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John J. Simmins Senior Project Manager
IntelliGrid Program October 5, 2012
A Monetization of Missing and Inaccurate GIS Data for the Purpose of Justifying Investment in
GIS Data Improvement Initiatives Smart Grid Information Sharing Webcast – GIS Interest Group
2 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Contents
Some context for the GIS research
Cost/benefit analysis model
EPRI survey results
3 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Affordability
Dependability
Efficiency
Vision of the Future
4 © 2012 Electric Power Research Institute, Inc. All rights reserved.
GIS Data Quality Project
5 © 2012 Electric Power Research Institute, Inc. All rights reserved.
GIS Data
“Process automation is limited by our
incomplete and inaccurate
operational data.”
“We have minimal ability to accurately
and quickly measure our
business performance.”
“We react slowly to shifting work
volumes due to manual resource
allocation processes.”
“Process standardization is
limited by vertically integrated systems.”
“We execute simple business tasks with high skill and high
cost resources.”
“We react inconsistently to information
requests.” “We have costly and inconsistent
asset management processes.”
6 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Work Mgt.
BI / Analytics
Mobile GIS
Smart Grid Systems
Mobile Workforce
Work Optimization
Mobile GIS
IVR
DMS / OMS
SCADA
Distribution Automation
AMI
Demand Response
Materials Mgt.
Maint. Mgt.
Engineering Analysis
GIS Mapping
Graphic Design
Asset Management
Ope
ratio
ns
Man
agem
ent
CIS CRM
Customer Management Customer
Empowerment
Executive Information
System Central Databases
SCADA GIS MDMS CMMS
7 © 2012 Electric Power Research Institute, Inc. All rights reserved.
DMS
How Data Enables Workflows
Network Analysis
WMS
Planning & Engineering
Distribution Automation
Schedule and Dispatch
Work Order Drafting & Design
AMI (MDM)
Home Automation and Demand
Response
Service Restoration
OMS
CMMS
Maintenance & Construction
Wireless Mobile
Enablement
AMI MDMS
GIS
8 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Data Quality Survey and Results
9 © 2012 Electric Power Research Institute, Inc. All rights reserved.
EPRI GIS Data Quality Survey – Phase 1
• Thirteen utilities participated in the survey.
• Outage management and engineering analysis are the most common uses of GIS data.
• Integration and dependencies vary widely.
• No correlation between integration of the GIS and data quality.
• User are generally confident in the data.
• Utilities are doing a better job at ‘completeness’ than ‘accuracy’ of data.
• Benefits of ‘good’ data are seen, but repercussions of ‘bad’ data are not.
10 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Who “Owns” GIS?
IT, 42%
Shared Services, 8%
Engineering, 17%
Other, 17%
Operations, 8%
Each, 8%
11 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Who Maintains GIS? IT, 8%
Shared Services, 8%
Engineering, 17%
Other, 17%
Operations, 25%
Each, 25%
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GIS Market Share
ESRI, 69%
GE Smallworld, 23%
Intergraph, 31%
Other, 8%
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Data Dependencies of GIS
0
1
2
3
4
5
6
7
8
9
10
OMS DMS
Engineering Analysis CMMS
CIS
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Data Inputs to GIS
0 0.5
1 1.5
2 2.5
3 3.5
4 4.5
5
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Direct Users of GIS Data
0 1 2 3 4 5 6 7 8 9
10
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GIS Functionality
0%
10%
20%
30%
40%
50%
60%
70%
17 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Utilities' Expressed Level of Data Quality
0%
10%
20%
30%
40%
50%
60%
70%
Worse than 50% 50% - 75%
75% - 90% Better than
90%
Data Accuracy
Data Completeness
18 © 2012 Electric Power Research Institute, Inc. All rights reserved.
EPRI GIS Data Quality Survey – Phase 1
Of the thirteen utilities that participated in the survey:
• 36% store all distribution data in GIS, but 66% make use of an asset management system.
• 66% have unique asset IDs, only 27% physically tag the asset in the field.
• 54% felt that data accuracy was 75-90% (64% user confidence in data).
• 63% felt that data completeness was 75-90% (72% user confidence in data).
• Only 9% of utilities have experienced a catastrophic problem due to data, but 56% have enjoyed a benefit of good data.
• While 91% have programs to improve data, only 54% have dedicated staff.
• 73% have automated quality assurance.
• 91% have not seen quality deterioration over time.
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Cost Benefit Model for GIS Functions
Benefits
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Cost/Benefit Analysis Guiding Documents
• “Methodological Approach,” published Jan, 2010
– Jointly funded by DOE and EPRI
– Provides framework for estimating benefits & costs
– Provides definitions, concepts and data sources
– Publicly available: Product ID 1020342
• “CBA Guidebook, Volume 1: Measuring Impacts,” published May, 2011
– provides a manual for practical application with step by step instruction
– provides guidance for documenting the project in detail and approach to perform a CBA,
– includes templates for working through the process.
– Publicly available: Product ID 1021423
21 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Overview of Smart Grid Evaluation Process
Smart Grid Assets
Determine Benefits (monetized)
Determine Impacts
(physical measures)
Smart Grid Functions
SG Assets
Functions Functions
Benefits
Tables 4-4 and 4-8 in “Methodological Approach” and Tables 5-1 and 5-2 in “CBA Guidebook”
22 © 2012 Electric Power Research Institute, Inc. All rights reserved.
CBA Terminology: Impacts, Metrics, and Benefits
• An Application is a selection of functions for a given configuration and system context.
• Impacts are measurable physical changes within the bounds of the system under study.
• Impacts are differences between a measured quantity and its baseline measurement.
• Benefits are monetary products of impacts. Some may be negative, i.e., costs.
• In short: We measure impacts, calculate metrics, monetize costs and benefits.
System (Program, Project,
Sub-Project)
Device1 Device2
Device3 Device4
Costs/ Benefits
Costs/ Benefits
Costs/ Benefits
Function 1
Function 2
Function 3
Application
System Configuration & Operation
• Location • Connection • Direction of
Influence • Point of Impact • Intended Use
Market Environment • Market versus
Integrated Utility • Regulatory conditions
Impacts
Impacts
Impacts
Metrics
Metrics
Metrics
Measure Calculate (algorithms)
Monetize
Functions: Physical
Capabilities
Application: Use of System
in its Environment
Impacts: Measurable
Physical Changes
Metrics: Calculated
from Impacts
Costs/Benefits: Monetized Impacts
Systems: Combination of
Devices and Software
Project4
Project2 Project1
Project3
23 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Expected Impacts of Improved Data
Reduction in the overall cost of operations as a whole - Sloppy data may be easier and cheaper to maintain, but yields poor engineering decisions;
Increase efficiencies in implementing and troubleshooting Smart Grid communications issues;
OMS and DMS improvement – Outage and distribution management systems are heavily reliant on the accuracy of the connected model. As connectivity and switching increase in accuracy outages can be isolated and repaired more quickly resulting in reduced outage duration, metrics and cost;
Improved crew efficiencies - Improved distribution system representation allows crews to locate field assets more quickly, to drive less and have correct replacement parts;
Improved load forecasting and system planning effectiveness;
Reduced work order creation, construction, and close out process time – Designs are posted to the GIS more quickly such that staff have maps which actually reflect the as-built;
Improved material management and forecasting efficiency;
Enabled information exchange with internal and external agencies; and
Improved safety due to more accurate facilities records – Crews should never rely solely on mapped information to protect their health and safety.
24 © 2012 Electric Power Research Institute, Inc. All rights reserved.
CBA Compares Two or More Alternatives
In our demonstration framework, the reference case can be called the “Baseline Scenario.” – If “Do nothing” is a viable alternative,
then the project is discretionary. • “Do nothing” is the “Baseline Scenario.” • CBA compares incremental costs and benefits
relative to the Baseline “Do nothing” scenario.
– If “Do nothing” is not a viable alternative, then action is imperative: i.e., there is a problem that must be solved.
• The least-cost solution forms the “Baseline Scenario.” • CBA determines the least-cost solution. • Remaining alternatives are discretionary projects
that may “layer” over the Baseline Scenario. • CBA compares incremental costs and benefits of each layer.
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“Layering” of Alternatives to Isolate Impacts
• Interdependent projects “layer” on the baseline scenario.
• Impacts should pair with the investments that produce them.
Discretionary Project 1
Baseline Scenario (includes obligatory
investments)
Discretionary Project 2
Incremental Cost of Project 1
Incremental Cost of Project 2
Baseline Measurements
CBA1
CBA2
CBA3
Incremental Cost of Project 3
Measurements
Measurements
Measurements Discretionary
Project 3
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Discretionary Project 1
Discretionary Project 2
Baseline Scenario (includes obligatory
investments)
Incr $ Incr $
Measurements Incr $ Incr $
Measurements
Measurements
Measurements
Baseline Measurements
“Layering” of Alternatives
Mutually exclusive alternatives may necessitate multiple paths through various layers of projects.
Discretionary Project 3
Discretionary Project 3
27 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Examples of EPRI GIS Data Improvement Projects
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Automating Phase Identification Using AMI Data
Correlating Voltage
AMI Voltage
Data
SCADA Voltage
Data
Customer Phase ID
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Phase Identification Example
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Field Force Data Visualization
• Inexpensive to deploy • Inexpensive to maintain • Applications:
– GIS data improvement – Asset maintenance manuals – Storm damage assessment – Asset information access – Switching communications – Work-order information flow – Real-time system status
validation – Visualizing faults in the field
Field Work Becomes Easier and More Efficient
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“LineView” – Automated Asset Recognition
• Uses images from: • Google street view • Utility aerial images • Utility ground images
• Pattern recognition utilizing “neural networks”
• Automated way of completing GIS
32 © 2012 Electric Power Research Institute, Inc. All rights reserved.
The Cost Benefit Model Functions
Benefits
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Systems (Costs) for the CBA
Parameter Description Area Primary Objective
Data creation Time/effort for process of data creation Clean-up Utility Operational Efficiency
Data maintenance Reduced effort for maintenance Clean-up Utility Operational Efficiency
Current data assessment Required understanding of existing data limitations Clean-up Utility Operational Efficiency
Staff/Retirees/Vendor Time Actual time for clean-up process Clean-up Utility Operational Efficiency
QA Team equipment Computers, Monitors, Space Clean-up Utility Operational Efficiency
Software Licenses Additional seats for GIS Clean-up Utility Operational Efficiency
Awareness of Inaccuracies Increased awareness of current state of data Clean-up Utility Operational Efficiency
Automated Routines Programming time Clean-up Utility Operational Efficiency
Vehicles Light-trucks for field survey Field Survey Other
Staff Time or Contractor Field resources knowledgable in electrical system Field Survey Other
Data Input Additional staff time or responsibility for input and oversight Field Survey Other
Data acceptance review Staff time and training Field Survey Other
Equipment Mobile devices and office equipment, GPS Field Survey Other
Historical Inaccuracies in Rate Base Potential to discover rate base has been miscalculated Field Survey Other
Programming Develop interfaces between GIS and other systems Integration Other
Staff Testing and Acceptance Time Interface testing and quality control Integration Other
Licenses For any COTS solutions Integration Other
Software Cost Costs for interfaces or bus Integration Other
Interface Maintenance Ongoing maintenance of interfaces and service bus Integration Other
Process Change Workshops Development of necessary business process change to support data quality improvement Training Other
Change Management Training Staff training workshops Training Other
Data Use Training Reduction of costs associated with intuitive data, processes and systems Training Other
34 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Impacts (Benefits) for the CBA Description Area Primary Objective Prevent orphan database Data Creation Utility Operational Efficiency Provide correlation between databases Data Creation Utility Operational Efficiency Assets are correctly referenced to real world location Data Creation Utility Operational Efficiency Not “St” or “Street” Data Creation Utility Operational Efficiency Staff don’t draw and re-draw designs Data Creation Utility Operational Efficiency Data reflects the as-built more quickly Data Creation Utility Operational Efficiency Reduce data entry Data Creation Utility Operational Efficiency Efficiency in office Data Creation Utility Operational Efficiency Design correctly the first time Data Creation Utility Operational Efficiency More users have ability to edit basic attributes Data Maintenance Utility Operational Efficiency Documentation for future changes Data Maintenance Utility Operational Efficiency Time savings Data Maintenance Utility Operational Efficiency Time savings Data Maintenance Utility Operational Efficiency Balance storage and creation efficiency Data Maintenance Utility Operational Efficiency Accurate routing and problem location identification Operations Utility Operational Efficiency Reduce ‘no address’ calls Operations Utility Operational Efficiency
Bring correct replacement materials, no need to measure conductor size Operations Utility Operational Efficiency Less drive time Operations Utility Operational Efficiency Better understanding of existing plant Operations Utility Operational Efficiency Maps reflect the as-built field condition Operations Utility Operational Efficiency More eyes on the data, shared ownership Operations Utility Operational Efficiency Access to customer/premise information Operations Utility Operational Efficiency Reduce export time and effort to OMS Operations Utility Operational Efficiency Staff acceptance and use of data Operations Utility Operational Efficiency
Good data will obviate other sources and files which have been necessary to supplement bad data Engineering/ Analytics Utility Operational Efficiency Model accuracy Planning Utility Operational Efficiency Greater confidence in analysis Planning Utility Operational Efficiency Savings due to data quality improvements Planning Utility Operational Efficiency Better metrics and visibility in real-time data quality Planning Utility Operational Efficiency Able to find/analyze assets (San Bruno Explosion) Operations Utility Asset Efficiency
Designs are electrically connected to model Data Creation System Operational Efficiency
System integration and data sharing Data Maintenance System Operational Efficiency
Connected model from substation to transformer to customer Operations System Operational Efficiency
Precision for smart grid devices Operations System Operational Efficiency
Powerline Loss Engineering/ Analytics System Operational Efficiency
Balance loading to three phases Engineering/ Analytics System Operational Efficiency
Identify opportunities for efficiency or excess capacity Planning System Operational Efficiency Prevent unplanned outage Operations Reliability SAIDI, CAIDI, SAIFI improvement Engineering/ Analytics Reliability Certainty of existing system Planning Reliability Provide accurate information to crews Operations Other Theft Engineering/ Analytics Other Statistic and Metric accuracy Engineering/ Analytics Other Goodwill and headline avoidance Planning Other Less negative publicity Planning Other Goodwill with important/large customers Planning Other Confidence in company direction and management Planning Other Confidence and goodwill of regulatory agency/board Planning Other Workplace satisfaction and dedication Planning Other Efficiency with replacements, i.e. PCB phase-out Planning Other Accuracy and completeness, i.e. number of poles Planning Other Assets are added and capitalized more quickly Planning Other Pay the correct district Planning Other Recover lost revenue Planning Other Reduction of Unknown third party attachments Planning Other Potential revenue from sale of quality data Planning Other Eased data sharing Planning Other Methodology and consulting services Planning Other
• List of impacts of improved GIS data quality
• There is a factor of the likelihood of the user achieve the impact that was obtained from the second EPRI survey
• The user will assign the monetary value based on their unique situation
35 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Net Present Value
Where:
r = discount rate
t = year
n = analytic horizon (in years)
∑= +−
=n
t t
t
rCostsBenefitsNPV
0 )1()(
NPV is calculated by summing the dollar-valued benefits and then subtracting all of the dollar-valued costs, with discounting applied to both benefits and costs as appropriate.
A CBA will yield a positive NPV if the benefits exceed the costs.
36 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Internal Rate of Return
Discount Rate
Net
Pre
sent
Val
ue
Internal Rate of Return - IRR
NPVirr = NPVcash in – NPVcash out
37 © 2012 Electric Power Research Institute, Inc. All rights reserved.
Together…Shaping the Future of Electricity
Thanks: • Jeff Roark • Tom Short • Jared Green • Jerry Gray • Matt Olearczyk • Boreas Group
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