Asset Analytics in a Renewable World

Transcription

1 Asset Analytics in a Renewable World Corey Plott, Duke Energy Renewables, Sr. Business Analyst Ryan Sullivan, Duke Energy, Sr. IT Applications Analyst

2 150+ years of service 7.5 million electric customer 1.5 million retail gas customers Duke Energy began investing in commercial renewable assets in 2009 Favorable market environment led to additional investments Today ~ 2,500 MW of wind ~ 600 MW of solar ~ 1,400 MW of operated wind and solar Enough to power ½ million homes $5 billion in assets Presence in AZ, CA, CO, FL, KS, NC, NM, NY, OK, PA, TX, WI, WY

3 The Problem Due to changes in market conditions, the business unit turned from focusing on growth to focusing on operational efficiency Challenge: Leverage IT to create a more efficient solution around renewables reporting process Heavy manual process for reporting and procurement of data Skilled workers spending more time locating the proper data needed Lack of consistency Accessing seven data sources to pull daily data for 1,100+ wind turbines and 1,400+ inverters Manual reporting and calculation process is prone to human performance errors Took 6 people one week to manually process monthly operational data Microsoft Applications being used to the max of their ability

4 Legend % Automated Mixture Manual & Automated Primarily Manual Data Sources Legacy Process Invoiced Budget s Versify SCADA/PI Monthly Reliability Report Wind/Solar Summary Fleet Curtailment Report Monthly Curtailment Report Settlement Report Fault Log Weekly Reliability Report Monthly NCF CXL Report Monthly OEY Solar Fault Report Weekly Curtailment Report INDU Report Variance Report

5 Asset Analytics Calculations Why? The use of analytics is due to our data being unreliable at times from common site communication issues Implemented industry best practices to address these common abnormalities using advanced analytics

6 Solar Asset Analytics Calculations Flat-line indicator: Check for valid daylight, OPC status, and inverter output over the past 15 minutes - Range (change in data is what we expect) - Current Value (valid value within expected limits) Active Power Filtered: Check for good data through flat line analysis - If active power value is good and within inverter limits, use it - If not, substitute with total site power / available inverter count Expected Power -> Irradiance and Back of Module temperature Dynamic calculation via Performance Equations Flat line tag for communication issues and limit thresholds, average of site data, a substitute site will be referenced with a regression calculation when needed All attributes calculated at a 10 minute level to provide to the business

7 Wind Asset Analytics Calculations - Flat-line indicator: Check for valid wind speed data over the past 15 minutes - Range (change in values are what we expect) - Current Value (valid value within expected limits) - Active Power Filtered: Check for good data - If active power value is good, use it - If not, substitute with total site power / turbine count - Wind Speed with Regression - If wind speed is not available, use an available backup

8 Event Frames - Inverter/Turbine Faults, Manufacturer status codes, identify the operating state of an inverter (stop, warning, running) - Capturing irradiance/wind speed, actual generation, expected generation and lost generation during the event *Future: predictive analytics on hardware failure based off previous operating conditions that led to particular faults - Availability of the unit during a single day - Multiple event frame triggers to determine when the day has changed

9 Duke Energy and the PI System

10 OSIsoft s Reach into Duke Energy s Business Regulated business Fossil and hydro operations Nuclear Solar Transmission and distribution Battery storage Natural gas distribution Commercial business Wind Solar Battery storage

11 OSIsoft s Footprint within Duke Energy 165 PRODUCTION PI SERVERS 5M DATA POINTS 900 INTERFACES 70TB OF ARCHIVED DATA 5,000 USERS 1984 EARLIEST PI ARCHIVES DATE BACK TO THIS YEAR

12 PI Asset Framework & Asset Analytics

13 Scope: Commercial Renewables PI AF 1,100 WIND TURBINES 19 WIND SITES ~2,300 MW WIND GENERATION 1,400 SOLAR INVERTERS Asset Analytics: ~ 40,000 Analyses 51 SOLAR SITES ~ 100,000 Calculation evaluations per minute ~60 MW SOLAR GENERATION ~ 175,000 Calculation events processed per minute

14 Current Architecture Site SCADA OPC & Embedded PI Site PI API Node Consolidated PI Server PI Asset Framework AF # 1 > 18,000 Analyses AF # 3 > 5,000 Analyses AF # 2 > 16,000 Analyses AF # 4 > 1,500 Analyses Wind AF # 1 AF # 2 AF # 3 AF # 4 Wind PI Server Solar PI Server Solar PI ACE Curtailment Calcs Site Wind API Nodes Site Level SCADA Solar Solar API Node Site Level SCADA

15 The Architecture Journey

16 The PI Asset Framework Journey Q PI Asset Framework 2010 Q Converted PEs to Asset Analytics Q Distributed Calculations -Visualization -Agile Project Q PI Asset Framework 2014 R2 - Deployed Event Frames Q PI Asset Framework Engaged OSIsoft for best practices - Detailed performance monitoring Q Optimize Calculations and Architecture - Convert PI ACE to Asset Analytics

17 Distributed Calculations Distributed Calculations Across multiple PI AF Servers Maintain duplicate PI AF Templates Only enable analyses on 1 of the 4 Analytics Monitoring Tuned the asset analytics service configuration to increase evaluation and backfill threads Dedicated SQL Environment Implemented a dedicated SQL cluster instance to handle large backfill operations

18 Key Performance Items Scale PI Asset Framework, SQL Server, & PI Data Archive Properly Use a dedicated SQL Server instance Tune compression & exception to minimize excess calculation I/O Monitor Asset Analytics using performance counters

19 Monitoring Performance Counters Real-time monitoring Correlate cause & effect Monitor maximum latency For increased lag Tune calculations Based on results Add more AF Servers For scaling assets

20 Leverage OSIsoft Best Practices OSIsoft Embedded Engineer Optimize Expressions Offload calculations to PI Data Archive PEs --> AA Convert Performance Equations to Analytics Monitor Calculations Asset Analytics statistic performance OSIsoft Enterprise Agreement

21 Lessons Learned Partner with the business to validate calculation results Use test environments to confirm calculation results in a productionlike process Monitor calculation statistics and make modifications to optimize performance Leverage your OSIsoft Enterprise Agreement for best practice collaboration

22 Next Steps Architect a FULL development/test environment Reconfigure architecture to deploy full sites to servers vs. spreading out the calculations Explore Microsoft Windows Clustering for high availability Asset Analytics Engine Detect and Correct Periodically detect when a communication issue occurred Correct by automatically recalculating that timeframe for affected sites/calculations

23 The Business

24 Perspective

25 The Impact: Business Improvement A single, secure data repository for the entire business unit to use Reduction in resource requirements on a monthly basis Automation of the reporting process

26 The Impact: Visualizations

27 Asset Analytics in a Renewables World Duke Energy Renewables operates wind and solar assets to serve our customers and sought to modernize our data collection and reporting process. COMPANY and GOAL CHALLENGE Labor intensive manual reporting process Manual reporting prone to human performance errors Lack of consistency Accessing seven data sources to pull daily data wind turbines and solar inverters SOLUTION Leveraged OSIsoft s suite for advanced analytic calculations while implementing data warehousing techniques for data visualization PI Asset Framework, Asset Analytics & Event Frames Automatic and dynamic calculations Collaboration between the business and IT groups RESULTS Modernization of data retrieval and reporting Over 200 hours of labor saved per month Automation of reports Data consistency Enabler for future predictive analysis 27

28 Speakers Corey Plott Sr. Business Analyst Duke Energy Renewables Ryan Sullivan Sr. IT Applications Analyst Duke Energy

29 Blue Diamond Award Winner!

30 Questions Please wait for the microphone before asking your questions Please remember to Complete the Online Survey for this session State your name & company

31 Merci Thank You Grazie