Remote Sensing and Sensor Networks:

Transcription

1 SDG&E Meteorology Remote Sensing and Sensor Networks: Providing meteorological intelligence to support system operations Mike Espinoza Project Manager Steven Vanderburg Senior Meteorologist Brian D Agostino Senior Meteorologist Slide 1

2 2007 San Diego Firestorm Strong Santa Ana winds fanned the flames of several major fires in San Diego County which burned more than 350,000 acres Slide 2

3 Santa Ana Forecasting 3

4 Fire Preparation and Safety Understanding Santa Ana s Julian Ramona West Witch Santa Ysabel 65 km x 3.4 km 4 mph

5 SDG&E MesoNet / Weather Network We own and operate the nations 3 rd largest,and densest weather network Currently 138 weather station MesoNet Supports operational decisions 8 Portable Weather Stations Reports every 10 minutes Redundant communications All data is made public 6 Back-Country Weather Cameras We collect 130,000 data points daily Supports real-time operations Supports forecasting capability Anemometer measures wind speed/gust Temperature, Relative Humidity Sensor Datalogger, Communications Dead- Fuel Moisture Sensor Supports research Slide 5

6 SDG&E MesoNet/Weather Network All stations are on SCADA Weather station installation near Los Coches Substation, Lakeside

7 SDG&E MesoNet/Weather Network SDG&E Weather Stations and Instruments Anemometer (wind speed & gusts) Slide 7

8 SDG&E MesoNet / Weather Network

9 SDG&E Weather Network Slide 9

10 SDG&E MesoNet / Weather Network Slide 10

11 SDG&E VISA & KPI

12 Weather Cameras 6 Cameras Borrego Springs, Creelman, Loveland, Rincon, Rough-Acres, Warner Springs Monitor the weather Impacts (vegetation, structures) Flying Debris Creelman Slide 12

13 NOAA Port & MetWise Enterprise This system will improve our forecasting capabilities Greater lead time, increased resolution, better accuracy Direct access to NWS data, forecasts, and warnings Slide 13

14 Using Satellites to Determine Surface Greenness Slide 14

15 00/01/06 00/05/11 00/09/07 00/12/28 01/05/24 01/09/06 01/12/20 02/04/18 02/08/01 02/11/14 03/03/11 03/06/24 03/10/07 04/02/10 04/06/08 04/09/21 05/01/18 05/05/10 05/08/30 05/12/13 06/04/25 06/08/08 06/11/21 07/03/19 07/07/09 07/10/22 08/02/25 08/06/09 08/09/22 09/01/05 09/05/04 09/09/07 09/12/21 10/04/12 10/07/26 10/11/15 11/03/14 11/07/04 11/10/24 12/03/05 Using Satellites to Determine Surface Greenness Satellite data can help us determine the current state of the fuels across our service area The information is updated daily Slide 15

16 Using Weather Technology We are using this technology to increase our knowledge base about the weather and how it impacts the electric system. We provide the system operators with the information intelligence to make better and more informed operational decisions. Slide 16

17 Renewable Energy/Solar Forecasting SDG&E MesoNet Currently Operates 43 Pyranometers Approximately 25 additional Locations through Sustainable Communities

18 Solar Forecasting / Marine Layer Research Power Generation Forecasting Coastal Marine Layer Forecasting/Modeling Statistical Approach / Research Numerical Weather Prediction Modeling past 50 years for wind, solar radiation, temperature in conjunction with MeteoGroup Design of System Hardening Projects Support better understanding of Santa Ana Winds and Fire Potential Acquiring Atmospheric Profilers Improve monitoring and short-term forecasting of the marine layer 1..

19 Solar Photovoltaic Power Generation Forecasting Project Objectives: Advance SDG&E s understanding of long-term benefits, determine solar forecasting Integration requirements Green Power Labs Project Deliverables: Provide SDG&E with Day Ahead (DA) and Hour Ahead (HA) Solar Power Forecasting of 12 PV facilities Our Current Operational Solar PV 1 : ~120MW Our Current Approved and Pending Solar 2 : 1GW+ 1. Current operational solar capacity provided by SDG&E operations. 2. Approved and Pending CPUC Interconnection Requests, solar PV and solar thermal. October 2011.

20 Coastal Marine Layer (CML) Forecasting Binary Logistic Regression Model Binary Classification Cloudy or Clear Compartmentalize San Diego County into 1km grid, ~32,000 grid cells in region Before the CML spatial and inland extent can be studied, each grid cell must be quantitatively identified as cloudy or clear for each time step Slide 20

21 Numerical Weather Prediction (NWP) Next Steps Data Assimilation (Surface & Satellite) Increasing Resolution (Horizontal and Vertical) Increase computational capability Start running high-resolution outputs of solar radiation Slide 21

22 Thank You! Questions? Slide 22