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Earthquakes cause outages of power transmission system components due to direct physical damage and also through the initiation of cascading processes. This article explores what are the optimal capacity investments to increase the resilience of electric power transmission systems to earthquakes and how those investments change with respect to two issues: (1) the impact of including cascades in the investment optimization model and (2) the impact of focusing more heavily on the early stages of the outages after the earthquake in contrast to more evenly focusing on outages across the entire restoration process. A cascading outage model driven by the statistics of sample utility data is developed and used to locate the cascading lines. We compare the investment plans with and without the modeling of the cascades and with different levels of importance attached to outages that occur during different periods of the restoration process. Using a case study of the Eastern Interconnect transmission grid, where the seismic hazard stems mostly from the New Madrid Seismic Zone, we find that the cascades have little effect on the optimal set of capacity enhancement investments. However, the cascades do have a significant impact on the early stages of the restoration process. Also, the cascading lines can be far away from the initial physically damaged lines. More broadly, the early stages of the earthquake restoration process is affected by the extent of the cascading outages and is critical for search and rescue as well as restoring vital services. Also, we show that an investment plan focusing more heavily on outages in the first 3 days after the earthquake yields fewer outages in the first month, but more outages later in comparison with an investment plan focusing uniformly on outages over an entire 6-month restoration process.
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Can an influence graph driven by outage data determine transmission line upgrades that mitigate cascading blackouts?
We transform historically observed line outages in a power transmission network into an influence graph that statistically describes how cascades propagate in the power grid. The influence graph can predict the critical lines that are historically most involved in cascading propagation. After upgrading these critical lines, simulating the influence graph suggests that these upgrades could mitigate large blackouts by reducing the probability of large cascades.
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- PAR ID:
- 10081642
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PMAPS.2018.8440497
