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Infrastructure Resilience Conference 2018

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Scenario Generation for Analysis of Power System Resilience to Adverse Weather Events

There is increasing interest in designing more resilient infrastructure systems. The ability to better withstand and recover from adverse events will result in fewer service disruptions and lower costs over the long run. In order to improve system resilience, we must first understand the critical threats and resulting consequences. From there, we can work to mitigate these consequences through better planning and operational decision-making. Here, we focus on the electric power transmission system of one utility company. We use historical outage data to develop realistic scenarios that can be used for planning in a stochastic optimization context to increase resilience for both near and far time horizons. Stochastic optimization seeks to find the best solution to an operational problem given that uncertainty exists about the future. We use real data, and scenarios represent plausible future outcomes based on adverse events experienced by the system. We first identify the probability of failure for each line in the transmission system from the historical data. We can do this either overall across all data, or for specific types of weather events (i.e., for a thunderstorm or an ice storm.) We then randomly sample from these probability distributions to determine which lines fail in a given scenario. These baseline scenarios can also be augmented to represent extreme weather events, which result in much higher numbers of failures. We show that this type of scenario generation is needed to get trusted results from optimization models, as the scenarios are linked to the actual data from the customer. We also highlight the many challenges of data availability and of working with historical power system data. The level of detail at which the data is collected directly determines the rigor of the scenarios and the confidence with which they can be used for decision-making.

Andrea Staid
Sandia National Labs
United States


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