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GIS-Based Design for Effective Smart Grid Strategies

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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.

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GIS-Based Design for Effective Smart Grid Strategies

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. GIS-Based Design for Effective Smart Grid Strategies 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
  5. 5. GIS-Based Design for effective Smart Grid Strategies
  6. 6. 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
  7. 7. GIS-Based Design for Effective Smart Grid Strategies 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
  8. 8. GIS-Based Design for Effective Smart Grid Strategies - 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
  9. 9. GIS-Based Design for Effective Smart Grid Strategies 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
  10. 10. GIS-Based Design for Effective Smart Grid Strategies 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
  11. 11. GIS-Based Design for Effective Smart Grid Strategies 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
  12. 12. GIS-Based Design for Effective Smart Grid Strategies 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
  13. 13. GIS-Based Design for Effective Smart Grid Strategies 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
  14. 14. GIS-Based Design for Effective Smart Grid Strategies 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
  15. 15. ©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

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