Interconnections Seam Study [Slides]
The Interconnections Seam Study analyzes the grid impacts of increased connections between the Eastern and Western Interconnections in North America.
17 WIND ENERGY↗
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Power System Event Identification with Transfer Learning Using Large-scale Real-world Synchrophasor Data in the United States
The lack of sufficient labeled events and long training time limit the applicability of deep neural network-based power system event identification using synchrophasor data. In this paper, we propose to leverage transfer learning technique to boost the reliability and reduce the required training time of neural classifier for power system event identification. We use the weights of a neural classifier trained on one transmission system as the initial parameters of another neural classifier for a different transmission system. Numerical tests with real-world synchrophasor data from the Eastern and Western Interconnections of the United States show that the proposed transfer learning approach is very effective in not only improving the training reliability but also reducing the training time.
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Synchronized Phasor Measurement Unit-based Central Controller for Normal and Contingent Operation of a High-Capacity HVDC Macrogrid for the Continental US
This paper presents a Synchronized Phasor Measurement Unit (PMU) based controller to support the coordination and operation of a high-capacity high-voltage direct current (HVDC) macrogrid transmission for the continental US. The proposed controller allows for optimal scheduling of each segment of the multi-terminal HVDC network under normal and contingent conditions, while considering minimum levels of frequency response requirements. The performance of the proposed controller is tested on a 100,222-bus representation of the US Eastern and Western interconnections. Results show that a centralized PMU-based controller improves the frequency nadir of the HVDC macrogrid under contingency conditions by 25.9%. Furthermore, the primary frequency response (PFR) of the HVDC macrogrid is increased by 33.4%. The performance of each interconnection under the largest credible contingency conditions is also discussed.
Mohan, Nihal↗
Grid Ready, Flexible Large Power Transformer
GE Research and its partner Prolec GE have proposed a novel and breakthrough concept to improve the flexibility and interchangeability of large power transformers (LPTs). LPTs refer in this report to transformers with a power rating equal or greater than 100 MVA. The concept consists of a 3-phase autotransformer bank having one specified high voltage side (HV) voltage and three configurable low voltage side (LV) voltage taps. Moreover, the leakage impedance is adjustable offline using taps which allows to accommodate short-circuit requirements for a wide range of LPTs applications. The research performed aimed to develop and validate a cost-effective design of a flexible LPT and to demonstrate its competitiveness with the LPT fleet currently in operation in the U.S. The concept is intended for the long-term replacement of existing LPTs. In this project, the U.S based population of LPTs was analyzed to determine their commonality in power, voltage classes, and impedance range. Up to 9,700 units, currently deployed in the NERC’s Eastern and Western interconnection regions, were evaluated. This analysis yielded the specifications of two flexible LPTs to be designed to demonstrate the manufacturability and competitiveness of the concept: a 345 kV, 230 MVA and a 230 kV, 100 MVA LPTs. The two LPTs are both configurable to 161 kV, 138 kV, and 115 kV on the LV side and allow leakage impedance variation between 4% and 14%. The design output showed that the flexible LPT is manufacturable and competitive with conventional LPT. The proposed design has a power efficiency of 99.5% and a total active part weight increase ranging only between 15% and 28% relative to conventional LPTs it can be substituted with. Indeed, for the flexible LPT, the higher the LV side voltage is, the higher is its power rating. Hence, flexible LPTs are more competitive when compared with the largest conventional LPT they can replace. Power systems analyses were carried out to evaluate the performance of the flexible LPT when replacing an existing conventional LPT. These analyses showed that short-circuit and inrush currents will remain respectively within 15% and 9.4% of their original levels suggesting that no substations equipment is likely to be replaced due the installation of the flexible LPT. In addition, the analyses showed that the transient overvoltage anticipated at the impedance configuration switch will be manageable and will not pose any particular risk during lightning or switching events. This research proved that the deployment of flexible LPTs in the U.S power grid can greatly improve the grid reliability and its resiliency especially in the event of the loss of multiple LPTs. Moreover, the concept can significantly help reduce LPTs’ spare inventory by providing a high spare coverage level. The performed analysis showed that the designed 345 kV flexible LPT can serve as a spare for up to 34 units currently in operation in the U.S while the designed 230 kV flexible LPT can replace up to 101 units. Flexible LPTs offer new opportunities to U.S utilities to improve interchangeability of LPTs in the transmission system and to better strategize the upcoming renewal of about 70% of the population of LPTs anticipated to retire within the next two decades.
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Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data
As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.
Synchrophasors, Machine Learning, System-wide Even↗
Machine Learning Guided Operational Intelligence from Synchrophasors (Final Report)
Schweitzer Engineering Laboratories (SEL) and Oregon State University (OSU) received over 27 terabytes of electrical power system phasor measurement unit (PMU) data for the Eastern, Western, and ERCOT interconnections. The dataset includes measurements spread across 446 PMUs from early 2016 to mid 2018 depending on the interconnect. The full dataset was split into a training and test (holdout) dataset by PNNL. All data was received in the Apache Parquet format. The overarching goal of this project is to develop and execute a strategy to mitigate data anomalies, perform analysis on the dataset, and detect anomalous events in the data.
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Frequency Response and Congestion Management using Multi-Terminal HVDC Overlay
Continental level power system interconnections using a High Voltage direct current (HVDC) transmission lines have been considered to bring economic benefits such as interregional power exchange. This paper describes the modeling process and studies the technical benefits of having multiple HVDC lines, including a grid of HVDC lines (macrogrid) configuration, connecting the North American Eastern and Western electric power interconnections. The models developed provide steady state and stability analysis for multiple HVDC overlay topologies and provide technical benefit analysis in terms of frequency response and congestions management. The paper also provides a comparison of different HVDC topologies performance and their grid support in case of major disturbances
Tbaileh, Ahmad↗
The North American Renewable Integration Study (NARIS): A U.S. Perspective
The North American electric power system is undergoing significant change, with renewable resources now contributing more generation than ever before. This transformation is poised to continue given decreasing technology costs and ambitious decarbonization goals at the federal, state, local, corporate, and consumer levels. The North American Renewable Integration Study (NARIS) aims to inform grid planners, utilities, industry, policymakers, and other stakeholders about challenges and opportunities for continental system integration of large amounts of wind, solar, and hydropower to support a low-carbon future grid. The National Renewable Energy Laboratory (NREL) conducted a detailed, continent-wide analysis with planning scenarios of transmission, generation, and demand to reach 80%–92% carbon reductions (from 2005) for the Canadian electric power system, and up to 80% reductions continent-wide. We used a suite of models to study future scenarios and gain insights, including potential impacts on costs, emissions, resource adequacy, and the specific technologies that help enable the transition to a low-carbon grid. Our analysis had a particular focus on the potential role of cooperation among the three North American countries and between regions within each country, and how transmission can support sharing of supply and demand diversity. The NARIS project began in 2016. This report describes a U.S. perspective in coordination with the U.S. Department of Energy, and a companion report describes a Canadian perspective in coordination with the Natural Resources Canada. NARIS was an extension of a previous body of work, including the Western Wind and Solar Integration Study, the Eastern Renewable Generation Integration Study, Interconnections Seam Study, and the Pan Canadian Wind Integration Study. NARIS analyzed the entire continent in detail while studying higher renewable generation than previous studies. The scenarios in NARIS were informed by the goals in the Mid-Century Strategies for the Paris Agreement in each country.
14 SOLAR ENERGY↗
The North American Renewable Integration Study (NARIS): A Canadian Perspective
The North American electric power system is undergoing significant change, with renewable resources now contributing more generation than ever before. This transformation is poised to continue given decreasing technology costs and ambitious decarbonization goals at the federal, state, local, corporate, and consumer levels. The North American Renewable Integration Study (NARIS) aims to inform grid planners, utilities, industry, policymakers, and other stakeholders about challenges and opportunities for continental system integration of large amounts of wind, solar, and hydropower to support a low-carbon future grid. The National Renewable Energy Laboratory (NREL) conducted a detailed, continent-wide analysis with planning scenarios of transmission, generation, and demand to reach 80%–92% carbon reductions (from 2005) for the Canadian electric power system, and up to 80% reductions continent-wide. We used a suite of models to study future scenarios and gain insights, including potential impacts on costs, emissions, resource adequacy, and the specific technologies that help enable the transition to a low-carbon grid. Our analysis had a particular focus on the potential role of cooperation among the three North American countries and between regions within each country, and how transmission can support sharing of supply and demand diversity. The NARIS project began in 2016. This report describes a Canadian perspective in coordination with the Natural Resources Canada, and a companion report describes a U.S. perspective in coordination with the U.S. Department of Energy. NARIS was an extension of a previous body of work, including the Western Wind and Solar Integration Study, the Eastern Renewable Generation Integration Study, Interconnections Seam Study, and the Pan Canadian Wind Integration Study. NARIS analyzed the entire continent in detail while studying higher renewable generation than previous studies. The scenarios in NARIS were informed by the goals in the Mid-Century Strategies for the Paris Agreement in each country.
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Variability in Wind Energy Generation across the Contiguous United States
ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Additionally, gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.
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MindSynchro
This report presents the developments and results of MindSynchro project as part of DOE OE FOA 1861. DOE and Pacific Northwest National Laboratory (PNNL) have made available to FOA awardees datasets containing years of real historical data recorded from various phasor measurement units (PMUs) which are installed in three large US interconnections: Texas (IC A), Western (IC B), and Eastern (IC C). The main goal of the project, which was successfully achieved, was to develop methods for detection and identification of events which are relevant for power grid operation. Tasks performed for achieving the project goals included data exploration and pre-processing, the development and application of physics-based features, data analysis and labeling based on unsupervised learning approaches, training and testing of DSSL models for classification of events which are relevant for power grid operation, and deployment of solutions to cloud environments. The methods developed in the project can potentially provide relevant benefits to power grid asset owners/operators in general in terms of situational awareness. Two main types of outcomes can be provided by these tools: Identification of specific relevant power grid event types: Semi-supervised ML methods developed in the project can adequately employ not only the relatively scarce labeled data but also the large amount of available unlabeled data to train models for detection of specific event types. Such methods enable the application of trained models for the detection of events in a population of PMUs much larger than that associated to the labeled events. Support in data labeling / label validation: Labels are critical for training of models for identification of specific types of events. However, labeling large amounts of data is a manual and tedious process. This means that such process is error prone and is not scalable. Methods developed in the project, based on ensembles of clustering models, have been successfully employed for turning manual labeling into a scalable process. Accurate identification of specific relevant events can provide the operators with immediate situational awareness that could otherwise require hours or days of analysis from domain experts. We envision that such methods could be initially employed in support of post-mortem analysis of events and, as confidence is gained, they could be employed for online/real-time support, providing, among other benefits, insights for avoiding major events which could happen due to a combination of smaller ones. On the longer term, related methods could potentially be employed to improve protection and control.
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WMO Air Quality and Climate Bulletin - No. 2
Ongoing climate change, caused by the accumulation of greenhouse gases in the atmosphere, is happening on a timescale of decades to centuries and is driving environmental changes worldwide. In contrast, the air pollution that occurs near the Earth’s surface happens on a timescale of days to weeks, and across spatial scales that range from local (for example, urban centres) to regional (such as the eastern United States of America, northern India or the Amazon). Despite these wide‑ranging differences, air quality and climate change are strongly interconnected. The WMO Air Quality and Climate Bulletin reports annually on the state of air quality and its connections to climate change, reflecting on the geographical distribution of and changes in the levels of traditional pollutants.
Air quality↗
The 30/20 GHz flight experiment system, phase 2. Volume 1: Executive summary
Summary information on the final communication system design, communication payload, space vehicle, and development plan for the 30/20 GHz flight experiment will be installed on the LEASAT spacecraft which will be placed into orbit from the space shuttle cargo bay. The communication concept has two parts: a truck service and a customer premise service (CPS). The trucking system serves four spot beams which are interconnected in a satellite switched time division multiple access mode by an IF switch matrix. The CPS covers two large areas of the eastern United States with a pair of scanning beams.
Bronstein, L.↗
Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) Field Campaign Report
With their extensive coverage, low clouds greatly impact global climate. Presently, low clouds are poorly represented in global climate models (GCMs), and the response of low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The poor representations of low clouds in GCMs are in part due to inadequate observations of their microphysical and macrophysical structures, radiative effects, and the associated aerosol distribution and budget in regions where the aerosol impact is the greatest. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer (MBL) clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. Boundary-layer aerosol in the ENA region is influenced by a variety of sources, leading to strong variations in cloud condensation nuclei (CCN) concentration and aerosol optical properties. A permanent ENA site was established by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility on Graciosa Island in the Azores, providing invaluable information on MBL aerosol and low clouds. At the same time, the vertical structures and horizontal variabilities of aerosol, trace gases, cloud, drizzle, and atmospheric thermodynamics are critically needed for understanding and quantifying the budget of MBL aerosol, the radiative properties, precipitation efficiency, and lifecycle of MBL clouds, and the cloud response to aerosol perturbations. Much of this data can be obtained only through aircraft-based measurements. In addition, the interconnected aerosol and cloud processes are best investigated by a study involving simultaneous in situ aerosol, cloud, and thermodynamics measurements. Furthermore, in situ measurements are also necessary for validating and improving ground-based retrieval algorithms at the ENA site. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) project was motivated by the need for comprehensive in situ characterizations of boundary-layer structure, and associated vertical distributions and horizontal variabilities of low clouds and aerosol over the Azores. The ARM Aerial Facility (AAF) Gulfstream-1 (G-1) aircraft was deployed during two intensive measurement periods (IOPs). The first deployment took place from June 21 to July 20, 2017, and the second one took place from January 15 to February 18, 2018. Flights were carried out in the Azores, near the ARM ENA site on Graciosa Island. Deployments during both seasons allow for examination of key aerosol and cloud processes under a variety of representative meteorological and cloud conditions. The science themes for the deployments include: (1) Budget of MBL CCN and its seasonal variation; (2) Effects of aerosol on cloud and precipitation; (3) Cloud microphysical and macrophysical structures, and entrainment mixing; (4) Advancing retrievals of turbulence, cloud, and drizzle; and (5) Model evaluation and processes studies.
Eastern North Atlantic, Graciosa Island, aerosols,↗
Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors
Abstract Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.
Geology↗
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
The profile of the liquid water content (LWC) in clouds provides fundamental information for understanding the internal structure of clouds, their radiative effects, propensity to precipitate, and degree of entrainment and mixing with the surrounding environment. In principle, differential absorption techniques based on coincident dual-frequency radar reflectivity observations have the potential to provide the LWC profile. Previous differential frequency radar reflectivity (DFR) efforts were challenged by the fact that the measurable differential attenuation for small quantities of LWC is usually comparable to the system measurement error. This typically renders the retrieval impractical, as the uncertainty can become many times greater than the retrieved value itself. Theoretically, this drawback can be mitigated following two interconnected approaches: (1) increasing the frequency separation between the dual-frequency radar system to measure greater differential attenuation and (2) increasing the radar operating frequency to reduce the instrument measurement random error. Our recently developed 239 GHz radar was deployed during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) along with a variety of collocated remote sensing and in situ instruments. We have combined Ka-band (35 GHz) and G-band (239 GHz) observations to retrieve the LWC from more than 100 vertical profiles of shallow clouds with typical amounts of LWC smaller than 1 g m -3 . We theoretically and experimentally demonstrate that the Ka-band and G-band pair of frequencies offers at least a 65 % relative improvement in the LWC retrieval sensitivity compared to previous works reported in the literature using lower-frequency radars. This new technique provides a missing capability to determine the LWC in the challenging low liquid water path (LWP) range (< 200 g m -2 ) and suggests a way forward to characterize microphysical and dynamical processes more precisely in shallow clouds.
54 ENVIRONMENTAL SCIENCES↗
Novel Geothermal Development of Deep Sedimentary Systems in the United States
Economic and reservoir engineering models show that stratigraphic reservoirs have the potential to contribute significant geothermal power in the U.S. If the reservoir temperature exceeds about 150 – 200 °C at 2 – 4 km depth, respectively, and there is good permeability, then these resources can generate power with a levelized cost of electricity (LCOE) of close to 10 ¢/kWh (without subsidies) on a 100 MW power plant scale. There is considerable evidence from both groundwater geology and petroleum reservoir geology that relatively clean carbonates and sandstones have, and can sustain, the required high permeability to depths of at least 5 km. This paper identifies four attractive stratigraphic reservoir prospects which are all located in the eastern Great Basin, and have temperatures of 160 – 230 °C at 3 – 3.5 km depth. They are the Elko basins (Nevada), North Steptoe Valley (Nevada), Pavant Butte (Utah), and the Idaho Thrust Belt. The reservoir lithologies are Paleozoic carbonates in the first three, and Jurassic sandstone and carbonate in the Idaho Thrust Belt. All reservoir lithologies are known to have high permeability characteristics. At North Steptoe Valley and Pavant Butte, nearby transmission line options allow interconnection to the California power market. Modern techniques for drilling and developing tight oil and gas reservoirs are expected to have application to geothermal development of these reservoirs.
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Impacts of Demand-Side Resources on Electric Transmission Planning
Will demand resources such as energy efficiency (EE), demand response (DR), and distributed generation (DG) have an impact on electricity transmission requirements? Five drivers for transmission expansion are discussed: interconnection, reliability, economics, replacement, and policy. With that background, we review the results of a set of transmission studies that were conducted between 2010 and 2013 by electricity regulators, industry representatives, and other stakeholders in the three physical interconnections within the United States. These broad-based studies were funded by the US Department of Energy and included scenarios of reduced load growth due to EE, DR, and DG. While the studies were independent and used different modeling tools and interconnect-specific assumptions, all provided valuable results and insights. However, some caveats exist. Demand resources were evaluated in conjunction with other factors, and limitations on transmission additions between scenarios made understanding the role of demand resources difficult. One study, the western study, included analyses over both 10- and 20-year planning horizons; the 10-year analysis did not show near-term reductions in transmission, but the 20-year indicated fewer transmission additions, yielding a 36percent capital cost reduction. In the eastern study the reductions in demand largely led to reductions in local generation capacity and an increased opportunity for low-cost and renewable generation to export to other regions. The Texas study evaluated generation changes due to demand, and is in the process of examining demand resource impacts on transmission.
99 GENERAL AND MISCELLANEOUS↗