GIS-Based Design for Effective Smart Grid Strategies

GIS-Based Design for Effective
Smart Grid Strategies

Executive summary
An accurate, up-to-date model of a utility’s distribution
network is the backbone of Smart Grid technologies. But
a Schneider Electric survey shows that 74% of utilities
are concerned about the readiness of their network
model to support Smart Grid applications. This paper
presents a quantitative comparison of a Geographic
Information System (GIS)–based graphic work design
system vs. a CAD-based tool, demonstrating how the
GIS-based design approach is better able to keep up
with the continuous changes in a dynamic electrical
distribution network.

998-2095-05-20-12AR0

Summary
Executive Summary . ................................................................................... p 1
Introduction ................................................................................................. p 4
It’s all about the network . ............................................................................ p 6
The state of the GIS . ................................................................................... p 7
Graphic Work Design process can induce error............................................ p 9
GIS-based GWD improves network data accuracy and completeness ......... p 10
Quantitative comparison of design methodologies ....................................... p 11
Conclusion................................................................................................... p 12

GIS-Based Design for Effective Smart Grid Strategies

Executive summary
The Distribution Management System (DMS) is a key smart grid technology; and
complete, up-to-date network asset information is mandatory in order to develop
and maintain the accurate network model on which the DMS is based. The utility’s
enterprise geographic information system (GIS) database stores and maintains
this network asset data and can manage the workflow for updating this vital
information.
In evaluating its readiness for Smart Grid implementation, the utility needs to
assess the completeness, accuracy and backlog of the data contained in its GIS.
One utility determined that inaccurate data accounted for a 50 percent deviation
between DMS-modeled load flow and observed load flow. This discrepancy made
it clear to the utility that its DMS is unusable to predict voltage reduction gains
and volt/VAR optimization – the very operational functions the utility targeted in its
smart grid strategy.
Many errors in the GIS data can be attributed to a complex or duplicative graphic
work design (GWD) process, from initiation, through design, review, lockdown,
posting and as-built updating. A GIS-based GWD works within the GIS database
and eliminates re-digitization of designs; takes advantage of network connectivity
for QA/QC checks to improve data accuracy and completeness; and significantly
reduces backlog and speeds network updates. This approach not only
streamlines the design process but also yields more accurate asset information
that supports network modeling, maintenance and vital planning and decision
making processes.
A quantitative comparison of the time involved in the typical workflow of a small
design project, completed by means of manual sketching and also with GISbased and CAD-based methodologies, shows the time savings related to the GISbased GWD are accountable and significant. The same productivity advantages
were seen when comparing typical workflows involved in a large design project.
GIS-based design provides faster update of the network model, making it more
appropriate for supporting DMS functionality – the heart of an effective smart grid
strategy.

White paper | 01


Introduction
The industry’s experience and knowledge of smart grid capabilities and
benefits are growing rapidly, and utilities’ smart grid plans are becoming more
sophisticated every day. While AMI once was considered the ‘golden child’ of the
smart grid, utilities now see the Distribution Management System (DMS) as one
of the most important components of an effective smart grid strategy. Perhaps
not as readily acknowledged is the vital role an enterprise Geographic Information
System (GIS) plays in driving the accurate network model needed for DMS
implementations.
In this paper, we highlight the value of an enterprise GIS-based graphic work
design (GWD) system in maintaining an accurate distribution network model – the
heart of an effective smart grid strategy. Inefficient GWD processes do not keep
up with the network changes continuously being planned and executed in the
dynamic electric distribution network. The resulting out-of-date or inaccurate
network model can not drive mission-critical smart grid applications such as
OMS, DMS, DSDR and VVR and, consequently, compromises potential smart grid
efficiency enhancements and operational improvements. An efficient, GIS-based
GWD solution is key to building a robust smart grid foundation.

White paper on Energy Efficiency | 02

GIS-Based Design for
effective Smart Grid
Strategies


It’s all about the network
DMS a key technology
In a survey conducted by Microsoft in 2010,
approximately 70 percent of the utilities responding
rated DMS as a very important technology in
implementing tomorrow’s smart grid.1 Indeed, the
Department of Energy identified2 five fundamental
technologies that will drive the smart grid; in addition
to communications, response capability and storage,
two DMS-related technologies make this list:
• dvanced control methods, to monitor essential
A
components, enabling rapid diagnosis and precise
solutions appropriate to any event
• mproved interfaces and decision support, to
I
amplify human decision-making, transforming

Utilize existing asset and network data at the start of your
GIS-based design - perform advanced structural analyses,
sag, tension and clearance investigations ensuring
adherence to regulatory requirements.

grid operators and managers quite literally into
visionaries when it comes to seeing into their
systems

DMS needs accurate data
The DMS is based on a network model that must

DMS functionality is all about the network: accurate

accurately reflect:

up-to-date network data yields an accurate model
driving advanced DMS functions that deliver the

• etwork asset information
N

network improvements expected. Conversely,
inaccurate or stale data results in a poor network

• eal time data
R
• ime series data
T

model yielding unexpected and often ineffective
results.

• ransactional data
T

GIS key in network data integrity
It is the utility’s enterprise GIS database that not
only stores and maintains network asset data but
also manages the workflow for updating this vital
information. GIS data is the key to maintaining the
accurate and up-to-date network model that is fed to
all smart grid information systems, including SCADA,
DMS, OMS, MDM and others.
1
Microsoft, March 2010. Worldwide Utilities Industry Survey
2
Department of Energy, Office of Electricity Delivery and Energy Reliability, 2008. The Smart Grid: An Introduction.
White paper | 04


The state of the GIS
GIS Readiness
A benchmark study conducted by Esri® queried
226 electric utility companies in the U.S. and
worldwide about their confidence in the accuracy and
completeness of the GIS serving their operations. The
results, made available by Esri to the public in a 2010

GIS data backlog – Only one-third of utilities say
they update their GIS data within ten days of field
work completion. One in four respondents reported
there is information older than six months that is not
reflected in their GIS; see Figure 2.

report,3 suggest many utilities might not be ready
to rely on their network model repository to support
smart grid applications –
GIS data completeness – Less than 70 percent
of respondents report having a complete model of
their primary distribution, because not all data related
to distribution assets has been converted to the
enterprise GIS.
GIS data accuracy – Respondents expressed they

Figure 2. Utilities report that work orders can be
outstanding for six months or more before GIS updates
reflect completion of the field work. Source: Esri, 2010.

have detected high rates of data error in their GIS
databases; see Figure 1.

Figure 1. When asked to assess GIS data error rate, only 15
percent of survey respondents reported high confidence
(less than 2 percent error) in the accuracy of their GIS data.
Source: Esri, 2010.

GIS data quality
Often, the problems with GIS data quality have to

• hase mismatches; see Figure 3
P

do with information that should be part of any utility
GIS. Data errors exist because the data was entered
incorrectly or converted or migrated inaccurately. In
some cases, the data was managed by a system,
such as CAD, with a lack of network integrity rules;
in other cases, the information wasn’t entered at all.
Errors include –
• ransformer/customer connectivity
T
3

Figure 3. This GIS map shows phase mismatch, with C-phase transformers and
A-phase transformers on the same C-phase line.

Esri, 2010. Is Your GIS Smart Grid Ready? A State-of-the-Industry Report.
White paper | 05


- hase changes between conductors
P
- evices/conductors where phase is null
D
- evices and conductors that are incorrectly
D
looped or multi-fed
• oltage mismatches
V
- onductor voltage changes without a tap or
C
transformer
- evices and conductors where voltage is null
D
- evices that have a different voltage than their
D
connected conductors
• evices, particularly switches, with null or duplicate
D
IDs
• tandard geometric problems such as
S
disconnected devices or conductors
Errors such as those identified above often can
be attributed to poor data management workflow
when graphic work design (GWD) is performed in a

+

Not all utilities are confident
Schneider Electric user surveys suggest that
the reliability of their network model might be
keeping many utility managers up at night –
W
• hat is your network model’s readiness to
support Smart Grid applications?
- 1 percent – Missing data or
6
inaccuracies
- percent – Many missing data elements/
4

inaccuracies
- percent – Not ready
9
• What is the status of the data management

workflow to maintain this model to smart
grid requirements?
- 5 percent – Some gaps and
6
inaccuracies
- percent – Poorly established
4
• What concerns are you focusing on?
- 1 percent – GIS Data Quality
8
Improvement

non-GIS-based system. Continuous data correction
results in backlog and a network model that is never
quite accurate.

Consequences of inaccurate GIS data
At one utility, inaccurate data accounted for a 50 percent deviation between DMS-modeled load flow and
observed load flow. The utility was not able to use DMS to predict voltage reduction gains and volt/VAR
optimization – the very operational functions targeted in its smart grid strategy. Just for the record: in this
instance, the model used inaccurate conductor material and size attribute data and other inaccurate device
information fed to it from the GIS database.
This utility used two different GWD systems with separate workflows, neither of which was GIS-based.

White paper | 06


Graphic Work Design process can
induce error
The distribution network is dynamic and always changing
Subdivisions are added, businesses and homes

Projects like these add, remove and switch load

require more power, load shifts occur with seasonal

and change the network continuously. When the

variability, and outages occur. The GWD process

design is completed, the network model must be

by which a utility extends, maintains and upgrades

updated quickly and accurately so systems using that

its infrastructure is involved in nearly any daily work

information can perform as expected.

order:
• New Services
• Capital expansions
• Replacements
• Conversions/upgrades

Design issues vary widely
Designs can be simple or highly complex; and the

different designs can be occurring simultaneously,

workflow – from initiation, through design, review,

both involving a common asset such as a pole or

lockdown, posting and as-built updating – can be

fuse and requiring collaboration. Some designs might

simple or highly complex. Within the same utility,

take years to complete, with partial postings done as

different workflows involving different departments

each phase is complete.

might be applied on different projects. Further,

A poor data management workflow defeats the purpose
Performing inefficient and complex GWD workflows
independently of the network GIS database – while
being challenged by varying design complexity,
project overlap, long transactions and partial
postings – is a recipe for disaster. These conditions
increase the backlog, introduce core data errors and
contribute to an inaccurate and out-of-date network
model. All of this compromises the enhanced
efficiencies and operational improvements that any
smart grid strategy promises.

Figure above. Utilize accurate GIS landbase data to
perform advanced design optimization analyses.

White paper | 07


GIS-based GWD improves network
data accuracy and completeness
Streamlines the overall GWD workflow
A GWD that works within the GIS database:

• ignificantly reduces backlog – direct update of
S
the network model within minutes

• oes away with re-digitization of designs into
D
the GIS – and the opportunity for error

• ast turnaround of network update – preliminary
F
or even partial posting to publish design effects

• nforces Network Integrity and QA/QC rules
E

before a design is energized; reduces the backlog

– maintains valid network connectivity and data

between completion of construction and network

integrity on the fly, using QA/QC checks to catch

model update

errors, ensuring data is entered properly from the
start

• liminates custom CAD-GIS integration –
E
typically necessary to import designs from CAD
systems

Benefits tangible and intangible
Utilities report that GIS-based design returns tangible

Utilities also report the significance of the intangible

productivity benefits and significant operational cost

benefits –

savings –
• educed labor involved in map preparation and
R
updates: cost savings from $40,000 to $160,000

• mproved customer satisfaction: allows better
I
response to service calls

a year
• etter decision support: graphical display of work
B
• ncreased productivity due to improved access to
I

data for crew coordination and outage dispatch

information that supports network analysis and
operational studies: cost savings from $38,000 to
$360,000 a year
• mproved engineering and maintenance efficiency
I

• etter safety practices: support for crew safety and
B
dig-safe activities, sharing data with other utilities
• tandardized design practices – consistency of
S

in system upgrades, reducing necessary plant

materials; capturing the knowledge of experienced

investment: cost savings from $200,000 to $1.5

workforce

million
Most importantly, the GIS-based GWD process
• educed overall cable for UG installation: cost
R
savings of 4 percent of total budget

provides a single point of entry and unified workflow
for asset data and network model management. The
central asset and network repository – the single
version of the truth, not five versions of the partial
truth – feeds an accurate, up-to-date network model
to mission-critical smart grid systems. Remember:
smart grid is all about the network.

White paper | 08


Quantitative comparison of design
methodologies
GIS-based design vs. CAD
and manual sketching
To quantify some of the tangible benefits listed above,
Schneider Electric examined the productivity of using
a GIS-based design tool, Schneider Electric Designer,
compared to that of using manual sketching and
CAD-based tools. The purpose was to identify areas
where GIS-based design using Designer can improve
productivity and to quantify the overall efficiency gains
that can be achieved through implementing a GISbased design tool.
The comparison included both small and large
design jobs in typical design workflow. Small design
jobs involved new customer connections or minor
upgrades that averaged approximately 5.5 hours for

2. Gather preliminary design information: Before

completion and reflected small budgets of $2,600

the design layout, most projects require an

to $5,000. Large design jobs included network

assessment of the location of existing plant in

reinforcement, mains replacement, road widening or

service, customer requirements and other factors.

large residential or commercial subdivisions. Large

For smaller projects, this aspect of the design can

designs averaged 47.5 hours for completion and had

be a significant percentage of the total effort.

much larger budgets.
3. Layout design: This is the step in which the new

The term “typical design workflow” might seem like

facilities or modifications to the existing network

an oxymoron, because design workflows within a

are specified through the staking/sketching/

utility can vary greatly, as much as those among

engineering process.

different utilities. For some projects, certain steps
might be sequenced differently or even omitted.

4. Tabulate materials: In this step the materials

However, the nine tasks described below are useful

required to build the job are totaled and reported.

for this study.

In some work processes, these materials may be
reserved for use on the project through a materials

1. nitiate work: This step usually starts with a
I

management system.

service request, repair or maintenance requirement
or an internal capital improvement project

5. Prepare cost estimate: Nearly all workflows

and is often generated from a separate work

require the planner/designer/technician to estimate

management system (WMS).

the cost of the project before proceeding to
construct it.

White paper | 09


6. evelop work package and construction
D

8. Update design sketch for as-built changes:

sketch: Often the most time-consuming step,

Many designs are modified to meet field

the development of the work package is the

requirements, with the result that a drawing mark-

communication medium between the designer

up is needed to true up the original sketch to

and the construction crew. It might include permit

reflect the actual as-built conditions.

drawings, crew instructions, a report of the
9. Post as-built changes to enterprise

materials tabulation and other standard notes.

database(s): Most job closeout processes
7. ubmit for approval, release for construction
S

require that facilities changes be documented in

and build: Depending on the cost of the estimate,

a mapping system (either manual or digital), and

most workflows require some form of approval

sometimes in tabular databases for plant or asset

before committing to construction of a design,

records systems.

either by a supervisor or customer or both. Then
the design is issued to crew and constructed.

Results
For both small and large jobs, an assessment of the

sketching showed a 48 percent reduction in overall

time for each task in the “typical design workflow”

time (about 2.64 hours); Designer vs. CAD showed a

was made for each of the three methods (manual,

40 percent reduction in overall time (about 1.9 hours).

CAD and GIS-Based). The results shown in Table 1

For large design jobs, Designer vs. manual sketching

and Table 2 clearly indicate that a GIS-based design

showed a 38 percent reduction in overall time (about

tool such as Designer is the much more productive

17.95 hours); Designer vs. CAD showed a 13 percent

method. For small design jobs, Designer vs. manual

reduction in overall time (about 4.4 hours).
TABLE 1

Design Productivity for Small Jobs

Workflow Task
1. Initiate Work
2. ather preliminary design
G
information
3. Layout design
4. Tabulate material
5. Prepare cost estimate
6. evelop work package
D
and construction sketch
7. ubmit for approval,
S
release for construction,
and build
8. pdate design sketch to
U
reflect as-built conditions
9. ost as-built changes to
P
enterprise database(s)
Total Hours/Savings

Manual
Design
Hours
0.3
1.25

Percent
of Total
Project
5.43%
22.64%

CAD
Design
Hours
0.30
1.06

GIS-Based GIS-Based
GIS-Based
Design
Savings over Savings over
Hours
Manual, %
CAD, %
0.27
10%
10%
0.56
55%
47%

0.67
0.33
0.33
0.67

12.14%
5.98%
5.98%
12.14%

0.77
0.33
0.33
0.34

0.84
0.07
0.10
0.44

-25%
80%
70%
35%

-9%
80%
70%
-30%

0.17

3.08%

0.17

0.14

15%

15%

0.3

5.43%

0.20

0.17

45%

18%

1.5

27.17%

1.28

0.30

80%

76%

5.52

100.00%

4.77

2.88

48%

40%
White paper | 10


TABLE 2
Design Productivity for Large Jobs

Workflow Task
1. Initiate Work
2. ather preliminary design
G
information
3. Layout design
4. Tabulate material
5. Prepare cost estimate
6. evelop work package
D
and construction sketch
7. ubmit for approval,
S
release for construction,
and build
8. pdate design sketch to
U
reflect as-built conditions
9. ost as-built changes to
P
enterprise database(s)
Total Hours/Savings

Manual
Design
Hours
2
8

Percent
of Total
Project
4.21%
16.84%

GIS-Based
GIS-Based
Savings over Savings
Manual, %
over CAD, %
10%
10%
55%
47%

16
3
2
8

33.68%
6.32%
4.21%
16.84%

9.60
3.00
2.00
4.00

13.60
0.60
0.60
6.00

15%
80%
70%
25%

-42%
80%
70%
-50%

0.5

1.05%

0.43

0.35

30%

18%

4

8.42%

2.68

2.20

45%

18%

4

8.42%

3.40

0.80

80%

76%

47.5

100.00%

33.91

29.55

38%

13%

See the sidebar on this page describing one energy
company’s improved efficiency with GIS-based GWD.

What does this mean to
the smart grid?
The two most critical steps in the GWD workflow
tasks for efficient smart grid operations are Task 2:
Gather Preliminary Design Information and Task 9:
Post As-built Changes to Enterprise Database. In the
smart grid context, these tasks can be renamed Task
2: Start from Existing Network Model and Task 9:
Update the Network Model.
And in both of these key tasks, GIS-based design
with Designer saves considerable time:
• In Task 2, Designer is 47 percent faster than CAD.
• In Task 9, Designer is 76 percent faster than CAD.
In other words, GIS-Based design with Designer is
faster at updating the network model. It all starts and
ends with an accurate up to date network model – a
single version of the truth.

CAD
GIS-Based
Design Design
Hours Hours
2.00
1.80
6.80
3.60

Energy company improves workflow,
efficiency
DONG Energy, headquartered in Denmark,
obtains, produces, distributes, deals and
sells energy and associated products in
northern Europe. It specializes in the design
and documentation of new electrical facilities,
developing more than 6,000 sites annually. Prior
to instituting its GIS-based GWD, DONG project
staff worked with a less-than-efficient design
workflow that required hand-drawn sketches to
be transferred to the GIS database and materials
information to be entered into a separate file.
After project completion, the GIS typically was not
updated for five to six weeks.
Now, with a GWD application that integrates
seamlessly with the GIS, all the features selected
in the design phase are automatically saved in the
GIS immediately. The sketch is visible to everyone
in the organization throughout the process.
A formal work analysis has shown this GWD
workflow has reduced project drawing time by 60
percent a year – saving the company almost two
million DKK, or more than $350,000 USD. DONG
energy expects these savings to be consistent
going forward, as this process better positions the
company to support smart grid initiatives.

White paper | 11


Conclusion
Increasingly, utilities are realizing the significance of
DMS in achieving success in their smart grid projects.
To realize the promise of advanced DMS functions,
however, the DMS needs a complete, accurate and
up-to-date network model. An enterprise GIS-based
GWD system is the proven solution for maintaining
network model data accuracy.
Progressive utilities targeting smart grid deployment –
or even looking to save design time and the additional
costs associated with asset and network data errors
– are implementing GIS-based GWD and seeing
significant benefits:

Figure above. Powerful construction sketch
generation tools in a GIS-based GWD application.

R
• educed design time and backlog with direct and
immediate update to the GIS network model
E
• fficient workflows, a unified data store and
streamlined business processes that eliminate
redundant tasks and data errors
I
• mproved availability of network information across
the utility, streamlining analysis, planning and
decision making
M
• aking accurate network information available to
smart grid systems so they can deliver expected
results

White paper | 12

©2012 Schneider Electric. All rights reserved.

Schneider Electric USA, Inc.
4701 Royal Vista Circle
Fort Collins, CO 80528
Phone: -866-537-1091
1

+ (34) 9-17-14-70-02
Fax: 1-970-223-5577
www.schneider-electric.com/us
June 2012

GIS-Based Design for Effective Smart Grid Strategies

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