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Electric vehicle managed charging can benefit all consumers by supporting grid planning, operation, and reliability – especially complementing high-renewable systems.
As part of the second round of the Washington Clean Energy Fund, Avista Corp received a $3.5 million matching grant in support of a shared energy economy project to test the integration of energy assets–from rooftop solar and battery storage to building energy management systems–that can be shared and used for multiple purposes. The goal of this project is to demonstrate how both the customer and the utility can benefit from this shared energy economy model and demonstrate that the electric grid can become more reliable, efficient, resilient, and flexible. Pacific Northwest National Laboratory was engaged by the U.S. Department of Energy and the Washington State Department of Commerce to work with Avista in assessing the benefits of the shared energy economy model. This report documents the techno-economic assessment of the shared energy economy model, including the definition of use cases and applications, collection and preparation of data and input parameters, development of modeling and optimization methods, and case studies and analysis results.
Present power engineers have been challenged to employ electromagnetic transient (EMT) simulations to study the restoration of wind-dominant grids. This paper addresses this hurdle by developing strategies to: (i) reliably control the dc-link voltage of grid-forming wind turbines with dc-coupled batteries, (ii) tune controllers to stably interconnect wind power plants into a grid under recovery, and (iii) autonomously make restoration decisions if transmission faults occur during restoration. These contributions are assessed via EMT simulations of wind-dominant versions of the WSCC 9-bus grid and a 22-bus power system. Furthermore, this paper is significant to address recommendations by the North American Electric Reliability Corporation.
Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.
Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1-2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1-2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions.
The substantial integration of renewable energy brings significant challenges to balance the system in real time because of the variability and intermittency of renewable power. For the reliable system operation, the frequency regulation service is used to stabilize the system frequency through automatically balancing the system generation and load. On one hand, with the substantially increasing deployment of renewable energy in electricity system, the requirement of frequency regulation (FR) services increases significantly. On the other hand, the current main resource of FR services, the controllable conventional generation, is continuously decreasing in the system generation mix. This means that in the future high renewable penetration power system, additional and alternative reliable FR services providers such as distributed energy storage (DES) resources should be explored. Although the capability of utility-scale energy storage to provide FR services has been demonstrated, the integrated control and dynamic modeling of distributed energy resource (DER) providing frequency regulation grid services has been rarely explored. There are several challenges to adopt DERs to provide reliable grid services as illustrated in FERC 755. First, the distributed installation of DES requires a comprehensive cyber-physical dynamic system (CPDS) modeling to fully consider the impacts of the communication latency variability on its real time FR provision capability. Unlike the conventional generators, there are two-layer communication between DERs and system operators which increase the communication delay. Second, unlike the conventional generators whose dynamic models have been comprehensively studied, the difference among individual DER components' power-dynamic characteristic brings challenges in the accurate dynamic AGC modeling of its power-frequency relationship. Third, the temporal dependent state of charge uncertainty of DERs challenges DERs' power and frequency regulation capacities scheduling in the look-ahead generation scheduling. Therefore, the aggregator should optimize the frequency regulation provision from individual DER in real time. To overcome these challenges, this project proposes a cyber-physical dynamic system (CPDS) model to handle the uncertainty of DERs two-layer communication latency and power dynamics. The variability of DERs' communication delay and dynamic constraints will be comprehensively modeled. The DERs' AGC model with communication delay is designed to validate DESs' frequency regulation services. Like current performance-based frequency regulation evaluation, the delivery of DERs' frequency regulation will be assessed through post-analysis of the actual AGC response with respect to the AGC control signal from system operators. Consequently, the reliability improvement with DERs providing reliable frequency regulation services can be evaluated from a comprehensive perspective considering all the dynamics of communication and power.
Numerical modeling and simulation (M&S) tools play a key role in the research, development, and overall safety assessments of next-generation nuclear energy systems. One such tool, Bison, is a nuclear fuel performance code that is applicable to many fuel forms (e.g., light-water reactor fuel, oxide and metallic fuel for fast reactors, tri-structural isotropic (TRISO) fuel, and plate fuel), and it uses the finite element method to model the thermo- mechanical response of nuclear fuels. One fuel form widely utilized in Generation-IV high-temperature gas-cooled and fluoride- salt-cooled nuclear reactor concepts is TRISO fuel. Recently, Bison’s capabilities were significantly expanded to enable it to model the performance of TRISO particles and compacts. It is important that Bison’s computational results be reliable and predictive, since this code is used to inform high-consequence decisions. The various processes developed to address this issue generally entail two fundamental steps: verification and validation (V&V). Verification ensures that the code functions correctly and is reliable. Code/solution verification, code benchmark, and software quality assurance exercises are examples of verification activities. On the other hand, validation is the process of assessing a code’s capability to accurately model physical problems. Comparisons between code results and experiments quantify the validation level. Application of V&V procedures is crucial to the development of computational tools that are free of coding mistakes and can accurately represent reality. The current study presents an overview of Bison V&V activities relevant to the TRISO fuel concept, which include code/solution verification exercises, CRP-6 Benchmark—a Coordinated Research Program through the International Atomic Energy Agency (IAEA)—exercises, and validation exercises with the Advanced Gas Reactor (AGR)- 1/2/3/4 experiment series.
Here, from within the umbrella of the Simplified Human Error Experimental Program (SHEEP) framework, this paper analyzes human performance differences between professional and student operators when using a simplified simulator (i.e., Rancor Microworld). This paper represents a crucial step in understanding the fidelity of the simplified simulators and student operators within the SHEEP study. This paper explores a randomized factorial experimental design that features two independent variables: participant type and event class. Six human performance measurements are considered in the experiment. The experiment is conducted using 20 professional reactor operators employed at actual nuclear power plants (NPPs), along with 20 trained students. The experimental data are analyzed via statistical analysis methods. Finally, this paper examines the differences in human performance between actual operators and students when using Rancor Microworld.
Traditional methods for assessing the resource adequacy (RA) of a power system are becoming obsolete due to emerging trends such as the increasing deployment of variable renewable energy and storage. Consequently, analysts are recommending that RA be assessed using a Monte Carlo simulation approach that models chronological power system operations over many instances of possible operating conditions. However, this approach is necessarily more complex and computationally demanding, which is an obstacle to real-world implementation. Here, in this study, we investigate which operational details of power systems are important to capture in order to accurately evaluate a system's RA, versus details that add complexity but do not meaningfully affect RA results. To do so, we develop a probabilistic RA assessment framework by adapting an existing production cost model and apply it to a case study based on the IEEE Reliability Test System. Our results indicate that multi-year data, storage dispatch, and transmission limits are key details to incorporate. Accurate RA results can be obtained using non-economic dispatch strategies as long as they are coordinated with detailed operational strategies. We also demonstrate how popular expectation-based RA metrics can mask important differences in the characteristics of loss of load events.
Data scarcity in human reliability analysis (HRA) has been a major challenge in the quantification process. Many institutes have collected HRA data through experiments using full-scope simulators with actual operators. Nevertheless, there are still some limitations to relying solely on full-scope studies. This paper aims to propose how full-scope data collection studies can be supported through the Simplified Human Error Experimental Program (SHEEP). The SHEEP framework was developed by Idaho National Laboratory (INL) to collect HRA data through a simplified simulator and student operators. This paper introduces the major tasks in the SHEEP framework, with a particular focus on differences that arise due to participant type (i.e., student vs. actual operator), based on experiments using a simplified simulator (i.e., the Rancor Microworld). This paper also describes whether the data collected via this approach could support a representative full-scope data collection study (i.e., the HuREX study) based on the experimental data.
The project investigates Measurement, Reporting, and Verification (MRV) methods for Enhanced Weathering and Marine Carbon Dioxide Removal (mCDR) technologies. Enhanced Weathering accelerates natural mineral processes to capture CO2, while mCDR leverages oceanic processes for carbon sequestration. The study focuses on developing standardized MRV protocols, assessing environmental impacts, and evaluating the scalability and economic feasibility of these carbon sequestration methods. The goal is to ensure reliable and transparent data for validating and improving CO2 removal technologies.
Abstract. The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model and observational differences is thus vital to reduce uncertainty in the impact of aerosols on the climate. These reported discrepancies arise from the different methods of separating the components of aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model (GCM) output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties. Cloud adjustments remain a significant source of uncertainty, particularly for ice clouds. However, they are consistent with the uncertainty from observation-based methods, with the liquid water path adjustment usually enhancing the Twomey effect by less than 50 %. Depending on different sets of assumptions, this work suggests that model and observation-based estimates could be more equally weighted in future synthesis studies.
The integration of Inverter-Based Resources (IBRs) into power systems, including both transmission and distribution networks, poses challenges for studying grid dynamic behaviours under various operating conditions and system events. This paper addresses the urgent need to investigate the influence of IBRs on grid dynamics, and their potential to enhance power system reliability and resilience. We propose a flexible, scalable transmission and distribution co-simulation platform, using opensource tools only for assessing the impact of grid-following (GFL) and grid-forming (GFM) IBRs on dynamic stability at various renewable penetration levels (up to 100%). This platform enables researchers to explore different contingencies at transmission, distribution, or both, providing a comprehensive evaluation of grid status. A series of case studies, including both small and large T&D systems with varying GFL/GFM configurations and contingencies, have been conducted. The results not only robustly validate our co-simulation framework, but also provide invaluable insights for effective IBR management in the power grid.
Residential space-conditioning-based demand flexibility (DF) has become an increasingly sought-after method for demand-side load management to enhance grid reliability and facilitate integration of renewable energy generation. However, predicting the effectiveness and flexibility of residential DF resources is challenging due to the variability in household energy use behaviors. Current estimates show that only 50 % of projected savings from DF resources are actualized due to regulatory, technological, and social barriers. From a household perspective, concerns over thermal comfort during space conditioning-based DF events significantly impact participation decisions. Currently, there is a very limited understanding of how thermal comfort during space-conditioning-based DF events in real-world settings impacts household energy use behaviors and, consequently, the success of DF programs in achieving targeted savings. This paper proposes a method to comprehensively assess the thermal comfort implications of DF strategies and presents results of their impacts on DF event participation decisions and demand savings. Here, the proposed method was applied to a heat pump DF field study in Cordova, Alaska. The study’s key findings are: 1) DF event setpoint offsets that maintain indoor operative temperatures between 18 to 22 °C (65 to 71°F) may be preferred in Cordova, Alaska; 2) Household-level thermal comfort is more sensitive to the duration of the DF event than to the degree of temperature offset from baseline conditions; 3) The delayed impact of changes in indoor operative temperature in response to setpoint offsets, both during and after a DF event, influences occupants’ thermal comfort perceptions and willingness to persistently participate in events. The findings from application of the proposed method can help inform future larger-scale occupant-centric DF programs as it can capture information not readily available through utility and device-level energy use data. Thus, it can supplement these sources and help program administrators develop occupant-centric DF strategies, enabling more accurate predictions of participation rates and savings estimates for space-conditioning-based DF programs.
The existing fleet of commercial nuclear power plants (NPPs) are an important asset in the nation’s portfolio of electrical generating resources. Their continued safe and reliable operation are critical to providing a large source of carbon-free electricity to power the nation’s economy. The United States Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program develops the scientific bases, methods, and tools, for the continued safe and economical operation of the nation's commercial NPPs. The Plant Modernization Pathway within LWRS Program focuses on providing guidance to industry on the full-scale implementation of modernization solutions for NPPs that significantly reduce the technical and financial risks associated with modernization. This research is focused on helping the nuclear industry understand how to digitize and digitalize their NPPs so that they can design their modernization solutions to be scalable, sustainable, and integrated both laterally and horizontally within their organization. That is, this research creates a digital transformation in NPPs by reshaping cultural mindsets and by identifying business efficiencies. In partnership with industry, and using four previously established guiding principles for digitalization, this research supported NPP modernization through assessing readiness for digitalization as a means to achieve integrated operations for nuclear. Specifically, this research created an assessment to review an entire organization’s work processes to gather information about the digitalization health of the plant. The assessment tools were administered to plant employees, and the results were used to develop a digitalization plan. The survey assessment identified the optimal candidate processes that would most benefit from a digitalization initiative which were revealed through analytical frameworks. One analysis calculated mean digitalization health indicator scores for all endorsed activities which allowed the researchers to rank and color code the results for easy identification. Individual health indicator scores are also provided, should our industry partner wish to understand these findings according to their own organizational priorities, business considerations and desired end-state. The results were also analyzed from the perspective that organizations are comprised of different types of innovators (e.g., generators, optimizers, conceptualizers, and implementers), which differentially affects the organization’s ability to comprehend and adapt to change (i.e., opportunities to innovate). Understanding the relative composition of innovator types at an NPP allows them to gather insights into the strengths and weaknesses they have in innovating how work is performed. For the utility that partnered with this research team, the results showed that implementers make up the largest portion of respondents and conceptualizers the smallest portion. Knowing the proportion of innovator types gave this organization insights on how they can effectively implement their innovation solutions. Additionally, the results were analyzed from a technical, economic, and risk perspective to identify and quantify work reduction opportunities (WROs). Recognizing that not all cost-saving opportunities are the same, a Technical, Economic and Risk Assessment (TERA) was performed to evaluate WROs to identify areas of greatest potential and lowest risk. The key results from TERA included a digitalization opportunity score for each activity, and a calculation of potential cost savings. These two outputs formed the bases for calculating a priority index and rank for the activities/processes assessed. From the prioritization calculations, TERA can then help the utility 1) decide what digitalization priorities to invest money in implementing and then 2) calculates how much should be invested in the digitalization initiatives selected to achieve cost savings and/or an acceptable return on investment. Last, onsite interviews revealed several inefficiencies in the standard work processes that occur cross-departmentally that are due to the absence of digitized and digitalized processes. Examples of these include time spent scanning paper documents and then uploading the documents electronically, obtaining signatures, and searching for desired information. This represents a digital but not digitalized process. Over 15 opportunities to improve work processes were identified through this multi-method digitalization assessment. The various analytical assessments used (e.g., TERA, digitalization health indicator scores), as well as discussions with the utility partner, corroborated that all the opportunities identified had a strong potential to make work processes more efficient and to improve overall performance of the NPP.
Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract)
Through transformative science and innovation, the U.S. Department of Energy's (DOE's) National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory's (NREL's) Flatirons Campus helps lead the way to a sustainable energy future that powers America with significant levels of reliable, low-cost, and accessible wind energy. Since 1976, NREL has provided an ideal environment for the research and development (R&D) of advanced energy technologies through: Wind energy resource assessments, World-class research facilities, High-performance computing and modeling, Data and technology analysis, Manufacturing breakthroughs, Environmental analysis and wildlife conservation efforts, Education and training programs for a future workforce. These activities enable the innovations needed to advance U.S. wind systems, address market and deployment barriers, and drive down the cost of wind energy with more efficient, more reliable, and more predictable wind energy systems. This report provides an overview of the achievements NREL delivered on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during the first and second quarters of Fiscal Year (FY) 2020.
The Defense Waste Processing Facility (DWPF) is currently preparing to initiate processing of Sludge Batch 10 (SB10), which is comprised of material from Tanks 11H, 13H, 15H, and 26F, Alternate Feed Stock-2 and Sodium Reactor Experiment material from H-Canyon. Frit 473 (8B 2 O 3 -8Li 2 O-5Na 2 O-79SiO 2 , in weight percent) was recommended for sludge-only (SO) and coupled processing with the Salt Waste Processing Facility (SWPF) based on previous assessments of SB10 projections with the DWPF Product Composition Control System (PCCS) glass property models and their associated Measurement Acceptance Region (MAR) constraints. Due to the lower processing rate of Sludge Batch 9 (SB9), the heel in Tank 40 is anticipated to be greater than 40 inches at the projected start of SB10 processing to meet the upcoming Accelerated Basin Deinventory addition dates in Tank 51 for Sludge Batch 11. In December 2021 and February 2022, Savannah River Remediation System Planning provided updated SB10 Tank 40 blend projections based on heels of 74 inches, 84 inches, 94 inches and 103.4 inches. Savannah River Mission Completion (SRMC) subsequently pursued Wash Cycle Y to further reduce the total sulfur in the sludge batch and increase processing flexibility at DWPF. In August 2022, SRMC System Planning provided an updated SB10 Tank 40 blend projection with a 76 inch-heel representing Decant Y2 and 60 kilogallons of bearing water inleakage that is anticipated during the Tank 51 to Tank 40 transfer. The objectives of this task were to: • Determine the impact on the operating windows for SO and coupled processing • Determine whether any composition gaps exist between the already completed SB10 variability study and the reprojected SB10 glass composition region • Compare the SB10 reprojected glass composition region to the DWPF PCCS model development and validation ranges to ensure that compositional gaps do not exist between the data sets. This report documents the results of these evaluations. Calculation-based frit assessments were performed using the DWPF PCCS glass property models and their associated MAR constraints. Evaluated parameters for coupled processing included the following transfer volumes per Sludge Receipt and Adjustment Tank batch: 5700 gallons of Tank 40 sludge, 2400-4500 gallons of the SWPF monosodium titanate (MST) and sludge solids stream, and 15,000 gallons of strip effluent. Based on these MAR assessment results, Frit 473 remains viable for SB10 processing. A target waste loading (WL) of 36% is possible for SO operation and single strike (0.4 g/L MST) coupled processing up to 600 mg/L of SB9 insoluble sludge solids at a nominal transfer volume of ~2800 gallons. Increasing the single strike transfer volume to 4500 gallons may reduce the maximum WL below 40%. Operating windows are 12 percentage points for SO processing and 14-16 percentage points for coupled processing. Frit 625 allows for a target WL of 36% and is acceptable for use during the SB9 to SB10 transition to deplete remaining inventory as needed. Operating windows are 8 percentage points for SO processing and 12-14 percentage points for coupled processing. Predictive PCCS evaluations performed at DWPF will provide insight into batch-specific acceptability at desired WLs for compositions having expected oxide ratios during processing versus the extreme vertices (corner points) evaluated in this study. The reprojected SB10 glass composition region generally overlaps the previously evaluated SB10 variability study composition region. Thus, the minor composition shift of these updated SB10 projections indicates that no additional glasses are necessary to demonstrate acceptability relative to the chemical durability of the Environmental Assessment benchmark glass and predictability using the current PCCS models for durability. Based on a comparison of the PCCS model development and validation data to the reprojected SB10 glass composition region, the viscosity and liquidus temperature models will reliably predict SB10 compositions. No additional glasses are necessary to demonstrate predictability of these models.