Data Analytics at American Electric Power

Presentation to:SWEDE

May 8, 2014Tom Weaver, PE

Business Analytics is the convergence of three key areas

Business Opportunities

Working with OpCos, define business opportunities or problems we are trying to solve in 3 areas

‐ Distribution‐ Meter‐ Consumer

Technical Solutions

Define the technical solutions that meet business needs for

‐ Data capture‐ Data storage‐ Complex processing‐ Visualization

CommercialSolutions

Define the commercial relationships that are required to make this journey successful

‐ Build vs. Buy‐ Collaboration with others

AEP BusinessSolution

Collaboration is vital as considerations are

inter-connected

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Analytic Capabilities

Analytic Capability Answers the Questions

Standard Reports What happened? When did it happen?

Ad Hoc Reports How many? How often? Where?

Query DrilldownOr OLAP

Where exactly is the problem? How to I find the answers?

Alerts/Monitoring When should I react? What actions are needed now?

Statistical Analysis Why is this happening? What opportunities am I missing?

Forecasting What if the trends continue? How much is needed? When will it be needed?

Predictive Modeling What will happen next? How will it affect my business?

Optimization How do we do things better? What is the best decision for a complex problem?

What does Business Analytics Mean?

SO

UR

CE

DA

TA

– Conceptual View

MACSS(MCS & OPS)

AMI (UIQ & LGCC)

MDM

OperationalData Store

TERS

DA System (PI)

OPERATIONAL

PowerOn

CES Data

PEV Data

PeopleSoft

GIS

Started simplepending maturity of vendor solutions

Analytics framework today

SWAMI

AMIGO

Metering Analytics Needs Analytic Capability

Availability of Data for Load Research and Development of

Detection Reports (Hot Sockets, Etc)

Standard

Service Order Processing Process/System Monitoring

Standard

GUI for the integration of meter events and orders

Standard

SAS

5© 2013 Electric Power Research Institute, Inc. All rights reserved.

Why is Data Analytics a Strategic Initiative for the Industry?

Sense Communicate Compute Control

Power Plants Transmission Substations Distribution Consumers

Sensor and Communication Technology Leapfrogging Ability to Mine Data for High Value Applications for Electric Utilities

6© 2013 Electric Power Research Institute, Inc. All rights reserved.

Distribution Modernization Demonstration on “Big Data”Data Management & Analytics to Support Operations, Planning and Asset Management

Mission:• Benchmark “State of the Industry” • Demonstrate applications• Collaborate with industry leaders

Vision:• Develop “best practices”• Accelerate understanding• Document cost benefit

Take advantage of new opportunities afforded by a sensor enabled grid

Potential Breakthroughs:– Better visualizations, insights– Emerging analytics

capabilities– Application of data

7© 2013 Electric Power Research Institute, Inc. All rights reserved.

Day (0)Storm Event

Day (+3)Storm Recovery

Weather Forecasts

Historical Damage

Storm Protection Settings

Management Systems

Customers Interfaces

Field Crew Interfaces

Assets and Inventory

AMI, SCADA, GIS

Damage Assessments

N+1 Data Sources

Day –(3)Storm Forecast

1 0 11 0 0 1 1

Predictive Analytics

SituationalAnalytics

Field Crew Support

High Performance Computing

Requirements

Data Sets:

Data Integration and Analytics Applied to a Storm Event and Recovery

• Leverage the New EPRI High Performance Computing System

• Define the right system for the application

• Evaluate fast pattern recognition for storm damage data

AMI Meter Temperature Monitoring • Monitoring 502,310 meters.• 85% accurate, 520 Issues out of 612 Field Orders.• Next Steps for on-going Improvements:

• Automate monitoring.• Change cutoff per season for more accuracy.• Optimize parameters?

Site Genie/Quality of Service Report

•Use SAS to decode then analyze the vectors.

•Broken CT and PT on transformer rated meters, poor connections under billing of commercial customers.

•New customer validation of service, saved Ohio 208 site visits this year.

Issues PopulationOhio 38 5,776 0.66%PSO 19 1,718 1.11%I&M 0 473

Description of Issue NumberCorrected Service Type in Meter 8Bad Cable 2Service Incorrect in MACSS 2Bad PT 2Blown Transformer Fuse 1Theft 1No Issues 1Total Feedback 17

Voltage Magnitude Analysis – Transformer Rated Meters

• Next Step: Create automated programs to analyze.

FUTURE: Energy Diversion Detection – Monitor Load Profile

• Analyze the Voltage and kWh of Load Profile

• Flag premises with high voltage drop but low kWh compared to neighbors.

• Program flagging premises documented on the wrong transformer.

FIRST: Clean Up AEP’s MACSS Data – Correlate Premises to Proper Transformer

2179

South

Texas Voltage Magnitude Monitoring

Hi Volt/Failing Transformers – 111 found Oct. ’13 to Feb. ‘14

2S on 12S Service: 75% registration

Utilities looking for . . .

15

Optimize Utilization

& Costs

Improve grid

efficiency

Speed up Restoration

Limit the Impact

Avoid the Outage

GridResiliency

GridRestoration

GridHardening

GridHealth

Improving grid reliability

GridUtilization

Used with permission from General Electric

Typical grid reliability objectives

Total Grid Risk Management– Proactive service &

maintenance– Reduction of capital

expenses– Lower repair costs – Enhance system reliability,

availability & performance– Support optimized asset

replacement – Optimize workforce

productivity & safety •Used with permission from •General Electric

Focused maintenance

ReducedCapEx, OpEx

Enhanced Performance

Manage asset risks

Efficient & Optimized Operations

Proactive asset risk

management across entire

life cycle

16Used with permission from General Electric

AEP Distribution Analytics

Currently planning• Load analytics• Vegetation management• Convert sensor data to actionable stepsFuture Plans• Automating reliability metrics• Tying asset age and health to outage trends• Storm damage prediction

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Questions?Tom Weaver – tfweaver@aep.com