Storm Damage Modeling at Pacific Gas & Electric

Transcription

1 1 Storm Damage Modeling at Pacific Gas & Electric Mike Voss PG&E Applied Technology Insert damage photos March 2015

2 Gas System Operations Electric Operations Power-Gen/ Hydro 2 Short Term Energy Supply PG&E - ATS Meteorology Cust. Energy Solutions DCPP Nuclear

3 Questions: What is the primary modulator of short term performance in Electric Operations at PG&E? 3 (SAIFI, SAIDI, CAIDI, 911 Response, and Wires Down) Weather What can we do about this? Predictive Modeling In the short term, accurate predictive modeling improves SAIDI, CAIDI, and 911 response times by serving as the tool to drive preparedness and response In the longer term, predictive modeling does not help SAIFI and Wires Down for individual storm events (outages will happen, wires will come down), but the understanding of weather impacts derived via the modeling effort helps drive improvements in reliability

4 Weather Signal (That portion of outage volume that is caused by adverse weather) 2013 Daily Outage Volume (transformer and above) 4 Unplanned Outage Volume total outage volume baseline outage volume 0

5 Weather Signal and SAIDI Daily Outage Volume and SAIDI Total Outage Volume Unplanned Outage Volume Morro Bay Baseline Outage Volume SAIDI

6 Weather Signal and Wires Down Daily Outage Volume and Wires Down Total Outage Volume 100 Unplanned Outage Volume Baseline Outage Volume Wires Down

7 Genesis of PG&E Storm Damage Modeling January 4, 2008 Storm 7

8 DSO SOPP Model Distribution System Operations Storm Outage Prediction Project 8 Adverse weather categories 1 to 5 are used to indicate the Sustained Outage (SO) severity level Categories are defined in terms of outage activity compared to normal background levels on non-weather days. Level Abbrev. Outage Count Outage Volume Qualitative Weather Frequency Category 1 CAT 1 < 130 1X Little or no adverse weather expected Daily Category 2 CAT X Some adverse weather likely 2 to 5 days per month on average Category 3 CAT X Significant Adverse weather likely 4 to 10 days per year on average Category 4 CAT X - 32X Extreme weather likely 0 to 3 days per year on average Category 5 CAT 5 > 2000 > 32X Extreme to catastrophic weather likely 1 storm in 10 to 20 years* * Storm of record was January 4, 2008 with ~2700 sustained outages (SO)

9 Bigger Picture of Daily Outages 9 Outage Volume History through ,500 Cat 5 total outage volume 2,000 1,500 Cat 4 1, Cat 3 Cat /1/2007 1/1/2008 1/1/2009 1/1/2010 1/1/2011 1/1/2012 1/1/2013 1/1/2014

10 Again 2013 Outage Volume (Transformer level and above) 10 Unplanned Outage Volume Cat 3 Cat 2 E-Cat 1 total outage volume baseline outage volume (E-Cat 1 means Elevated Cat 1 or Weather Day )

11 SOPP Model 11 The Storm Outage Prediction Project Model (aka SOPP Model) was developed by ATS Meteorology to predict weather related outage activity and has been in use since 2009 SOPP was originally intended to help PG&E predict and prepare for major storms; the genesis was the January 4, 2008 mega storm However, PG&E experiences many weather events of smaller magnitude, which increase the level of outage activity above normal levels As a result, SOPP has evolved into a daily operations tool

12 Minor Weather Event Frequency 12 Days per Year Reaching Cat Level Days per Year Reaching Category Level Year History of PG&E Minor Storm Days Per Year Weather Day Cat 2 0

13 Major Weather Event Frequency Year History of PG&E Major Storm Days Per Year Days per Year Reaching Cat level Days per Year Reaching Category Level Cat 3 Cat 4 Cat 5 2 0

14 Weather Signal Climatology Outage Climatology - Average Daily Historical Volume of Sustained Outages (SO) Total Total Weather-Related Background Background Daily Average Historical Outage Volume /1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 (Transformer level and above outages 1995 through 2012)

15 Weather Signal Decomposed 15 Average Historical Outage Volume Due to Weather Outage Weather Climatology - Average Historical Volume of Sustained Outages (SO) Due to Weather Total Total Weather Outages South North East East North West West heat heat Low Low Snow Lightning heavy Rain Rain Rain Rain Flash Flash 0 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 (Transformer level and above outages 1995 through 2012)

16 Weather Risk Calendar 16 Weather Risk Matrix for the PG&E Distribution System PG&E System* *Risk Scoring High Risk Medim Risk Low Risk Little or No Risk Color Weather Element Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Total **South Wind Events West/Northwest Wind Events North/Northeast Winds Low Snow Lightning Heat Heavy Rain First Rain (flash) Coastal Drizzle/Fog (flash) Total

17 PG&E 70,000 Square Miles Diverse topography and microclimates Distribution system is sensitive to weather On average 30,000 unplanned sustained transformer level and above outages per year, roughly 10,000 are directly weather related each of the 19 Divisions has unique weather vulnerabilities and outage climatology 17

18 Weather Risk by Division 18 Weather Element HB SN NV SA SI NB SF EB DI PE MI DA SJ CC LP ST YO FR KE **South Wind Events West/Northwest Wind Events North/Northeast Winds Low Snow Lightning Heat Heavy Rain First Rain (flash) Coastal Drizzle/Fog (flash) Mutli-model approach: one model for each division and each weather type impacting that division ~160 models Main technique: Dynamic filtering based on analog storm pattern matching tools Depending on size and type: traditional techniques such as regression modeling are also used (wind speed or pressure gradients vs. outage activity)

19 19 DSO SOPP Model (oversimplified) 20 years of Historical Weather and Outage Data ~160 Models Weather forecast model data from the ECM, GFS, NAM, POMMS Real-time Observations Radar, satellite, surface, etc. Meteorologist - Supplemental Tools and Experience DSO SOPP Model (man-machine mix) DSO SOPP Model Forecast Cat Cat 1 Cat 2 Staffing Normal Normal, but have a plan Issued: Thursday, June 26, :47 Cat 3 Cat 4 Staffing & Timing as Directed Staff to Model, Timing as Directed Transformer Level Outages and Above Cat 5 Staff to Model, Timing as Directed Thursday 6/26/2014 Friday 6/27/2014 Saturday 6/28/2014 Qualitative Weather Adverse weather unlikely Adverse weather possible Adverse weather likely Extreme weather possible Extreme weather likely Sunday 6/29/2014 Outages by Division SO CESO TM CR SO CESO TM CR SO CESO TM CR SO CESO TM CR Northern (NR) Humboldt Sonoma North Valley Sacramento Sierra Bay Area (BA) North Bay San Francisco East Bay Diablo Central Coast (CC) Peninsula Mission De Anza San Jose Central Coast Los Padres Central Valley (CV) Stockton Yosemite Fresno Kern PG&E SYSTEM PG&E Internal Use Only ATS - Meteorology Note s: SO = Sustained Outages, CESO = Customers Experiencing Sustained Outages, TM = Troublemen, CR = Crews

20 Present SOPP Model 20 SOPP predicts the daily outage volume and resources needed to respond Forecast is created every day, multiple times daily during weather events Goes out via to over 4000 people Aides in advance planning and restoration efforts

21 Questions: 21 Is the model right? No, models are seldom right Is the model useful? Yes, both the direct benefits and the intangibles Methodology, databases, forecast tools, and expertise has enabled: Better situational awareness during weather events meteorologist takes on valuable role in Planning and Intelligence section of ICS Improved overall understanding of weather risks to system reliability, and the impact to Electric Operations performance

22 22 Questions: Could the model be better? Absolutely, continuous improvement is the modus operandi

23 SOPP Model Improvements POMMS PG&E Operational Mesoscale Modeling System 23 High-resolution weather model has recently come online PG&E Operational Mesoscale Modeling System (POMMS) Model and data output are customized for PG&E Improved weather modeling = improved storm damage modeling Caution: because models are wrong, one can be misguided in even greater detail

24 24 Thank You Mike Voss