a monetization of missing and inaccurate gis data for the ... smart grid information... ·...

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


Affordability

Dependability

Efficiency

Vision of the Future


GIS Data Quality Project


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.”


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


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


Data Quality Survey and Results


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.


Who “Owns” GIS?

IT, 42%

Shared Services, 8%

Engineering, 17%

Other, 17%

Operations, 8%

Each, 8%


Who Maintains GIS? IT, 8%

Shared Services, 8%

Engineering, 17%

Other, 17%

Operations, 25%

Each, 25%


GIS Market Share

ESRI, 69%

GE Smallworld, 23%

Intergraph, 31%

Other, 8%


Data Dependencies of GIS

0

1

2

3

4

5

6

7

8

9

10

OMS DMS

Engineering Analysis CMMS

CIS


Data Inputs to GIS

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5


Direct Users of GIS Data

0 1 2 3 4 5 6 7 8 9

10


GIS Functionality

0%

10%

20%

30%

40%

50%

60%

70%


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


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.


Cost Benefit Model for GIS Functions

Benefits


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


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”


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


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.


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.


“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)


Incremental Cost of Project 1


Baseline Measurements

CBA1

CBA2

CBA3


Measurements

Measurements

Measurements Discretionary

Project 3




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.




Examples of EPRI GIS Data Improvement Projects


Automating Phase Identification Using AMI Data

Correlating Voltage

AMI Voltage

Data

SCADA Voltage

Data

Customer Phase ID


Phase Identification Example


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


“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


The Cost Benefit Model Functions

Benefits


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


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


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.


Internal Rate of Return

Discount Rate

Net

Pre

sent

Val

ue

Internal Rate of Return - IRR

NPVirr = NPVcash in – NPVcash out


Together…Shaping the Future of Electricity

Thanks: • Jeff Roark • Tom Short • Jared Green • Jerry Gray • Matt Olearczyk • Boreas Group