Capgemini ses - smart grid operational services - gis pov (gr)

management, engineering design and
construction and to support advanced
applications such as outage management,
GIS has been around most electric
utilities for many years.
As they begin to implement Smart Grid
initiatives, utilities are increasingly
realizing that the accuracy of the data
stored in their GIS is essential to their
projects’ success. As a result, companies
are concentrating on two GIS issues that
significantly impact Smart Grid
implementations: how the initial GIS
was implemented (how data was
modeled) and the quality
Introduction
After worldwide spending on Smart
Grid initiatives in 2008 exceeded
$12 billion—a total expected to
surpass $33 billion within the next
five years—utilities are intensely
focused on maximizing their Smart
Grid investments. To that end,
companies are reviewing the state of
their existing infrastructure systems.
To successfully meet the future business
expectations of Smart Grid, the
availability of complete and accurate
grid data is essential. Therefore, the
Geographic Information System (GIS)
is a critical component.
Installed for a number of reasons
including paper mapping
displacement, asset
Conducting a GIS Data
Refresh—The Foundation
of Your Smart Grid Program
GIS refresh strategies, processes and considerations
Energy, Utilities and Chemicals the way we see it

are visible, reasonably easy to access and
fairly easy to understand. Operators need
only to start at the substation then follow
the wires, noting any electrical device or
change. They do not need special skills
or experience that cannot be provided
through simple training. They can be
outside contractors or unassigned
internal staff. Crew size can be one-man
or two-person depending on local utility
requirements and desires. If the feeder
can be driven, sometimes the increased
cost of two-man crews can be offset by
the increased productivity. An allocation
should be provided to cover overhead
and transportation costs.
Factors that affect productivity
include the type of electrical
construction, the number of feeders
per right-of-way, feeder accessibility
(backyard, alley, etc.), number of
devices per feeder, number of
customers per feeder, the amount of
new data to be collected and most
importantly, the number of GIS data
errors detected that must be
documented. An overhead walk-down
usually has minimum safety concerns
since the walk-down staff is not
required to make contact with
electrical equipment.
Underground
A walk-down of underground
construction is much more complex
and expensive. Unlike overhead,
underground construction requires
inspection teams to open enclosures,
man-holes and vaults. This increases
the complexity, safety concerns and
crew skill requirements. These issues
usually result in the need for
supplementary crews and equipment
support to meet safely requirements.
The result is increased costs and
reduced productivity. An allocation
should be provided to cover overhead
and transportation costs.
Factors that affect productivity are
basically the same as for overhead with
the added complexity of exposing the
underground network for inspection.
Again, these factors include the type of
of the electric network data contained in
the GIS. Utilities that implemented GIS
as an asset model frequently discover
that the electric network data may not be
electrically connected creating complete
electric feeders as required by Smart
Grid. The other issue of data quality is
obvious -- with incorrect network feeder
data, the Smart Grid is unable to
correctly analyze and operate the
electric network.
Solving both of these issues requires
refreshing and correcting both the
model and the data contained in the
GIS. However, as companies quickly
learn, conducting a GIS data refresh is
more complex than performing an
initial GIS data loading. For most
utilities, the initial data loading was
easier to estimate because all data had
to be validated and input into the
system. With a GIS data refresh, all the
data must be reviewed and validated
(typically called a “walk-down”) but
only new data and errors must be
updated ("data posting"). Because many
factors can affect the costs of a refresh
effort, the purpose of this paper is to
review and discuss GIS refresh
strategies, processes and considerations.
Feeder Walk-Down
The feeder walk-down is the process of
an operator physically walking each
feeder starting at the substation and
tracing it to the end of each branch.
During the walk-down, the operator
validates all current GIS data and
collects any missing data. In addition
to correcting the current GIS data
elements, if Smart Grid initiatives
require data that is not currently
maintained in the GIS, the operator
must update the GIS data model and
collect the additional data during the
walk-down. This is usually done by
printing paper feeder maps from the
GIS and marking up the maps with
discrepancies as the operator walks
the feeder.
The data to be captured or updated
depends upon the utility’s data
requirements. However, it typically
includes electrical connectivity, circuit
phasing, electrical asset information, sizes,
ratings, and conductor configurations.
Two major factors impact walk-down
costs: the labor rates of the walk-down
field operators and their productivity.
While the labor rates can usually be
easily determined, field crew productivity
can be impacted by a number of factors
which vary between overhead and
underground construction. We will look
at each type of walk-down separately
because they vary significantly.
Overhead
The easiest and least costly walk-down is
of overhead construction. These feeders
2

Energy, Utilities and Chemicals the way we see it
electrical construction, the number of
feeders per right-of-way, feeder
accessibility (vault, man-hole, etc.),
number of devices per feeder, number of
customers per feeder, the amount of new
data to be collected and most importantly,
the number of GIS data errors detected
that must be documented.
Data Posting
Following the walk-down and the
documentation of the GIS data to be
updated, the next major step in a GIS
refresh is the posting of the documented
update data to the GIS. This process is
time-consuming but relatively
straightforward. Many companies
outsource the data-posting process,
usually to an off-shore firm to benefit
from the reduced labor costs.
The first step is to transfer the data
updates to the off-shore vendor. If the
data updates were captured on paper
maps, there are two options to
transferring the data: the maps can be
copied and mailed or they can be
scanned and electronically transferred.
The data-posting vendor’s job and cost
is determined by three factors: the
number of updates to be posted, the
productivity of the GIS software tools
and the business processes imposed by
the utility. It is assumed that the vendor’s
staff is skilled and trained on the
appropriate GIS system.
The vendor normally uses the following
factors to estimate the total cost for
data posting: the number of feeders,
the number of conductor segments
(not conductor miles), the number of
pieces of electrical equipment, the
number of customers per feeder, the
number of breaks or jumpers, and the
estimate of the percentage of errors that
will require posting.
By totaling the number of facilities on a
feeder and assuming that 30 percent of
feeder data requires updating, you can
calculate the number of updates. If you
assume three to five minutes per
Conducting a GIS Data Refresh—The Foundation of Your Smart Grid Program 3
change, which is dependent upon the
GIS system tools available and the
business process of the utility, you can
determine the cost per update and per
feeder. You must also provide for the
QA/QC processes, which will add
additional time and costs per feeder.
First Steps – Forecasting Model
The first steps in any GIS refresh are to
estimate the cost of the data refresh,
forecast the expected benefits and
develop the business case. To estimate
the effort and cost of a GIS refresh, it is
necessary to build a cost forecast model.
This is accomplished by first performing
an analysis of the GIS data for the feeders
to be refreshed. Any new data
requirements must be accounted for and
included in the GIS model.
The initial analysis should capture as
much data as possible about the
feeders including:
■ Facility counts (all major facility types)
■ Distance measurements
(miles of conductor)
■ Conductor segment counts
■ Construction characteristics
- Overhead vs. underground
- Three-phase or single phase
- Multi-circuit right of way sharing
- Vertical vs. horizontal on overhead
■ Neighborhood characteristics
- Urban or rural
- Front lot or back lot construction
(can you drive or must you walk?)
- Ease of access to facilities
(high, medium or low)
- Tree coverage
After an initial analysis of the feeder data
and characteristics has been performed,
a sample set of representative feeders
should be selected for a pilot walk-down.
The goals of the pilot are to capture
metrics data on crew performance and
the time required to walk-down the
feeder, as well as to capture a sample
of update metrics.
Walk-down Forecast
Following the pilot walk-down, the
team should conduct a statistical
correlation analysis to determine
which feeder characteristic variables
are significant and contribute most to
estimating the cost and effort required
to walk-down all the feeders selected
for refresh. A separate analysis will
need to be conducted for overhead
and underground feeders since the
characteristics and crew requirements
are so different. The correlation analysis
can be conducted using one of many
different tools available on the market
including Microsoft Excel. The results
of the statistical correlation analysis will

enable you to construct a forecast
model using the significant feeder
characteristics to forecast the time and
cost to walk-down all the feeders with
similar characteristics. Since the pilot
sample size is limited, the forecast
model can be improved as more feeder
metrics are captured.
Posting Forecast
The approach for developing a
forecasting model for data posting is
similar to the walk-down model but the
variables are different. The two factors
that most impact the posting are the
number of data updates required and the
productivity of the operator doing the
posting. The number of updates required
depends on the number of errors in the
existing GIS feeder data and the amount
of new data that must be posted. The
pilot walk-down should give you some
idea of the error rates as a percentage of
facilities on a feeder. The amount of new
data can be determined from the data
requirements and from the pilot walk-
down metrics. This data then can be
statistically analyzed to develop a
forecasting model. The operator
productivity is based on several factors.
The most significant are the GIS system
being used and the software tools
provided with the system. In addition,
the business process imposed by the
utility can have significant impact. The
business process can range from simple
editing (fastest) to a complex asset-
management work order process (much
slower). And finally, the individual skill
and talent of the operator comes into
play as well. However, with this factor
the operator tends to increase in skill as
the job progresses. As always, the
quality-control process must be added
into the overall posting process.
The pilot sample will allow you or the
vendor to capture metrics on posting
productivity and cost. This data can then
be used to develop a forecasting model.
If the utility used an off-shore data
conversion vendor, the vendor will have
a great deal of experience in forecasting
the effort and cost.
EUC_SGGISDTPOV_051209_025
Copyright © 2009 Capgemini. All rights reserved.
www.us.capgemini.com/energy
Capgemini, one of the
world's foremost providers
of consulting, technology and outsourcing
services, enables its clients to transform
and perform through technologies.
Capgemini provides its clients with insights
and capabilities that boost their freedom to
achieve superior results through a unique
way of working, the Collaborative Business
Experience. The Group relies on its global
delivery model called Rightshore®
, which
aims to get the right balance of the best
talent from multiple locations, working as
one team to create and deliver the
optimum solution for clients. Present in
more than 30 countries, Capgemini
reported 2008 global revenues of
approximately $12.74 billion and employs
over 90,000 people worldwide.
More information is available at
www.us.capgemini.com/energy.
About Capgemini and the
Collaborative Business Experience
®®
Summary
For most utilities, the key source of both
grid asset and feeder connectivity is their
GIS. Analysis of systems across North
America and throughout the world has
shown that while most utilities have
invested significant effort and cost in
creating their GIS, their data quality has
been in steady decline due to lack of
business emphasis and ineffective
supporting business processes. In
addition to data deterioration, the Smart
Grid may require additional data not
currently modeled in the GIS. This new
data capture could be significant if the
current GIS data model is inadequate.
In fact, depending upon the state of data
corruption and the number of new data
elements, the GIS data refresh project
can be a significant undertaking. That’s
why the utility should assemble an
experienced project team, with
experience in GIS and GIS data capture.
Remember, one reason for the GIS
data refresh is poor data quality.
Assuming you invested in good data
when the GIS system was initially
installed and the data quality has been
deteriorating, you will need to protect
your refresh investment with
improved business processes. The
bottom line? The walk-down is only
as good as the business processes that
keep the data maintained.
Gord Reynolds
Practice Leader
Smart Energy Services
gord.reynolds@capgemini.com
+1 416.732.2200

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