Society For Risk Analysis Annual Meeting 2017

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

T4-F
Power Systems Resilience

Room: Salon FG   3:30 pm–5:10 pm

Chair(s): Roshi Nateghi   rnateghi@purdue.edu

Sponsored by Engineering and Infrastructure Specialty Group



T4-F.1  3:30 pm  Forecasting Storm-Induced Power Outages and Restoration Personnel Needs. Guikema SD*, Quiring S, Buckstaff K, Beck M, Nateghi R, McRoberts B, Logan T; University of Michigan   sguikema@umich.edu

Abstract: Most past work on predicting power outages due to storms has focused on wind-related weather events such as hurricanes. In addition, past research has primarily focused on predicting power outages and has not dealt with estimating the level of resources needed to restore electric power service. In this talk we will provide an overview of recent work done to develop a power outage risk model for a major west coast utility. This model forecasts power outages at the level of utility service districts for any type of potentially damaging weather event. It also forecasts the total person-hours needed for restoration by different job classes. These forecasts are fully probabilistic, capturing the considerable uncertainty present in any forecast of power outages due to weather events. The model approach used is a hybrid three-stage model. The first stage is a classification model to estimate the probability of having weather-induced damage on any given day. The second stage is uses a quantile random forest to estimate the conditional probability density function of the number of damaged assets in each of the four primary asset classes, poles, transformers, overhead wire spans, and underground cable runs. The third stage then forecasts the conditional number of labor hours by labor class given the damage forecasts. This approach provides substantially more information to support storm-response planning than previous approaches, and it is an explicitly probabilistic approach, better characterizing power outage risk due to storms.

T4-F.2  3:50 pm  Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility. MacKenzie CA*, Zobel CW; Iowa State University   camacken@iastate.edu

Abstract: This talk presents a framework to help a decision maker allocate resources to increase his or her organization’s resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. The optimization model is applied to an example of increasing the resilience of an electric power network following Superstorm Sandy.

T4-F.3  4:10 pm  Quantifying power system resilience to support decisions in the face of adverse weather events. Staid A*, Watson JP, Bynum ML, Arguello B; Sandia National Labs   astaid@sandia.gov

Abstract: 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 electric 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 both now and in the near future. Stochastic optimization seeks to find the best solution to an operational problem given that uncertainty exists about the future. We use real data to assess the range of potential consequences given weather threats, and we generate scenarios to represent plausible future outcomes based on adverse events experienced by the system. We use these outage scenarios to evaluate the optimal decisions and potential actions that can be taken to minimize loss of load in the face of uncertain, future threats. Short-term decisions take the form of re-dispatching generators in advance of a storm, and long-term decisions encompass investments in hardening transmission lines to prevent future damage. Both sets of decisions rely on the identification of critical components and likely failures due to adverse weather. We demonstrate the potential increase in resilience from using stochastic optimization with real system data. We also highlight the many challenges of data availability and of working with historical power system data.

T4-F.4  4:30 pm  Electric Power System Inadequacy Risk in the Residential Sector. Nateghi R*; Purdue University   rnateghi@purdue.edu

Abstract: The U.S. electric power system is increasingly vulnerable to climate variability and change. Supply inadequacies can result from unanticipated climate-induced shifts in electricity demand due to non-stationary climatic conditions. In this talk, we will present an approach to assess the risks associated with shifts in residential demand due to climate variability. A place-based, data-driven approach is leveraged to identify and assess the risk factors which render the residential electricity sector vulnerable in face of future climate variability and change. The proposed quantitative decision-making tool, can be used by the utilities, energy professionals, policy makers, and regulators to design effective strategies to minimize supply inadequacy risks in face of climate variability and change.

T4-F.5  4:50 pm  Assessing the resilience power systems under renewable sources supply risk. Winckler V, Wollega E, Baroud H*; Vanderbilt University   hiba.baroud@vanderbilt.edu

Abstract: Recent research has focused on the opportunities to increase the supply of renewable energy using wind, solar, hydro, biofuel, and other sources in order to gradually replace the consumption of fossil fuels. With the prediction of renewables being highly uncertain and vulnerable to external events, the increase of power supplied by renewable sources can potentially lead to a less resilient power system. For example, a particular solar energy supply on a given day is determined by the thickness of the cloud on that day, the wind energy supply is affected by wind velocity, the volume of rain affects hydroelectric power generation, among other scenarios. The forecast of the level of supply from these renewable sources under future uncertainty is non-trivial. In addition, renewables supply disruptions are not uncommon due to natural hazards such as tornadoes and hurricanes. As such, it is important to make sure that power systems in the future are resilient to disruptive events. The objective of this paper is to establish and measure resilience metrics of power systems in the future as the fossil fuel supply gradually decreases and the renewables supply increases. The power system is modeled as a stochastic network in which supply nodes represent power plants and demand nodes represent cities, counties, or states. Using stochastic network optimization tools, this work seeks to identify the most effective way to distribute power in the United States and provides the combination of renewable and non-renewable sources that minimizes the impact and maximizes recovery in the event of a major disaster.



[back to schedule]