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Current efforts to assess risk to the power grid from geomagnetic disturbances (GMDs) that result in geomagnetically induced currents (GICs) seek to identify potential "hotspots," based on statistical models of GMD storm scenarios and power distribution grounding models that assume that the electrical conductivity of the Earth's crust and mantle varies only with depth. The NSF-supported EarthScope Magnetotelluric (MT) Program operated by Oregon State University has mapped 3-D ground electrical conductivity structure across more than half of the continental US. MT data, the naturally occurring time variations in the Earth’s vector electric and magnetic fields at ground level, are used to determine the MT impedance tensor for each site (the ratio of horizontal vector electric and magnetic fields at ground level expressed as a complex-valued frequency domain quantity). The impedance provides information on the 3-D electrical conductivity structure of the Earth’s crust and mantle. We demonstrate that use of 3-D ground conductivity information significantly improves the fidelity of GIC predictions over existing 1-D approaches. We project real-time magnetic field data streams from US Geological Survey magnetic observatories into a set of linear filters that employ the impedance data and that generate estimates of ground level electric fields at the locations of MT stations. The resulting ground electric fields are projected to and integrated along the path of power transmission lines. This serves as inputs to power flow models that represent the power transmission grid, yielding a time-varying set of quasi-real-time estimates of reactive power loss at the power transformers that are critical infrastructure for power distribution. We demonstrate that peak reactive power loss and hence peak risk for transformer damage from GICs does not necessarily occur during peak GMD storm times, but rather depends on the time-evolution of the polarization of the GMD’s inducing fields and the complex ground (3-D) electric field response, and the resulting alignment of the ground electric fields with the power transmission line paths. This is informing our efforts to provide a set of real-time tools for power grid operators to use in mitigating damage from space weather events.
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Integrating space weather and ground-based magnetotelluric data with powerflow solutions for real-time assessment of risk to the power grid
The National Space Weather Action Plan, NERC Reliability Standard TPL-007-1,2 associated FERC Orders 779, 830 and subsequent actions, emerging standard TPL-007-3, as well as Executive Order 13744 have prepared the regulatory framework and roadmap for assessing and mitigating the impact on critical infrastructure from space weather. These actions have resulted in an emerging set of benchmarks against which the statistical probability of damage to critical components such as power transmission system high-voltage transformers can be assessed; for the first time the impacts on the intensity of geomagnetically induced currents (GICs) due to the spatial variability of the geomagnetic field and of the Earth’s electrical conductivity structure can be examined systematically. While at present not a strict requirement of the existing reliability standards, there is growing evidence that the strongly three-dimensional nature of the electrical conductivity structure of the North American crust and mantle (heretofore ‘ground conductivity’) has a first-order impact on GIC intensity, with considerable local and regional variability. The strongly location dependent ground electric field intensification and attenuation due to 3-D ground conductivity variations has an equivalent impact on assessment of risk to critical infrastructure due to HEMP (E3 phase) sources of geomagnetic disturbances (GMDs) as it does for natural GMDs. From 2006-2018, Oregon State University (OSU) under NSF EarthScope Program support, installed and acquired ground electric and magnetic field time series (magnetotelluric, or MT) data on a grid of station locations spaced ~70-km apart, at 1161 long-period MT stations covering nearly ⅔ of CONUS. The US Geological Survey completed 47 additional MT stations using functionally identical instrumentation, and the two data sets were merged and made available in the public domain. OSU and its project collaborators have also collected hundreds of wider frequency bandwidth, more densely-spaced MT station data under other project support, and these have been or will be released for public access in the near future. NSF funding was not available to make possible collection of EarthScope MT data 1/3 of CONUS in the southern tier of states, in a band from central California in the west to Alabama in the east and extending along the Gulf Coast and Deep South. OSU, with NASA support just received, plans to complete MT station installation in the remainder of California this year, and with additional support both anticipated and proposed, we hope to complete the MT array in the remainder of CONUS. For this first time this will provide national-scale 3-D electrical conductivity/MT impedance data throughout the US portion of the contiguous North American power grid. Complementary planning and proposal efforts are underway in Canada, including collaborations between OSU, Athabasca University and other Canadian academic and industry groups. In the present work, we apply algorithms we have developed to make use of real-time streams of US Geological Survey, Natural Resources Canada (and other) magnetic observatory data, and the EarthScope and other MT data sets to provide quasi-real time predictions of the geomagnetically induced voltages at high-voltage transmission system transformers/power buses. This goes beyond the statistical benchmarking process currently encapsulated in NERC reliability standards. We seek initially to provide real-time information to power utility control room operators, in the form of a heat map showing which assets are likely experiencing stress due to induced currents. These assessments will be ground-truthed against transmission system sensor data (PMUs, GIC monitors, voltage waveforms and harmonics where available), and by applying machine learning methods we hope to extend this approach to transmission systems that have sparse or non-existent GIC monitoring sensor infrastructure. Ultimately by incorporating predictive models of the geomagnetic field using satellite data as inputs rather than real-time ground magnetic field measurements, a near-term probabilistic assessment of risk to transformers may be possible, ideally providing at least a 15-minute forecast to utility operators. There has been a concerted effort by NOAA to develop a real-time geomagnetically induced ground electric field data product that makes use of our EarthScope MT data, which includes the strong impacts on GICs due to 3-D ground conductivity structure. Both OSU and the USGS have developed methods to determine the GIC-related voltages at substations by integrating the ground electric fields along power transmission line paths. Under National Science Foundation support, the present team of investigators is taking the next step, of applying the GIC-related voltages as inputs to quasi-real time power flow models of the power transmission grid in order to obtain realistic and verifiable predictions of the intensity of induced GICs, the reactive power loss due to GICs, and of GIC effects on the current and voltage waveforms, such as the harmonic distortion. As we work toward integration of predicted induced substation voltages with power flow models, we’ve modified the RTS-GMLC (Reliability Test System Grid Modernization Lab Consortium) test case (https://github.com/GridMod/RTS-GMLC) by moving the geographic location of the case to central Oregon. With the assistance of LANL we have the complete AC and DC network of the RTS-GMLC case, and we are working to integrate the complete case information into Julia (using the PowerModels and PowerModelsGMD packages of LANL), or into PowerWorld. Along a parallel track, we have performed GIC voltage calculations using our geophysical algorithm for a realistic GMD event (Halloween event) for the test case, resulting in GIC transmission line voltages that can be added into our power system model. We’ll discuss our progress in integrating the geophysical estimates of transformer voltages and our DC model using LANL's Julia and PowerModelsGMD package, for power flow simulations on the test case, and to determine the GIC flows and possible impacts on the power waveforms in the system elements.
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- Award ID(s):
- 1720175
- PAR ID:
- 10513339
- Publisher / Repository:
- https://cpaess.ucar.edu/sites/default/files/documents/2019/2019%20Space%20Weather%20Workshop%20Draft%20Agenda%20with%20Abstracts.pdf
- Date Published:
- Journal Name:
- https://cpaess.ucar.edu/sites/default/files/documents/2019/2019%20Space%20Weather%20Workshop%20Draft%20Agenda%20with%20Abstracts.pdf
- Format(s):
- Medium: X
- Location:
- Boulder, CO
- Sponsoring Org:
- National Science Foundation
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