1. DISCUSSION Topic: aMI DATA ANALYTICS
2017 Transmission & Distribution Benchmarking
Community Insights Conference
DISCUSSION Topic: aMI DATA ANALYTICS
August 22-25, 2017
Burlington, VT

2. AMI DATA ANALYTICS Overview -- This year’s Discussions with our Community
Goal and Scope of Our Discussions
The goal of this 2017 discussion topic was to investigate how AMI data is currently being collected, stored, analyzed and leveraged to support business operations at T&D community companies that have established AMI data analytics groups.
The companies that agreed to share information during the webinars were asked to comment on three aspects of their AMI data analytics activities:
Organization and staffing of their AMI data analytics function
Data processes, systems and tools that are being used
Current and planned future applications of AMI data (“use cases”), with particular focus on applications that support T&D

3. Companies that shared information
Company
Spokespersons
First Energy
Robert Greene
Oncor Electric Delivery
Randall Schmidt
PHI
Bobby Besharati/
Belinda Kargbo
Thanks for taking the time to share your information and answer our questions!

4. HOW TO LEVERAGE THE VALUE OF TODAY’S PRESENTATION AND OBTAIN ADDITIONAL INFORMATION
Listen to recordings from the webinar sessions that were held on May 24 and May 30, 2017, and read and share copies of the presentation documents. These records are stored on the First Quartile website.
Below is a link to the website. Please handle this material the same as you would any of our benchmarking data, following our confidentiality agreements.
Today’s presentation is available for you to use as an overview document at any meetings you may have on this topic at your company
Additional information might be obtained from the T&D community companies that shared information during the webinars and/or those that have made presentations on this topic in other forums. Contact the T&D benchmarking coordinators of those companies to arrange follow-up discussions.

5. Today’s Overview
What are we talking about (definition and scope of the topic)?
Why should we be interested in this topic?
How have companies organized and staffed their AMI data analytics functions?
What systems and tools are used to collect, store and analyze AMI data?
How is AMI data being used to improve operations?

6. What are we talking about?
Definition: The term “AMI data analytics” encompasses the people, business processes, hardware, software and communication network services that enable utilities to collect, analyze and apply customer metering data in order to improve operations and better serve customers

7. What are we talking about?
Scope (process steps):
Collect AMI Data
Gather large volumes of smart meter data
Filter out noise
Identify and fix network problems
(Out of Scope)
Produce Information
Produce Customer Bills
Validate and clean up data
Analyze and summarize meter data and correlate it to other available data
Draw Insights
Identify causal relationships
Pinpoint locations of problems
Identify improvement opportunities
Make Decisions
Managerial
Operational
Strategic
Regulatory compliance
Efficiency gains
Reliability improvements
Other benefits
Achieve Outcomes
Source: Adapted from a slide in First Energy’s webinar presentation, May 24, 2017, Robert Greene

8. What are we talking about?
Scope (hardware and software):
Direct Connect Meter
Wide-Area-Network (WAN)
CGR, Range Extender
Head End
(Itron OpenWay, “OWCE”)
Connected Grid Network Management System (Cisco)
Itron Meter Data Management
(Itron Enterprise Edition)
Mesh Network
Standard Smart Meters
Source: First Energy’s webinar presentation, May 24, 2017, Robert Greene
Downstream Systems
(SAP, Teradata, etc)

9. What are we talking about?
Scope (potential operational impact areas):
Our primary focus for T&D

10. WHY should we be interested?
Companies that have installed smart meters throughout their system (47% of this year’s survey respondents) and those that are in the process of doing so (an additional 35%):
To learn about the experiences of other companies that have deployed AMI technology and are in the process of developing their analytics capabilities -- identify what applications (“use cases) have proved to be beneficial -- so that you can refine your company’s developmental plans
Companies that are piloting smart meters or about to start a pilot program (12% of this year’s survey respondents) and companies that have no current plans for smart meters (the remaining 6%)
To identify potential AMI data analytics applications (“use cases”) and associated benefits that might be used to bolster a business case to proceed with widespread AMI deployment at your company
82%
18%
Source: SG pg.19 – SG30, 17 responses

11. HOW HAVE COMPANIES ORGANIZED AND STAFFED THEIR AMI DATA ANALYTICS FUNCTIONS?
Is the staff centralized or decentralized? How is it aligned with other functions?
All three companies that shared information during the webinars have central AMI operations and data analysis groups within the Meter to Bill process organization which are focused on ensuring that the data collection system is working effectively, using data analytics tools to support problem root cause analysis and remediation
All three companies have or expect to have decentralized data analytics groups embedded in multiple areas of the energy delivery business unit, including revenue management/collections, system planning, asset management, outage management, regulatory reporting, etc. – these people develop and maintain the AMI data applications (use cases) that support their operations
What education and skills does the staff possess?
All three companies reported that they staff their data analytics groups with a wide range of skill-sets and work experience, including people with IT backgrounds, people with deep field experience in meter reading, meter operations and/or dispatch, electrical engineering technicians, engineers and statisticians. Their educational degrees range from high school only all the way up to doctorate degrees in various branches of science, technology, engineering and mathematics
What have been the most significant organizational challenges?
Identifying and installing database and data analysis tools that are capable of efficiently handling “big data” volumes and that meet the needs of multiple user groups
Establishing an effective data governance process as applications/use cases are identified and developed outside of the Meter to Bill process

12. What systems and tools are being used?
AMI Meters
Itron (FE); Landis & Gyr (Oncor); GE and Landis & Gyr (PHI)
Head-end Processor and Network Management System
Itron OpenWay and CISCO Connected Grid Network Management System (FE); Landis & Gyr Command Center (Oncor); Silver Spring Networks UIQ (PHI)
Meter Data Management System
Itron Enterprise Edition MDMS (FE); Gridstream MDMS (Oncor); Itron Enterprise Edition MDMS (PHI)
“Big Data” Repository
Teradata (FE); Oracle and Informix (Oncor); SAP, Oracle and Landis & Gyr Advanced Grid Analytics (PHI)
Data Analysis and Presentation Tools
QlikView and ServiceNow (FE); Numerous tools including Toad, Power BI, SQL Explorer/Developer and Cognos (Oncor); SAS, SQL, Toad, Tableau (PHI)
Collect AMI Data
Gather large volumes of smart meter data
Filter out noise
Identify and fix network problems
Produce Information
Validate and clean up data
Analyze and summarize meter data and correlate it to other available data

13. HOW IS AMI DATA BEING USED TO IMPROVE OPERATONS?
FIRST ENERGY
AMI Deployment Status
Deployment is in progress across four Pennsylvania operating companies (Penn Power, Penelec, West Penn and Met-Ed); now about 50% deployed in PA and using the collected data primarily for customer billing
Current Data Analytics Applications
Several current applications that support data collection continuous improvement
Future Data Analytics Applications
Remote Connect/Disconnect (RCD)
Meter Tampering Detection
Outage Management Ping (Restoration Verification)
Anticipated future advanced analytics applications to support various areas of the organization including Asset Management, Customer Service, Field Operations, Resource Management, Supplier and Regulator Settlements, etc.
Draw Insights
Make Decisions
Achieve Outcomes

14. HOW IS AMI DATA BEING USED TO IMPROVE OPERATONS? (CONTINUED)
ONCOR ELECTRIC DELIVERY
AMI Deployment Status
Fully deployed as of late 2012
Current Data Analytics Applications
Remote Connect/Disconnect
Meter Tampering Detection
Energy Theft Detection
Predicting Meter Failures
High/Low Voltage Detection
Power Quality Improvement Re: Momentary Outages/Light Flickers
Customer Outage Data “Scrubbing” For SAIDI Calculations
Predicting Transformer Failures
Predicting Asset Damage After Storms
Outage Restoration Verification After Storms
Customer Proactive Messaging (Outages,ERTs)
Future Data Analytics Applications
Asset Failure Prediction
Cable, Capacitors, Other Line Devices
Transformer Load Management
System Planning Support Applications
Network Connectivity Verification/Corrections
Draw Insights
Make Decisions
Achieve Outcomes

15. HOW IS AMI DATA BEING USED TO IMPROVE OPERATONS? (CONTINUED)
PHI
AMI Deployment Status
Fully deployed at two operating companies (PEPCO and DPL); no AMI currently at third operating company (ACE)
Current Data Analytics Applications
Asset Load Management
Transformers
Feeder Devices
Conservation Voltage Reduction (CVR)
Reliability Analysis
Improve accuracy of KPI calculations
Identify worst performing feeders for each region
Optimize reliability improvement efforts
Outage Restoration Verification After Storms
Planning Support for DER Integration (major initiative at PHI due to high current DER penetration levels and high volume of interconnection applications (1,500 per month))
Future Data Analytics Applications
Network Connectivity Verification/Correction (under development, should be rolled out in near future)
Draw Insights
Make Decisions
Achieve Outcomes

16. conclusions
Companies that have deployed AMI meters on a widespread basis have identified many opportunities to use the data that is collected through those meters to improve their business operations outside of the “meter to bill process”
Additional information on this topic can easily found through internet searches, including some interesting developmental work performed at several T&D community companies that did not participate in our May 24 and May 30 webinars (e.g., PECO, BG&E, APS, Georgia Power)

17. Introduction of today’s speakers
Steve Steffel – PHI Use of AMI Data Analytics to Support DG/DER Integration
Jon Pettit – Oncor Electric Delivery Overall Impact of Smart Meters on Utility Operations

18. Thank you for your Input and Participation!
Gene Dimitrov
Rob Earle
Your Presenters:
Ken Buckstaff
Debi Cook
Dave Carter Dave Weiler
About 1QC
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