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The increasing share of IBRs challenges the stable operation of our power systems. Leveraging an oscillation event in Kaua`i Island as an example, we showcase the root cause of this event and highlight the potential of grid-forming inverters to reduce the stability risks in high-IBR power systems.
As the power grid is undergoing rapid transformations, numerous questions are emerging about its vulnerability to wide-area extreme events (WAEE), which could influence its operations. Relatively few analyses have been conducted to date regarding the impact of correlated outages during WAEEs on the grid’s ability to balance resources with demand. This study addresses this gap by conducting a resource adequacy analysis for a hurricane-inspired WAEE on a 2035 synthetic power grid system for the United States. A sensitivity analysis was also conducted to characterize the relative impact of weather on unserved energy. Our results indicate that although the magnitude and duration of the shortfalls vary depending on weather conditions, persistent shortfalls are observed in some regions. Initial explorations indicate a strong correlation between transmission-constrained regions and regions with persistent shortfalls. Future work could generate empirically-grounded representations for generator outages as well as conduct causal analyses of these shortfalls to improve understanding of drivers as well as possible mitigation strategies. Continued exploration of extreme weather impacts on the grid is important to develop more robust understanding of the reliability and resilience of our power systems, especially as they undergo rapid transformations.
The grid voltage modulated direct power control (GVM-DPC) is a novel power regulation method for grid-connected voltage source inverters (VSI). However, interactions between real and reactive powers still exist in GVM-DPC under unbalanced grid voltage condition, which may deteriorate its transient performance. Here, this article proposes a power decoupling method for GVM-DPC-based VSI in unbalanced systems based on the dynamic feedforward power compensation strategy, which can reduce the coupling magnitudes of real and reactive powers in the transient stages. First, the power coupling mechanism of positive and negative sequence real and reactive powers of VSI under the unbalanced voltage condition is analyzed. Then, the power coupling magnitudes (PCM) are derived according to relationships among the positive sequence components of real power, reactive power and point of common coupling (PCC) voltages. Next, the PCM are compensated into the GVM-DPC for the better decoupling performance. Furthermore, a stability analysis of the proposed control system is studied based on the impedance model, and a comparison with the traditional power decoupling method based on virtual impedance is conducted to show the superiority of the proposed method. Finally, simulations, hardware-in-loop test and experiment are conducted to validate the effectiveness of the proposed method.
Improving the efficiency of electrical components is critical in reducing energy consumption for various industrial and residential applications, ranging from rotating machinery to all electric devices and electric vehicle (EV) components to power grid systems. Substituting Cu wires with reduced resistance conductors that incorporate carbon nanotubes (CNTs) into Cu─ultraconductive Cu (UCC) composites─has recently been considered a promising strategy to improve energy efficiency, power density, and/or performance across various applications. Here, in this study, we created stable material formulations [CNT-containing polyvinylpyrrolidone (PVP) in dimethylformamide (DMF) solution] and utilized commercially viable fabrication approaches (electrospinning and magnetron sputtering) that produced high-performance multilayered tape-based UCC composite architectures. Increasing the CNT volume fraction by sequential layering of the structure with additional Cu-CNT layers showed a nearly stepwise improved performance in electrical and mechanical properties. This study also provides valuable insight into the effectiveness of nitrogen doping in modifying the conductivity of the CNT matrix. Fabricated prototypes demonstrated a >10% increase in current carrying capacity and >10% improvement in mechanical strength compared to those obtained on pure Cu. We believe that the properties demonstrated here, combined with the scalable manufacturing pathway of our approach, pave the way in designing future advanced conductors for diverse energy efficient and high-performance electrical systems and applications.
Extreme weather events, such as hurricanes, severe thunderstorms, and floods can significantly disrupt power grid systems, leading to electrical outages that result in inconvenience, economic losses, and life-threatening situations. There is a growing need for a robust and precise predictive model to forecast power outages, which will help prioritize emergency response before, during, and after extreme weather events. In this paper, we introduce machine-learning models that predict power outage risk at the state level during and after extreme weather events. We jointly utilized two publicly available datasets: the U.S. historical power outage data collected by the Environment for Analysis of Geo-Located Energy Information (EAGLE-I™) system, and the National Weather Service historical weather alert data sets. We highlight our initial result and discuss future work aimed at enhancing the model's robustness and accuracy for real-world applications.
This report documents the Resilience Enhancements through Deep Learning Yields (REDLY) project, a three-year effort to improve electrical grid resilience by developing scalable methods for system operators to protect the grid against threats leading to interrupted service or physical damage. The computational complexity and uncertain nature of current real-world contingency analysis presents significant barriers to automated, real-time monitoring. While there has been a significant push to explore the use of accurate, high-performance machine learning (ML) model surrogates to address this gap, their reliability is unclear when deployed in high-consequence applications such as power grid systems. Contemporary optimization techniques used to validate surrogate performance can exploit ML model prediction errors, which necessitates the verification of worst-case performance for the models.
There has been an increase in extreme weather events, posing a threat to power grid systems, potentially influenced by factors such as population growth, changes in ecosystems, land cover, and land use in the service area, as well as the growth of certain vegetation types. This research seeks to develop a predictive model to mitigate potential damages caused by future winter storms. This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the number of power outages experienced at the county level, geographic details, weather information, and lagged outage and lagged weather data. The developed models were broadly divided into two groups, with six models in each group - one group without optimization and another with optimization, totaling 12 trained models. For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. In results, when comparing Group 2 (the optimized group) with Group 1 (the non-optimized group), it was found that optimization did not always lead to a reduction in RMSE and Mean Absolute Error (MAE). However, in terms of Mean Directional Accuracy (MDA), while all results in Group 1 were below the baseline accuracy of 0.33, all results in Group 2 exceeded 0.33, with some cases showing an increase of more than three times the baseline. The results indicated that, in the optimized model group, Population and Pressure were the most influential factors when using current weather data and geographical information. When using lagged data, lagged recorded outages and lagged Pressure emerged as the most significant factors. Among the 12 developed models, the L-1-2-O model showed the lowest RMSE and MAE, as well as the highest accuracy, with values of 390.62 households and 168.13 households, respectively. To normalize the RMSE and MAE values, each metric was divided by the average number of households among the counties in Texas. For the L-1-2-O model, the scaled RMSE was 0.88% and the scaled MAE was 0.38%. In terms of MDA, which indicates the accuracy of the prediction direction, the L-1-O model achieved the highest score of 0.41. Although this study focused on Texas, which suffered the greatest impact from the winter storms in 2021, with additional validation, the methodology used in this research could be applied to other regions.
Many scientific applications that support the mission of the US Department of Energy (US-DoE) require modeling complex engineering and/or physical systems. Here, examples of such complex systems arise from: (a) materials science to develop new compounds with exceptional mechanical and thermodynamical properties (e.g., resistance to mechanical stresses and high temperatures), (b) structural and nuclear engineering to model the temporal evolution of the structural damage of concrete shields exposed to continuous neutron and gamma radiations emitted by the nuclear reactor core, (c) urban sciences (e.g., transportation and smart buildings), and (d) power grid systems.
As increasing amounts of renewable energy (RE) resources are incorporated into the bulk-power grid, power system oscillations are expected to change. This work investigates how RE generation impacts the frequency and damping ratio (DR) of two dominant inter-area modes in the U.S. Eastern Interconnection (EI) using regularly updated estimates collected over a 12-month period. Quantile regression is used to derive the correlation between operating conditions and mode properties, and a bootstrap method is used to quantify the uncertainty associated with the correlation estimates. Results show that with an increase in system load, the frequency of a mode decreases and DR increases. Evidence that increasing RE generation results in an increase in frequency and decline in DR was found for one of the two modes studied. This work shows that increasing RE levels will impact the properties of inter-area oscillations in the EI, but it does not indicate the presence of immediate threats to grid stability. The outlined approach can be used to periodically assess changing mode properties as RE levels continue to grow and flag stability concerns before they become serious reliability threats.
Prescient is a software package for simulating electricity grid (power systems) operations, under both nominal and extreme event conditions. Core functionality includes discrete event simulation, commitment and dispatch optimization, probabilistic scenario construction, and reporting/visualization. SAND2020-0169 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
This talk will be about the Sun: how it changes with time, its magnetic cycle, flares, and the solar wind. The solar wind and what space is like between the Sun and Earth will be presented. Also, the Earth, its magnetic field, how the solar wind interacts with the Earth, Aurora, and how these affect human systems will be discussed. These interactions dictate how we build our systems in space (communications satellites, GPS, etc), and some of our ground systems (power grids). Some simple classroom activities will be presented that can be done using new data from space that is available daily on the internet, and how you can use the internet to get space questions answered within about 1 day. Finally, some career opportunities for jobs related to space for the future will be discussed.
Renewable energy sources are usually diffuse and require large facilities. Biofuels work better, are more economical to produce for ground transportation, but sharply increase competition for food croplands. Noble laureate Richard Smalley (deceased-2005) conceptual 20 TWe power generation covers hundreds x hundreds of miles. Combined with Fuller s superconducting power grid system would enable renewable planetary energy. A solar-wind project in Australia will have a 7km diameter collector interfacing with a 1 km tower to extract 200 MW from wind turbines mounted at the base. GE Energy s 3.5MW Wind Turbine is large and placing this in perspective, it is as if one were rotating a Boeing 747-200; the blade diameter is that large. Wind turbines are rapidly gaining popularity in Europe and photovoltaic (PV) is expected to also expand rapidly. It becomes clear that we need (and still have time) to develop new sources of energy. Hf 178 bombarded by X-rays produces Gamma-rays for heating. The reaction stops when the X-rays stop; the half life is about 30 years and seems manageable vs 30 000 years. Water splitting needs to be perused as do ultra fast ultra intense laser applications in terms of fusion and new materials developments including new ways to strip and re-bind hydrogen into fuels. New methods and tools for development are being found in quantum mechanical applications to macro-systems and need to be developed into a set of new tool boxes for development of these new energy sources.
Both steady-state and dynamic simulations have been widely used by HVAC&R industry to support product/equipment development for decades. Steady-state simulation focuses on the system mass, energy and momentum balance of an equilibrium state. It is based on high-fidelity components models, and thus is suitable for system and component design optimization. Dynamic simulation studies the system transient response and is generally used for controls development and verification. It usually does not require rigorous component models of high accuracy because 1) the commonly used PID control is feedback control whose control performance evaluation doesn’t require high fidelity system/plant model; 2) high-fidelity dynamic model significantly increases the number of equations and variables and creates tremendous challenge for math solver. For supervisory control, transactive control or optimization of an integrated building system, the HVAC&R equipment is often one of the sub-components to be controlled. High-fidelity equipment models are required for accurately evaluating control strategies. In addition, building equipment manufacturers have developed a lot of high-fidelity steady-state equipment/component models per their expertise. Thus, a platform that can integrate OEM high-fidelity steady-state model with dynamic building simulation and/or electric power system & grid simulation to support the development and verification of supervisory control for integrated building systems is necessary. In this study, ORNL’s heat pump design tool (HPDM) is utilized to develop a co-simulation platform for supervisory control and optimization in integrated building systems. It is based on a model that integrates high-fidelity steady-state simulation equipment models with dynamic building simulation. A practical case of using the proposed co-simulation platform to develop and evaluate the supervisory control and optimization is presented and discussed.
A dense β″-alumina/zirconia composite solid electrolyte and process for fabrication are disclosed. The process allows fabrication at temperatures at or below 1600° C. The solid electrolytes include a dense composite matrix of β″-alumina and zirconia, and one or more transition metal oxides that aid the conversion and densification of precursor salts during sintering. The composite solid electrolytes find application in sodium energy storage devices and power-grid systems and devices for energy applications.
The severity of the economic impact caused by grid outages has been the driving factor for creating innovative solutions that increases interest for the deployment of distributed energy resources to reduce the impact of grid outages. This paper presents a novel topology for providing export power applications. Proposed topology can be used for grid (primary)-side of bi-directional wireless power transfer (WPT) systems, mobile energy storage systems (ESSs), and electric vehicle (EV) batteries. The proposed concept here achieves energy transfer between sources by using a hybrid frequency bi-directional ac/dc converter without an additional front-end converter stage compared to the conventional systems. Due to inherent merit of the proposed bi-directional ac/dc converter, ac input with 60 Hz grid frequency can be directly transferred through the wireless coils and can be directly converted to the dc. Results demonstrate the performance and the functionality of the single-phase bi-directional system with the input / output of 110 V ac,rms and 200 V dc at 1 kW power. Full paper will demonstrate the converter operation at 10 kW power level with additional features and control aspects.
Hardware-in-the-loop (HIL) evaluation is a method in which the test subject is split into a physical part and a simulated part, and these parts are connected with interfaces to form a combined physical-numerical system. Power-hardware-in-the-loop (PHIL) systems, where actual hardware is connected to a real-time model, can exhibit issues of instability, inaccuracy and errors when operating in a closed-loop. The challenges of generating a PHIL setup are that, first, because of the limited dynamic response of the different parts of the system, the test results might be inaccurate, and, second, because of the high frequency noise introduced by the sensors to the closed-loop system, it can be difficult to design a compensator for the real-time emulator response, while stabilizing the closed-loop system at the same time. In this paper, different parts of a PHIL system are characterized, and the feedback compensator system design is proposed for the stable operation of the closed-loop PHIL system. The issues as observed in a PHIL system without any compensator are demonstrated using experimental results, and the effectiveness of a first order phase lead compensator is validated.
As distributed energy resources and smart devices become omnipresent in the electrical power grid, transactive energy control mechanisms are evolving. From real-time to day-ahead markets, these transactive energy algorithms involve more and more agents, whose behavior is going to affect the transmission and generation network. In order to study the interaction between the wholesale and retail energy markets, extensive co-simulations are performed. To be able to redesign, evaluate, and verify new control algorithms, the simulations need to provide results in a fast and reliable manner. This work has built and tested a transactive distribution grid model, the DSO-Stub, meant to offer a configurable distribution retail market while ensuring the computational burden is not significantly increased.
With increased attention on grid-forming inverters as a power system stabilizing device during high shares of inverter-based resource operations, there is a present need for a transparent and methodical investigation of the inverted and direct power to frequency control capabilities and impacts of these devices on emerging power systems. Here, analysis of the frequency dynamics of the droop-controlled grid-forming inverter and the synchronous generator illuminates the inverted active power-frequency relationship and the frequency response order reduction, forming the basis for novel, nonlinear frequency control approaches. Device-level electromagnetic transient domain simulations corroborate the order-reduction findings, establish that a properly designed dc-side system has a negligible impact on power transfer and will not impede frequency regulation, and confirm the primary frequency response improvement with nonlinear control. Simulations of the 9- and 39-bus test systems validate the order reduction and associated decoupling of the nadir and rate of change of frequency in larger networks. Oscillatory mode analysis confirms the grid-forming benefit of increased damping; decreased damping is observed at shares above 80%, but not at 100%. Finally, simulations on a validated Maui power system model with a 96% of inverter-based resources model yield a trend toward a first-order response.