The Vermont Weather Analytics Center Project: Electricity, Weather and Accelerating the Renewable Grid CIGRE: Grid of the Future Symposium 12 October 2015.

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Presentation transcript:

The Vermont Weather Analytics Center Project: Electricity, Weather and Accelerating the Renewable Grid CIGRE: Grid of the Future Symposium 12 October 2015 Chicago, IL Chris Root, Rob D’Arienzo Vermont Electric Power Company (Rutland, VT, USA)

Outline VELCO Motivation Vermont Weather Analytics Center (VTWAC) Overview Partners Models Renewable Integration IBM Deep Thunder Background Model Specifications Capabilities/Challenges Performance Metrics Renewable Integration Stochastic Engine (RISE) Summary Future Work

VELCO Vermont Electric Power Company (VELCO) was founded in 1956 when local utilities joined together to create the nation’s first “transmission only” electric company VELCO operates an interconnected electric transmission grid consisting of: 738 miles of transmission lines 13,000 acres of rights-of-way 55 substations, switching stations, and terminal facilities 1,300 miles of fiber optic communication network Equipment that enables interconnected operations with Hydro-Québec VELCO: Vermont’s transmission reliability resource

Motivation Global Risk Trends

Motivation Weather & Utilities Climate Central: a nonprofit news organization that analyzes and reports on climate science. ”Outage” = at least 50,000 customers affected Source: “Blackout: Extreme Weather, Climate Change and Power Outages” (Climate Central)

Motivation Weather & Utilities A tenfold increase in major power outages (those affecting more than 50,000 customer homes or businesses), between the mid-1980s and 2012. Non-weather related outages also increased during that time, but weather caused 80 percent of all outages between 2003-2012.   Source: “Blackout: Extreme Weather, Climate Change and Power Outages” (Climate Central)

Vermont Weather Analytics Center Project Overview Two-year, $16 million research undertaking to develop intellectual property using coupled data models and related software 2-year agreement/partnership with IBM 3 main goals: Increase grid reliability Lower weather event-related operational costs Optimize utilization of renewable generation resources Irene –cold river K32, KCW

Vermont Weather Analytics Center Partners VT Distribution Utilities (DU’s): VT College/ University: VLITE? ISO-NE? Other VT Organizations:

Renewable Integrated Stochastic Engine (RISE) Vermont Weather Analytics Center Models Deep Thunder Demand Renewable Renewable Integrated Stochastic Engine (RISE) Model Function Weather (Deep Thunder) To produce accurate weather forecasts up to 48 hours in advance down to 1 sq. km  Lower weather event costs Demand To increase accuracy of state load forecasts  Better plan for future needs Renewable To produce generation forecasts for solar and wind farms  Improve power supply/planning Renewable Integration Stochastic Engine (RISE)* To integrate the models’ results to optimize the value of Vermont's generation, demand response, and transmission assets *VELCO Only

Vermont Weather Analytics Center Models Renewable Sensor Data Renewable Generation Weather Data Wind Weather Model Solar Renewable Integration Power Data Electric Demand Meter Data

Vermont Weather Analytics Center Renewable Integration 3/31/2015 (Tue) Cloud Cover Overcast Sunny High/Low (°F) 41/26 42/24 Max Radiation (w/m^2) 241 965 2 weekdays with similar temperatures but large differences in cloud cover Overcast

IBM Deep Thunder Model Specifications Utilizes WRF-ARW 1071x1071 km, every 9 km 564x564 km, every 3 km 300x297 km, every 1 km Utilizes WRF-ARW (v. 3.5.1 since July 2014) 9/3/1 km horizontal nest (previously: 18/6/2 km) 51 vertical levels to target turbine hub heights Run 2x daily (00/12Z) out to 48 hours in 10 minute intervals RAP used for background fields NAM used for lateral boundary conditions Complex physics configurations for highly rural and urban environments ~4000 stations (gray markers on map): 9km nest (~4000), 3km nest (~1750), 1km nest (~550) – varies for each forecast

IBM Deep Thunder Model Specifications 16 km 1 km Observed radar on 5/27/14 – Isolated supercell over Rutland that produced golf ball-sized hail, high wind gusts, and flash flooding Horizontal Resolution: 16 km 1 km

Forecasted Wind Gusts – 6/28 Wind Event European (ECMWF) [16km] NWS [4km] Deep Thunder [1km] Increased Resolution

Deep Thunder [1km]

IBM Deep Thunder Capabilities Temperature Liquid Precipitation Typical Model Parameters Additional DT Parameters Temperature Liquid Precipitation Pressure Wet Bulb Dew Point Wind Speed Wind Chill Heat Index Wind Gusts  Cloud Water Density Cloud Height Visibility Shortwave Radiation Precipitation Rate Accumulated Precipitation Snowfall Rate Snowfall Depth Snow-to-Liquid Ratios Surface Runoff Maximum Reflectivity Lightning Potential Index Freezing Rain* *Potential future addition

Lightning Forecasting Deep Thunder (Lightning Potential Index) Observed Lightning Strikes Low Medium High

IBM Deep Thunder Challenges Convective events Hail Lightning Tornadoes Microbursts/downbursts Straight line winds Long-range forecasts Real-time observations Bottom line: Deep Thunder will serve as a powerful, complementary weather prediction tool

Performance Metrics Metric Description December 2014 April 2015 June 2015 Goal Actuals Wind speed error Wind direction error 1 – 4 m/sec 4 – 6 degrees 1 – 2 m/sec 1 – 4 degrees 1 m/sec 1 – 2 degrees 1.65 m/sec .09 degrees Wind power forecast precision (farm-level) 75% 1 wind farm 85% 4 wind farms 2 wind farms* 3 wind farms 80% with alarm data 1 wind farm* Solar power forecast precision (farm-level) 86% 1 solar farm >86% 12 solar farms 17 solar farms 12 farms with 86% accuracy Demand forecast accuracy, state level (MAPE) No goals set +/- 4.5% 41 substations +/- 3.0% 60 substations +/-2.9% 83 substations** <2% for state & DU service territory <5% for distribution substations State: 2.4% DUs: 2.7% Substations 3-8% * Additional wind farm data in process ** In some cases, forecasts include total loads aggregated from more than one substation (typically 1-4) for a total of 255 substations

Renewable Integration Stochastic Engine (RISE) RISE dashboard consists of the following four views: Analysis Context Every 12 hours, RISE produces a new set of forecasts using fresh inputs from VELCO (grid state (OSI model), planned outages (TOA) , ISO’s Generation (Unit Commitment) and IBM’s Deep Thunder, Demand and Renewables models Allows users to define new what-if scenarios by changing e.g. grid state, outages etc. and ask RISE to analyze under this scenario Stochastic Contingency Analysis For each analysis context, this view summarizes the analysis results for all the contingency situations User can select any contingency case (line in table) and step into next view Grid State For each contingency case, provides detailed output on each line and bus in the network Users can select up to five lines and buses and plot the predictions of power-flow/voltage over the next 24 hours Mitigation Actions Effect of various optimized mitigation actions RISE dashboard in VTWAC’s platform consists of four views. Two of these have been built, the Analysis view is being worked on now, and the mitigation actions view is to be defined. 20

Summary Development 2/2014 – 5/2015 Prove Out 6/2015 – 1/2016 Operational 2/2016 Today Initial feedback from prove-out efforts indicates that weather, demand, and renewable generation information provides value Weather & Energy Innovation Workshop (8/13) Participation from utilities across the country – ConEd, SDG&E, DTE, and HQ Shared strategies for effectively operationalizing weather prediction as well as lessons learned Collaboration with ISO-NE Wind power forecasting Renewable Energy Vermont (REV) Conference Working with Sandia to secure DOE funding for renewable integration research Submitted several abstracts to American Meteorological Society 2016 Energy Conference

Future Work Finalize hardware/software strategy for ongoing project Deploy first version of RISE model Finalize customizations requests amongst all models Additional training with VT-DU operators Continue model validation (working with ISO-NE) Expand project to serve other societal sectors and applications: Outage/Impact prediction AMI Analytics Agriculture Transportation Recreation/Tourism