management, engineering design andconstruction and to support advancedapplications such as outage management,GIS has been around most electricutilities for many years.

As they begin to implement Smart Gridinitiatives, utilities are increasinglyrealizing that the accuracy of the datastored in their GIS is essential to theirprojects’ success. As a result, companiesare concentrating on two GIS issues thatsignificantly impact Smart Gridimplementations: how the initial GISwas implemented (how data wasmodeled) and the quality

IntroductionAfter worldwide spending on SmartGrid initiatives in 2008 exceeded $12 billion—a total expected to surpass $33 billion within the next five years—utilities are intenselyfocused on maximizing their SmartGrid investments. To that end,companies are reviewing the state oftheir existing infrastructure systems. To successfully meet the future businessexpectations of Smart Grid, theavailability of complete and accurategrid data is essential. Therefore, theGeographic Information System (GIS)is a critical component.

Installed for a number of reasonsincluding paper mappingdisplacement, asset

Conducting a GIS DataRefresh—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 andfairly easy to understand. Operators needonly to start at the substation then followthe wires, noting any electrical device orchange. They do not need special skillsor experience that cannot be providedthrough simple training. They can beoutside contractors or unassignedinternal staff. Crew size can be one-manor two-person depending on local utilityrequirements and desires. If the feedercan be driven, sometimes the increasedcost of two-man crews can be offset bythe increased productivity. An allocationshould be provided to cover overheadand transportation costs.

Factors that affect productivityinclude the type of electricalconstruction, the number of feedersper right-of-way, feeder accessibility(backyard, alley, etc.), number ofdevices per feeder, number ofcustomers per feeder, the amount ofnew data to be collected and mostimportantly, the number of GIS dataerrors detected that must bedocumented. An overhead walk-downusually has minimum safety concernssince the walk-down staff is notrequired to make contact withelectrical equipment.

UndergroundA walk-down of undergroundconstruction is much more complexand expensive. Unlike overhead,underground construction requiresinspection teams to open enclosures,man-holes and vaults. This increasesthe complexity, safety concerns andcrew skill requirements. These issuesusually result in the need forsupplementary crews and equipmentsupport to meet safely requirements.The result is increased costs andreduced productivity. An allocationshould be provided to cover overheadand transportation costs.

Factors that affect productivity arebasically the same as for overhead withthe added complexity of exposing theunderground network for inspection.Again, these factors include the type of

of the electric network data contained inthe GIS. Utilities that implemented GISas an asset model frequently discoverthat the electric network data may not beelectrically connected creating completeelectric feeders as required by SmartGrid. The other issue of data quality isobvious -- with incorrect network feederdata, the Smart Grid is unable tocorrectly analyze and operate the electric network.

Solving both of these issues requiresrefreshing and correcting both themodel and the data contained in theGIS. However, as companies quicklylearn, conducting a GIS data refresh ismore complex than performing an

initial GIS data loading. For mostutilities, the initial data loading waseasier to estimate because all data hadto be validated and input into thesystem. With a GIS data refresh, all thedata must be reviewed and validated(typically called a “walk-down”) butonly new data and errors must beupdated ("data posting"). Because manyfactors can affect the costs of a refresheffort, the purpose of this paper is toreview and discuss GIS refreshstrategies, processes and considerations.

Feeder Walk-DownThe feeder walk-down is the process ofan operator physically walking eachfeeder starting at the substation and

tracing it to the end of each branch.During the walk-down, the operatorvalidates all current GIS data andcollects any missing data. In addition to correcting the current GIS dataelements, if Smart Grid initiativesrequire data that is not currentlymaintained in the GIS, the operatormust update the GIS data model andcollect the additional data during thewalk-down. This is usually done byprinting paper feeder maps from the GIS and marking up the maps withdiscrepancies as the operator walks the feeder.

The data to be captured or updateddepends upon the utility’s datarequirements. However, it typically

includes electrical connectivity, circuitphasing, electrical asset information, sizes,ratings, and conductor configurations.

Two major factors impact walk-downcosts: the labor rates of the walk-downfield operators and their productivity.While the labor rates can usually beeasily determined, field crew productivitycan be impacted by a number of factorswhich vary between overhead andunderground construction. We will lookat each type of walk-down separatelybecause they vary significantly.

OverheadThe easiest and least costly walk-down isof overhead construction. These feeders

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Energy, Utilities and Chemicals the way we see it

electrical construction, the number offeeders per right-of-way, feederaccessibility (vault, man-hole, etc.),number of devices per feeder, number ofcustomers per feeder, the amount of newdata to be collected and most importantly,the number of GIS data errors detectedthat must be documented.

Data PostingFollowing the walk-down and thedocumentation of the GIS data to beupdated, the next major step in a GISrefresh is the posting of the documentedupdate data to the GIS. This process istime-consuming but relativelystraightforward. Many companiesoutsource the data-posting process,usually to an off-shore firm to benefitfrom the reduced labor costs.

The first step is to transfer the dataupdates to the off-shore vendor. If thedata updates were captured on papermaps, there are two options totransferring the data: the maps can becopied and mailed or they can bescanned and electronically transferred.

The data-posting vendor’s job and cost is determined by three factors: thenumber of updates to be posted, theproductivity of the GIS software toolsand the business processes imposed bythe utility. It is assumed that the vendor’sstaff is skilled and trained on theappropriate GIS system.

The vendor normally uses the followingfactors to estimate the total cost fordata posting: the number of feeders, the number of conductor segments (not conductor miles), the number ofpieces of electrical equipment, thenumber of customers per feeder, thenumber of breaks or jumpers, and theestimate of the percentage of errors thatwill require posting.

By totaling the number of facilities on afeeder and assuming that 30 percent offeeder data requires updating, you cancalculate the number of updates. If youassume three to five minutes per

Conducting a GIS Data Refresh—The Foundation of Your Smart Grid Program 3

change, which is dependent upon theGIS system tools available and thebusiness process of the utility, you candetermine the cost per update and perfeeder. You must also provide for theQA/QC processes, which will addadditional time and costs per feeder.

First Steps – Forecasting ModelThe first steps in any GIS refresh are toestimate the cost of the data refresh,forecast the expected benefits anddevelop the business case. To estimatethe effort and cost of a GIS refresh, it isnecessary to build a cost forecast model.This is accomplished by first performingan analysis of the GIS data for the feedersto be refreshed. Any new data

requirements must be accounted for andincluded in the GIS model.

The initial analysis should capture asmuch data as possible about thefeeders 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 dataand characteristics has been performed, a sample set of representative feedersshould be selected for a pilot walk-down.The goals of the pilot are to capturemetrics data on crew performance andthe time required to walk-down thefeeder, as well as to capture a sample of update metrics.

Walk-down ForecastFollowing the pilot walk-down, theteam should conduct a statisticalcorrelation analysis to determine which feeder characteristic variables are significant and contribute most toestimating the cost and effort requiredto walk-down all the feeders selectedfor refresh. A separate analysis willneed to be conducted for overhead and underground feeders since the

characteristics and crew requirementsare so different. The correlation analysiscan be conducted using one of manydifferent tools available on the marketincluding Microsoft Excel. The resultsof the statistical correlation analysis will

enable you to construct a forecastmodel using the significant feedercharacteristics to forecast the time andcost to walk-down all the feeders withsimilar characteristics. Since the pilotsample size is limited, the forecastmodel can be improved as more feedermetrics are captured.

Posting ForecastThe approach for developing aforecasting model for data posting issimilar to the walk-down model but thevariables are different. The two factorsthat most impact the posting are thenumber of data updates required and theproductivity of the operator doing theposting. The number of updates requireddepends on the number of errors in theexisting GIS feeder data and the amountof new data that must be posted. Thepilot walk-down should give you someidea of the error rates as a percentage offacilities on a feeder. The amount of newdata can be determined from the datarequirements and from the pilot walk-down metrics. This data then can bestatistically analyzed to develop aforecasting model. The operatorproductivity is based on several factors.The most significant are the GIS systembeing used and the software toolsprovided with the system. In addition,the business process imposed by theutility can have significant impact. Thebusiness process can range from simpleediting (fastest) to a complex asset-management work order process (muchslower). And finally, the individual skilland talent of the operator comes intoplay as well. However, with this factorthe operator tends to increase in skill asthe job progresses. As always, thequality-control process must be addedinto the overall posting process.

The pilot sample will allow you or thevendor to capture metrics on postingproductivity and cost. This data can thenbe used to develop a forecasting model.If the utility used an off-shore dataconversion vendor, the vendor will havea great deal of experience in forecastingthe effort and cost.

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SummaryFor most utilities, the key source of bothgrid asset and feeder connectivity is theirGIS. Analysis of systems across NorthAmerica and throughout the world hasshown that while most utilities haveinvested significant effort and cost increating their GIS, their data quality hasbeen in steady decline due to lack ofbusiness emphasis and ineffectivesupporting business processes. Inaddition to data deterioration, the SmartGrid may require additional data notcurrently modeled in the GIS. This newdata capture could be significant if thecurrent GIS data model is inadequate.

In fact, depending upon the state of datacorruption and the number of new dataelements, the GIS data refresh projectcan be a significant undertaking. That’swhy the utility should assemble anexperienced project team, withexperience in GIS and GIS data capture.

Remember, one reason for the GISdata refresh is poor data quality.Assuming you invested in good datawhen the GIS system was initiallyinstalled and the data quality has beendeteriorating, you will need to protectyour refresh investment withimproved business processes. Thebottom line? The walk-down is onlyas good as the business processes thatkeep the data maintained.

Gord ReynoldsPractice LeaderSmart Energy Servicesgord.reynolds@capgemini.com+1 416.732.2200