INCREASING GRID RESILIENCE THROUGH DATA-DRIVEN MODELING FOR STORM - PowerPoint PPT Presentation

INCREASING GRID RESILIENCE THROUGH DATA-DRIVEN MODELING FOR STORM OUTAGE PREDICTION AND LONG-TERM PLANNING Steven Quiring, Texas A&M University Seth Guikema, Johns Hopkins University U.S. DOE State Energy Risk Assessment Workshop

Our Overarching Goals ¨ Provide useful information on weather-related impacts to aid ¨ (1) pre-storm preparation and ¨ (2) long-term hardening and mitigation by: ¤ Electric power utilities ¤ Other utilities and organizations dependent on electric power ¤ Local, state, and federal agencies

Power Outage Prediction ¨ Past work on hurricane power outage forecasting ¨ Ongoing work for DOE’s Energy Infrastructure Modeling and Analysis Division ¨ Demonstration of power outage prediction tool ¨ Gaps and future research

A “standard” utility response cycle Strongest winds, heaviest rain, highest surge Hurricane Off-coast, Rain begins Progress outer bands Wind, rain, surge for large storms, begin contact gradually diminish wind increases Time T~-5 days T~-2-3 days T=0 T = 0-1 day T > 1 day Monitor track, Finalize crew Begin repairs Conduct repairs begin estimating requests, position when safe according to Utility crew needs, materials and prioritization plan Activities & coordination call, crews Decisions begin crew requests

Timescales of Focus Long-term asset management Storm response planning Seasonal resource planning • How many hurricanes? • How will climate • How intense? change influence • How big? hurricane hazards? • Which basins? • Will risk of power outages change? • Are different asset management strategies needed? ctpost.com Ameren America

Power Outage Prediction: Past Work Primary Research team: Roshi Nateghi, Seth Guikema, Allison Reilly, Andrea Staid, Michael Gao (JHU), Steven Quiring (TAMU)

Past Work: Goals & Data ¨ Goal: Accurately estimate power outages 4-6 days before landfall and update every 6 hours ¨ Unit of Analysis: ¤ Utility-specific model: 12,000 ft. by 8,000 ft. grid cells ¤ Spatially general model: census tracts ¨ Data: ¤ Hurricane wind speeds & duration (wind field model) ¤ Geographic data: LU/LC, soil type, topography, DEM, etc. ¤ Climatological: soil moisture, drought indices, long-term precipitation levels ¤ Utility-specific: system inventory, tree-trimming

Selected Prior Work ¨ Outages ¤ Liu et al. (2005): a first model – GLM ¤ Han et al. (2009a, 2009b): improved accuracy, usability ¤ Guikema & Quiring (2012): further improved accuracy with a new statistical method ¤ Nateghi et al. (2013): substantial increase in accuracy with a Random Forest ¤ Guikema et al. (2014): model useable anywhere along the U.S. coastline with only publicly-available data needed as input ¨ Outage Duration ¤ Liu et al. (2007): a first model ¤ Nateghi et al. (2011): improved predictive accuracy ¤ Nateghi et al. (2014): simplified model w/o losing predictive accuracy ¨ Damage ¤ Guikema et al. (2010): Comparison of statistical models ¤ Han et al. (2014): Bayesian updating of FORM-based fragility curves

Model Development Process 10 hurricanes, 4-state area Have run model for 12(+) real storms In-house Willougby-based model Use scenario storms to (a) answer policy-related queries and (b) gain further confidence in the model

Prediction Process Iterative updating at Statistical Outage Hurricane track & every 6 hour hurricane Forecasting Model intensity forecast forecast update (ensemble or single) Hurricane Wind Field Model

Example Predictions: Early models, Katrina GLM (2009a) and GAM (2009b) with approximately 100 covariates. Actual Number GAM Predictions GLM Predictions of Outages (Han et al. 2009b) (Han et al. 2009a)

A “simpler”, accurate model Random forest model, reduced to 6 covariates (Nateghi et al., Risk Analysis 2013)

A Key Challenge: All of the above models were specific to a utility service area and required privately held data.

Spatial Generalization Can a model be developed that can be used for entire coast using only publicly available data while still maintaining accuracy? Approach Validate models across Train & validate hurricanes Predict for models in an approaching service area using hurricane Validate only public data models across states Learn from storms and refine models

Experience with Hurricane Sandy

First Model Run: Oct 26, 5pm Our Forecast: 10 million without power First Press Release by JHU

Progression of Hurricane Sandy Runs During the Event

So How Did We Do? ¨ Important distinction: We predict cumulative outages, utilities generally report peak outages ¨ Data Gap: Outage data at the scale at which we are making predictions ¨ Results: ¤ DOE estimated 8.5 million customers were out at peak ¤ Our final estimate as the storm transited the mid-Atlantic was 8-10 million out ¤ We were within 8% of DOE’s estimates for NY, PA, MA, RI, VA ¤ We overestimated outages for MD and DE ¤ We underestimated outages for CT

“What If” Scenario Analysis: Historic Storms Results Ran two sets of scenarios Predicted&Population& assuming current Without&Power&Relative& Storm& to&Hurricane&Irene& population distribution: Isabel' 0.33' Rita' 0.58' ¤ Historic storms (Isabel, Ivan' 0.64' Camille' 0.68' Rita, Ivan, Camille, Ike, Ike' 0.77' Katrina, Irene, Andrew, Katrina' 1.00' Irene' 1.00' Galveston, Sandy) Andrew' 1.18' Galveston' 1.26' ¤ Haiyan on tracks of HaiyanCAndrew'track' 1.61' Andrew, Katrina, Ike, and HaiyanCKatrina'track' 1.86' HaiyanCIke'track' 2.03' Irene Sandy' 2.90' HaiyanCIrene'track' 6.92' '

What If Analysis Results: Historic Storms

What If Runs: Haiyan

Long-Term Risk to Power Systems in a Changing Climate

Research Questions ¨ How would potential changes in hurricane hazards – intensity, frequency, location – influence power system risk? ¨ Which areas of U.S. coastline are most sensitive to changes in hurricane hazards? ¨ Can the possible changes be simulated in a way that will help support long-term utility hardening decision-making?

Existing Hurricane Climatology

Influence of Changes in Frequency

Metropolitan Area Impacts: New York City vs. Washington, DC

Ongoing Work for DOE

Year 1 Research Tasks ¨ Power outage forecasting for weather events ¤ Uncertainty quantification ¤ Assessing the importance of soil moisture and tree cover data for improving predictive accuracy ¤ Developing a secure web portal ¨ Scalable risk and uncertainty modeling to support long- term planning ¤ Incorporate storm forecast uncertainty into the multi-scale model ¤ Make the model applicable for multi-utility and ISO coordinating areas ¨ Engaging and educating stakeholders

Uncertainty Quantification Model forecast tracks for Hurricane Gustav on Aug 25 th (left) and Aug 29 th (right), 2008. Plot provided courtesy of Jonathan Vigh, Colorado State University. For more information about this graphic see http://euler.atmos.colostate.edu/~vigh/guidance/ GPCE = is the average distance between the model-predicted tropical cyclone tracks from the Geophysical Fluid Dynamics Laboratory (GFDI) Hurricane Prediction System, Global Forecast System (AVNI), Navy Operational Global Atmospheric Prediction System (NGPI), Met Office Global Model (UKMI) and the GFDL model (GFNI) run at the Fleet Numerical Meteorology and Oceanography Center

Monte Carlo Wind Speed Probability (MCWSP) Model MCWSP generates 1,000 forecast realizations by sampling from track and intensity forecast errors from the last 5 years and determines the wind radii of each realization using a simple climatology and persistence scheme Left - the first 10 of 1000 track realizations for a Hurricane Ike forecast starting at 1200 UTC 7 Sep 2008. The white line is the National Hurricane Center official track forecast. Right – Hurricane force wind speed probabilities from the National Hurricane Center operational product for the same time.

Uncertainty quantification: Hurricane Katrina Five best-case scenarios (fewest predicted outages) and five worst-case scenarios (most predicted outages) for Hurricane Katrina on 28 August 2005 at 7 a.m. CDT, which is approximately 24 hours prior to landfall. TD TS Cat1 Cat 2 Cat 3 Cat 4 Cat 5 Best Case Scenarios Worst Case Scenarios

Hurricane Katrina Quiring, S. M., Schumacher, A. B. and S. D. Guikema (2014) Incorporating hurricane forecast uncertainty information into decision support applications. Bulletin of the American Meteorological Society , 95: 47-58

Demonstration of web portal Web ¡portal ¡func/onality: ¡ 1. Takes ¡as ¡input ¡a ¡track ¡and ¡intensity ¡from ¡manual ¡ upload, ¡text ¡entry, ¡or ¡download ¡directly ¡from ¡a-­‑ deck ¡file ¡at ¡UCAR ¡ 2. Runs ¡the ¡wind ¡model ¡ 3. Runs ¡the ¡power ¡outage ¡forecast ¡model ¡ 4. Displays ¡the ¡map ¡of ¡forecast ¡outage ¡together ¡ with ¡outage ¡total ¡ ¡

Front Page

Track & Intensity Entry (Manual)

Displaying the Results – Ike

Displaying the Results – strong storm