interaction with the framework, eliminating the need for direct Rust programming.

24 POWER TRANSMISSION AND DISTRIBUTION↗