Asset Analytics in a Renewable World - OSIsoft€¦ · Asset Analytics in a Renewable World Corey Plott, Duke Energy Renewables, Sr. Business Analyst Ryan Sullivan, Duke Energy, Sr

#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC


Asset Analytics in a Renewable World

Corey Plott, Duke Energy Renewables, Sr. Business Analyst

Ryan Sullivan, Duke Energy, Sr. IT Applications Analyst



• 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



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


Legacy Process

SCADA/PI

Invoiced Budget

Weekly Curtailment Report

Monthly Curtailment Report

Fleet Curtailment Report

Settlement Report Weekly Reliability

Report

Monthly Reliability Report

Fault Log

Variance Report INDU Report

Wind/Solar Summary

CXL Report Monthly NCF

Monthly OEY Solar Fault Report

Versify Emails

Data Sources Primarily Manual

90-100% Automated

Mixture Manual & Automated

Legend



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



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



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



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



Duke Energy and the PI System



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



OSIsoft’s Footprint within Duke Energy

165 PRODUCTION PI

SERVERS

5,000 USERS

1984 EARLIEST PI ARCHIVES

DATE BACK TO THIS YEAR

5M DATA POINTS

900 INTERFACES

70TB OF ARCHIVED DATA



PI Asset Framework & Asset Analytics



Commercial Renewables – PI AF

1,100 WIND TURBINES

~2,300 MW WIND GENERATION

~60 MW SOLAR GENERATION

1,400 SOLAR INVERTERS

19 WIND SITES

51 SOLAR SITES

~ 40,000 Analyses ~ 100,000 Calculation

evaluations per minute

~ 175,000 Calculation

events processed per

minute

Scope:

Asset Analytics:



Current Architecture

• Site SCADA • OPC & Embedded PI

• Site PI API Node

• Consolidated PI Server

• PI Asset Framework

Site Level SCADA Site Level SCADA

Solar API NodeSite Wind API Nodes

Wind PI Server Solar PI Server Solar PI ACE

Curtailment Calcs

AF # 1 AF # 2 AF # 3 AF # 4

Wind Solar

AF # 1 > 18,000 Analyses

AF # 2 > 16,000 Analyses

AF # 3

> 5,000 Analyses

AF # 4 > 1,500

Analyses



The Architecture Journey



The PI Asset Framework Journey

Q1 2017 - Converted PEs to

Asset Analytics

Q4 2016 - PI Asset Framework

2014 R2 - Deployed Event

Frames


2010

Q4 2017 - Distributed Calculations

-Visualization -Agile Project


2017 - Engaged OSIsoft for

best practices - Detailed performance

monitoring

Q2 2018 - Optimize Calculations

and Architecture - Convert PI ACE to

Asset Analytics



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



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



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



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



Lessons Learned

Partner with the business to validate calculation results

Use test environments to confirm calculation results in a production-like process

Monitor calculation statistics and make modifications to optimize performance

Leverage your OSIsoft Enterprise Agreement for best practice collaboration



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



The Business



Perspective…



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



The Impact: Visualizations


RESULTS CHALLENGE SOLUTION

COMPANY and GOAL

27 #OSIsoftUC #PIWorld ©2018 OSIsoft, LLC

Asset Analytics in a Renewables World

• 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

Duke Energy Renewables operates wind and solar assets to serve our customers and sought to modernize our data collection and reporting process.

Leveraged OSIsoft’s suite for advanced analytic calculations while implementing data warehousing techniques for data visualization

Modernization of data retrieval and reporting

• PI Asset Framework, Asset Analytics & Event Frames

• Automatic and dynamic calculations

• Collaboration between the business and IT groups

• Over 200 hours of labor saved per month

• Automation of reports

• Data consistency

• Enabler for future predictive analysis



Speakers

Corey Plott

[email protected]

Sr. Business Analyst

Duke Energy Renewables

Ryan Sullivan

[email protected]

Sr. IT Applications Analyst

Duke Energy



Blue Diamond Award Winner!



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Asset Analytics in a Renewable World - OSIsoft€¦ · Asset Analytics in a Renewable World Corey Plott, Duke Energy Renewables, Sr. Business Analyst Ryan Sullivan, Duke Energy, Sr