Between the Poles

Smart Grid: A View from the Inside "It's All about Data"

I’ve invited Kevin Miller, who has many years experience in the electric power utility industry and who now works for Autodesk advising utilities on design information management challenges and solutions, to give his perspective on what the smart grid will mean for electric power utilities.

I have worked in the utility industry for the past 24 years.  I worked at a major electric utility in several capacities in the Transmission & Distribution organization, but for the majority of the time, I was involved in the implementation and support of Distribution systems including GIS, Outage Management System (OMS), Work Management, and Design.  During this time I have seen the concept of smart grid develop and take shape.

How we design, operate and maintain today’s electrical grid  for the most part hasn’t changed in the past 75 years.  Over the years, devices and equipment have evolved slowly (the level of R&D investment in the electric power industry is one of the lowest of any major industry),  but there really haven’t been major changes in how the grid functions.  Of course there was a massive investment in building out the grid to provide universal electrical power under the impetus, for example, of the Rural Electrification Act, which provided federal funds for installation of electric power in rural areas of the United States. 

But, in the 90’s, there was a major change when deregulation and decoupling deflected capital investment from the grid.  The “gold-plate” that had been lavished on the grid in the proceeding decades now was used to finance preparations for freer markets with the result that the technical evolution of the grid stagnated.  In the last two years, stimulus monies, green initiatives, and energy conservation are combining to create significant pressures to quickly catch up, to make the grid smarter to address today’s pressing problems, including reliability, security, customer empowerment, and global climate change.

The systems used to design, capture, maintain, and analyze the grid in use today are partial automations of 75 year old procedures.  The business processes for the design, construction, operation, and maintenance of the grid are carried out by different teams within utilities, for example, engineering design, construction, records, outage management, operations, and billing. Over the years the different functional teams have evolved procedures that support their own specific missions and informational and operational needs.  What has been missing is a holistic view of the entire business process for managing the life cycle of network assets.  Individual teams optimize their own sub-processes, but the optimized sub-processes do not take into consideration the “production and capture” business process  to capture and manage the digital data required to operate and maintain the grid.  The focus on sub-processes and not looking at the bigger picture of the overall business processes and information flows has resulted in enterprise data bases with stale, incomplete, and error laden data and network models.

In the past with the existing grid, we have been able to scrape by with out-of-date and inaccurate data.  Smart grid changes all that.  From a data perspective, smart grid is ultimately just much, much more data, much of it real time.  Getting the value (operational efficiencies and improved operating metrics such as fewer and shorter outages) out of smart grid investment relies on being able to utilize the digital data collected from smart devices to monitor, analyze and simulate the electrical grid in real time.  If your current digital model of your company's electric power network is based on inaccurate, out-of-date, and incomplete data because your business processes for managing the information flow across the organization are archaic and inefficient, it is going to get much, much worse with the smart grid. 

Systems optimized over the years at the sub-process level may have appropriate technology to utilize and analyze network data, but I find that utilities fall down significantly in their ability to “produce and capture” accurate and timely information to feed these systems. I am continually surprised at how bad a job utilities are doing at maintaining their network facilities data.  When companies are inundated by a sea of unreliable information and experience the difficulty in making operational decisions based on this information, they will quickly realize that they have to fix their business processes’ “produce and capture” problems.  But I expect that when that realization happens it will be too late.

Utilities need to address these looming problems now.   It is critical for forward looking organizations to assess the quality of their network facilities data and review their business processes from the perspective of operational efficiency, optimizing business processes and information flows for data quality, and making sure that they have the appropriate supporting technology.  Redundant data and inefficient data and work hand-offs are prime symptoms of an organization focusing on sub-process optimization and ignoring the big picture.  Reviewing your overall business process from soup to nuts with a perspective above the sub-processes (team level) is critical.  As well it is essential to develop a technology architecture that enables automating the overall business process in addition to supporting and optimizing the productivity of each operational team.  

Autodesk University Virtual

If you're stuck home in bed with the flu Dec 1-3 or your travel budget has been cut and you can't make it to Las Vegas, there is a way you can still keep up with AU classes and sessions via Autodesk University Virtual.  50+ classes and sessions will be streamed live.

You can find out more about AU Virtual here and see what is available on AU Virtual compared to being there live.

Practical Infrastructure Management Solutions for Utilities

I just came across an article in Electric Light and Power that describes several practical applications that utilities have deployed with little fanfare for designing new infrastructure, managing network assets, or planning improvements for grid modernization.  Some of the challenges that these solutions help address are minimizing backlogs, providing a unified view of infrastructure data, empowering the field force, improving design productivity, incorporating intelligent design data into workflows for business processes such as outage management and maintenance, and developing and maintaining digital network models, all of which are going to be essential for the coming smart grid.

GIS Analysis for the AutoCAD User

People who are familiar with AutoCAD know that AutoCAD Map 3D extends AutoCAD to support

• Geographic coordinate systems
• Support for geospatial vector and raster data
• Drawing cleanup such as duplicates, undershoots, and overshoots
• Creating thematic maps
• Accessing geospatial databases using FDO including ArcGIS, Oracle Spatial, and MySQL
• Surface rendering
• Publishing spatial to the Internet or creating map books
• Automating workflows

But many people may not be aware of AutoCAD Map 3D's GIS analysis capabilities. Michael Schlosser has just published an article in Cadalyst that explains how you can perform GIS analysis using AutoCAD Map 3D and introduces some of Map 3D's geospatial analysis capabilities including queries, thematic mapping, buffer analysis, topology analysis including network and overlay analyses, and 3D surfaces. 

FDO Toolbox v0.7.7 Released

The FDO Toolbox is a multi-purpose .Net geospatial tool to manage spatial data. It is written in C# and uses the Feature Data Objects (FDO) API.

A new version of the FDO Toolbox has been released.  This version includes custom file drag and drop handler support and supports FDO 3.4.0.   Jackie Ng recommends that you get this release if you use the bulk copy feature, as this release fixes some major defects.

Updated Tool Released for Migrating MapGuide 6.5 to MapGuide Enterprise

A new version of the MapGuide 6.5 Data Migration Tool including documentation has been released by Autodesk. The tool now supports MapGuide Enterprise 2008, 2009 and 2010.  You can use the Autodesk MapGuide Data Migration Tool to migrate your MapGuide 6.5 data files including MWF, MWX, UDL, MLF, and SMB (Symbol Library) to Autodesk MapGuide Enterprise.  You can find the tool and documentation here.

An ROI Analysis Comparing AutoCAD and AutoCAD Map 3D

There is an worthwhile article in Cadalyst by Marcia Carillo which reports on a return on investment (ROI) analysis comparing AutoCAD, a pure CAD platform, to AutoCAD Map 3D, a data-management and interoperability platform.

To do this you need to understand the workflow for which the new software will be used. The costs that Marcia considers are

• Training cost
• Software cost (cross-grade)
• Labour costs
• Effort (hours per month)
• Time required for training
• Productivity loss during training

Some workflows that Marcia identifies that Map 3D can assist with are

• Working with coordinate systems
• Working with vector and raster data such as ESRI, digital orthophoto quarter-quadrangle (DOQQ), digital raster graphics (DRG), Web Map Service (WMS), and Web Feature Service (WFS)
• Drawing cleanup such as duplicates, undershoots, and overshoots
• Creating thematic maps
• Managing databases or linking to databases
• 3D rendering of survey data
• Multiple users accessing or editing one DWG file at the same time
• Publishing data and maps to the Internet or creating map books

To do an ROI analysis for a typcal workflow, Marcia looked at basic workflows including data input, data management, data analysis, and publication of deliverables. She then aggregates the results and concludes that it is fairly easy to save a  bit more than two person-years (3,800 hours) over a five-year period. Assuming an average labour average cost of $70 per hour, this means moving from AutoCAD to AutoCAD Map 3D can result in savings of $266,000 per seat over five years.

Engineering Data to GIS and Back

Most of the world's vector spatial data is captured using CAD desktop applications, whether we are talking about engineering design data or traditional geospatial data.  Over the past 20 or so years most of the utility and telecommunications enegineering design data for outside facilities have been converted to electronic form, either by the utilities and telcos themselves or more often by conversion vendors, who invariably use CAD desktop applications to capture network facilities data from paper drawings.

Utilities and telecommunications have been facing problems arising from the inefficient flow of engineering design data from designers to the construction subcontractors to records (outside of North America referred to as network documentation) and between records and operations.  The problems that can be attributed to what is really a pathological paper-based information flow include poor data quality, redundant data, inefficient field operations, and others.  I've blogged several times about this in the past.  In many utilities and telcos the records department redigitizes from paper as-builts the same data that was created in electronic form by engineering.  This is the source of a number of problems including as-built backlogs, data errors, and the inefficient use of valuable staff.

At the WAURISA conference this week Brad Hofman of Snohomish County and Dusty Gallinger of The PPI Group gave an outstanding presentation that addressed the issue of repurposing intelligent engineering design data for GIS, asset management, and other downstream uses.  

PPI is developing a toolkit, based on AutoCAD Map 3D and FDO, called the Public Works Toolkit (PWT) which is designed to help Snohomish County as part of their day-to-day workflow repurpose their engineering design data and avoiding redigitizing from paper. The PWT creates an AutoCAD-based environment where design data can be classified as striping, easements catch basins, signs, and so on.  This enables Snohomish County to get a lot more value from their engineering design data by repurposing it for downstream uses such as GASB, NPDES, machine control, mobility-CRAB, bridgework - DOT, enterprise GIS, and asset maintenance and management.  Brad sees the PWT as having broad applicability including State of Washington Public Works Departments, other county Public Works Departments, the Washington County Road Administration Board, DOTs, consultants, and developers.

I think Brad really hit the nail on the head when he said "It’s amazing to me after all these years that it isn’t generally understood that the data we produce for civil engineering construction plans aren’t just plot files to drive a printer, but are actually rich datasets of survey grade vector geometry, and objects that can be densely attributed with location, materials, maintenance and other data."

There were many questions from the audience relating to the availability of the PWT.  Dusty responded that the PWT is a work in progress, but that PPI is planning to host a PWT workshop in the summer for people who are interested in finding out more about the PWT.

Viewing DWF Files with Firefox

This is pretty cool.  It's been possible to view DWF files with IE for some time. Now you can view them with Firefox.  You can find how to on Beyond the Paper.

Leveraging the value of CAD data

One of  the dimensions of the point of inflection which has been occurring in the geospatial world over the last year or two is that spatial is becoming increasing less special.  What this means in practice is that geospatial enabling is what most organizations are turning to as they include spatial data in their IT processes these days rather then traditional GIS.

A very good example of this is what is happening in the world of network infrastructure, which in practical terms refers to the power, telecommunications, water, waste water, cable, and other public utilities that you rely every day.

Managing Maintenance Costs

A rule of thumb for organizations responsible for maintaining network infrastructure is that maintenance represents 90% of the total cost of network infrastructure over its entire lifecycle from planning, through design and construction, operation and maintenance, to decommissioning.  An essential requirement for ensuring profitability is managing maintenance costs.  An important tool for managing costs is a digital model of network infrastructure.

Creating Network Infrastructure Data

Most of the world’s network facilities data has been and continues to be captured using CAD-based desktop applications.  For example, it is estimated that there are billions of CAD drawings files worldwide and the chief reason is that in most organizations the business process for building new network infrastructure relies on CAD to create the original engineering design.  Another reason is that that virtually all conversion vendors, the folks that convert paper to digital data, use CAD desktop tools for capturing network infrastructure data from paper-based construction drawings.

Leveraging Network Data throughout the Infrastructure Management Lifecyle

The business process by which network facilities data is captured and managed is comprised of several steps.  Engineering uses CAD-based tools to create construction drawings. Construction drawings are almost invariably paper drawings used by Construction in the field to build facilities.  Construction drawings are returned from Construction to Records as paper as-builts which are captured into a geospatial datastore, which in the past was a traditional GIS and which is the permanent database of record for network facilities.  This information is then used by Records to provide facilities maps, reports, and data to Sales, Marketing, Operations, Trouble Call, One-Call, regulatory agencies, and other internal and external consumers of network infrastructure data.

In most utilities and telecommunications firms, infrastructure data management processes are characterized by islands of information, Engineering (CAD), Records or Network Documentation (GIS), Construction, and Operations use their own specialized applications which are linked by paper-based information flows.  These processes are inefficient, slow, unresponsive, and expensive. 

Significant improvements in quality of service and reduction in cost are feasible by breaking down these silos of information to streamline the flow of information by leveraging the original CAD data throughout the infrastructure management lifecycle.

Data Quality

Inaccurate and out-of-date data is expensive.  It means missed business opportunities, loss of productivity, and higher costs.  Data quality in many utilities and telecommunications firms is poor and is an important factor in increasing for example the “cost per access line”, an important metric monitored by telecommunications firms.  There is a direct relationship between poor data quality and a high rate of returns or repeats.  Repeats refer to service requests that require more than visit.  In many utilities and telecommunications firms repeats comprise 25-30% of service requests.

Intelligent Model-based Network Facilities Database

A key requirement for managing network infrastructure efficiently and reducing maintenance costs is an intelligent, model-based database of the network facilities which represents a single point of truth accessible across the organization.  The data model of an intelligent facilities database includes location, class or type of equipment, properties, and relationships to other facilities for every element of plant in the network.

CAD/Geospatial Value Chain

The route from a paper drawing to an intelligent infrastructure management system involves several steps, and can be perhaps most easily understood in terms of a CAD/Geospatial value chain.

Level 0

The original record of network infrastructure in most utilities and telecommunications is paper drawings.  A large telecommunications firm will have millions of paper drawings.

Level 1

The next step toward automating infrastructure data management is CAD, which revolutionized the creation of construction drawings.  Traditonally CAD was focussed on producing paper construction drawings.

Level 2

To enable the preparation of maps on a geographic landbase, geospatial capabilities were added to desktop CAD applications, such as geographic coordinate systems, so that the location of facilities could be placed on the earth's surface.

Level 3

The next major step forward in automating the management of network infrastructure involved the concept of class.  Class refers to type of equipment.  For example, in a power network pieces of network plant can be classified as primary conductors, secondary conductors, transformers, fuses, and switches.  Each class of object typically has a set of properties which describe each object, such as input voltage, output voltage, manufacturer, year installed, etc.

Level 4

One of the most important advances in IT in recent years has been the extension of relational database management systems (RDBMS) to include spatial data types and spatial indexes.  Virtually all of the widely-used RDBMSs in use today are spatially-enabled.  These include Oracle, PostGIS/PostgreSQL, MySQL, Informix, and DB2. An important advantage of storing spatial data in a spatially-enabled RDBMS is that it provides open, standards-based access based on SQL, ODBC, and JDBC.  All of the major geospatial vendors support one or more spatially-enabled RDBMSs.

Level 5

The final stage in creating an intelligent database and infrastructure management system involves  support for relationships and constraints.  Relationships include connectivity relationships, such as connecting a primary conductor to a transformer.  Other types of relationships include those between structural elements such as poles or manholes and equipment that actually carries electricity such as primary and secondary conductors and transformers.

Relationships are important because they enable traces, for example in the case of a power network, a downstream trace to identify customers affected by a disabled transformer, or an upstream trace to identify the sub-station feeding the transformer.

Constraints are essential for data quality and prevent the user from entering bogus data.  Typical constraints are property constraints, for example, restricting voltages to those supported by the network, or restricting the year of installation to between 1950 and 2007.  Another important type of constraint restricts relationships between classes of objects.  For example, a primary conductor can be connected to a transformer or another primary conductor,  but not to a river.  Another example is requiring that a transformer has a single primary input and a single secondary output. 

Solution Architecture

Geospatial enabling means that  now the basic tools you need are a location-enabled CAD desktop, a geospatially-enabled RDBMS, an application for  enforcing constraints, managing metadata including stylization, and enabling traces, and a web mapping tool.  It is no longer necessary to implement a traditional GIS.  This approach simplifies and reduces the cost of the implementation, especially for engineering focused organizations familiar with CAD tools and wanting to avoid the cost of implementing a traditional GIS.