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March 6th, 2008
Spatially Enabling the Intelligent Grid, The Next Hollywood Blockbuster?

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powerlines.gifWhat do the movie Beowulf and the Las Vegas Valley Water District (LVVWD) have in common? They both make use of the sophisticated modeling capabilities of Autodesk software. Although LVVWD efforts may never turn into a blockbuster movie, better visualizing a network’s spatial characteristics—whether for water networks, gas pipelines or electric grids—can improve a utility’s understanding of its energy delivery assets. Spatially enabling electric transmission and distribution (T&D) networks contribute towards a more intelligent grid.

powerlines.gif What do the movie Beowulf and the Las Vegas Valley Water District (LVVWD) have in common? No, it’s not that millions of people turn to them—at least its city, in the case of LVVWD—for entertainment. Actually, Autodesk highlighted visualization applications for both LVVWD and the creators of Beowulf at its recent World Press Days 2008.

Although LVVWD efforts may never turn into a blockbuster movie, better visualizing a network’s spatial characteristics—whether for water networks, gas pipelines or electric grids—can improve a utility’s understanding of its energy delivery assets. In this article, we will take a closer look at spatially enabling electric transmission and distribution (T&D) networks and how this effort contributes towards a more intelligent grid.

Why Spatially Enabling the Intelligent Grid is Important

Many technologies out there already help utilities understand the grid spatially, particularly geographic information systems (GIS). Yet these technologies as they stand today cannot support the spatial needs of an intelligent grid.

To understand why, let’s first define the intelligent grid, then look at the basic reasons why utilities need to spatially consider their grid and determine where technologies fall short in supporting these needs.

What is an intelligent grid?

Many definitions are floating around today, but they all describe the vision of an electric T&D network that—through the use of information technology—is “smart” enough to predict and adjust to network changes. Therefore, an intelligent grid could recognize a potential problem, such as an abnormal operating condition, and communicate this problem to a decision maker (i.e., computer) that would automatically work to correct the problem.

Since utilities today do not have much visibility into their distribution networks, accomplishing this vision requires that utilities improve three basic technologies: communications networks, sensors and analytics. For example, say a utility has an outage on its distribution network. More network sensors—such as smart meters—collecting information means that utility can better pinpoint a problem’s location.

Communication networks installed along the distribution grid would then enable these sensors to communicate this problem to the utility. Improved analytics can efficiently process information and automate responses to the problem—such as dispatching the field crew closest to the area.

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There are many reasons that utilities want to develop such technologies. The above example reflects most basic driver of the intelligent grid: improving the grid’s reliability—particularly with the NERC mandatory reliability standards. Other basic drivers include the need to deal with customer demand that is outstripping the generation supply and a rapidly aging workforce.

Over the last year, however, intelligent grid drivers have definitely developed a green hue. Utilities are now emphasizing how the intelligent grid can help fight climate change. From the need to limit coal-powered generation and incorporate more distributed and renewable energy sources to grid-friendly appliances and plug-in electric hybrid vehicles, utilities are not just looking at how the intelligent grid can improve their reliability, but help them and their customers reduce their carbon footprint.
On top of these drivers, two other issues are pushing utilities to look at the intelligent grid in a spatial context. The issues include the distributed, yet connected nature of the grid and the types of decisions that utilities will make as they gain more intelligence about the grid.

The Grid is a Dispersed, Yet Interconnected Network of Components

The electric grid is a network of distributed assets and personnel that must connect and interact with one another. Looking at how these groups spatially relate to one another provides a way for utilities to understand their complex interactions. These components include :

•    Distributed assets: In order to deliver electricity, each asset on the grid—whether a transformer, meter or substation component—must cooperate with other assets throughout the system. Assets may be miles away from one another, but because of their connection, a problem with one asset can impact other assets upstream or downstream from it. Therefore, it is important to not only understand where a problem is occurring, but “connectedness” or how that problem could impact surrounding assets along the network.  

•    More intelligent grid assets: As the intelligent grid moves forward, utilities will install even more assets on the grid. From smart meters to distribution line sensors, these assets will not only provide more detail about the grid’s status, but also require utilities to maintain and better understand their condition. 

•    Distributed people: Along with distributed assets, utilities also have distributed personnel working on these assets. So utilities need to understand how personnel spatially interact with assets and other field crews. Understanding these spatial relationships can have important efficiency and safety implications for utilities. For example, crews need to know that they are indeed accessing the asset that has been disconnected from power and dispatch personnel need to understand which qualified crew is closest to an emergency job.

Decision-making Needs also Drive Spatial Technologies

Not only does the intelligent grid require utilities to install more assets, but utilities also need to collect more data about distributed assets, more efficiently analyze the data and make better decisions based on that analysis. At Energy Insights, we have identified two particular types of predictive decisions:

•    Very quick decisions (VeQuiDs): These types of decisions are made in milliseconds by computers and intelligent devices analyzing complex, real-time data. Yet this intelligent grid vision we talked about earlier is still a ways off for most utilities—especially in terms of widespread deployment.

•    Quick decisions (QuiDs): Most utilities do not have VeQuiD capabilities, but many proactive decisions about the grid don’t have to take place in just milliseconds. Many utilities today can make quick decisions (QuiDs) or decisions to adjust to network changes in a timeframe of months, days or minutes. Even though these decisions are not extremely quick, they still give utilities more intelligence about the grid and enable them to predict and correct network problems instead of just reacting when the grid fails.

No matter how quick the decision, all of these predictive efforts are based on the same thing: providing the decision-maker with access to good-quality data. Even though the goal of the intelligent grid is to automate more decisions about the grid, people will still be the primary QuiD-makers for years to come. In order to make these decisions, the intelligent grid will require many more personnel to have access to spatial information about the grid including:
•    Customer care representatives
•    Dispatch personnel
•    Managers
•    Executives
•    Field crews

Not only will more personnel need to view spatial information, but as less experienced people join the workforce, they will need easier ways to visualize and think about the grid. Given the spatial nature of the grid, spatially visualizing and analyzing data can paint an even clearer picture of the grid and its behavior. Understanding spatial relationships can help utilities pinpoint problems and take action more quickly. For example, the right side of Figure 1 shows a list of assets and their status. The left side of the figure shows the same assets in their spatial context. It easier to tell where problems are occurring—in this case the red assets indicate some problem—when decision-makers can view the assets spatially. 


Problems with Spatially Enabling the Intelligent Grid

Assets along the grid are already connected, but the real problem with spatially enabling the intelligent grid is connecting people and connecting technologies.

Disconnected spatial technologies
Traditionally, different spatial technologies reside in different departments throughout a utility. For example, a utility may have:
•    A GIS department that collects and tracks geospatial information for planning purposes
•    An engineering group that uses network analysis and design tools for making additions to the grid
•    A maintenance department that collects and stores asset information
•    A dispatch group that uses a separate mapping and routing system


As a result of different technologies sitting in different silos, utilities already collect spatial information, but just don’t make it readily available across the company (Figure 2). With this lack of connections, utilities cannot develop a rich picture of the grid and the interactions between different components—whether assets or field personnel. For example, vegetation inspections are often performed with helicopter flyovers. A system that allows the inspector in the helicopter to note problem vegetation anchored to a specific location would make deployment of work crews more efficient.

Disconnected people and technologies

As the intelligent grid moves forward, many people making QuiDs will not be GIS gurus. Not only is much of the spatial information buried in the different silos discussed above, but personnel throughout the company do not have the technical skills to read and access available information. Utilities cannot afford to train all employees on ArcGIS or AutoCAD, so they need a way to effectively present information to decision-makers.

Furthermore, making good QuiDs requires more than just access to information, but access to accurate, timely information. Utilities often experience delays with loading information into spatial databases and systems. For example, field crews may collect information and rely on other personnel to manually input the information into these systems.

How to Improve Spatial Understanding

Given these challenges, spatially enabling the intelligent grid does not mean that utilities must make substantial investments in new technology. Rather, utilities just need to better leverage existing technologies and personnel along with improving their decision-making processes.  

Realigning spatial technologies and applications
Spatial technologies—such as GIS and network analysis design programs—along with work and asset management programs already provide a layer of spatial information. To begin spatially enabling the intelligent grid, utilities need to realign these systems to provide a solid foundation of spatial data that personnel can access throughout the company (Figure 3). This does not necessarily mean that a utility must pull all of these systems into one database, but rather develop ways to begin connecting these systems—including more of an integration bus model or “mashup” of technologies.

For example, the California ISO uses a service-oriented architecture (SOA) solution to better display and analyze spatial information. These efforts include overlaying ESRI-based GIS data on satellite imagery and overlaying OSIsoft PI data on GIS. The system can visualize the grid, substations, generators, nuclear power plants and wind farms as well as “mash up” weather data and forecasted demand supply. Google Earth displays the real-time analytics.


Embedding Spatial Capabilities Into Everyday Applications

Once utilities make the spatial data more readily available, they need to consider ways to make the data more accessible for QuiDs about the grid. In order to make them more accessible, utilities need to embed spatial capabilities into everyday applications. For example, a utility may include a spatial view in their asset management application, such as what IBM’s Maximo product does today. It could also mean offering web-based access so management, executives and remote workers can readily access spatial information (Figure 3).

Improving real-time collection capabilities
Not only do utilities need to provide better access to spatial information, but they also need to ensure that personnel access up-to-date, accurate information. This may require utilities to provide field personnel with the ability directly update and correct spatial information. For example, handheld devices and laptops linked to spatial databases can provide ways for field personnel to directly input information into the system.

As the intelligent grid moves forward, intelligent grid sensors will play an increasingly important role in collecting and updating information. These sensors will not only bring in more real-time information, but also bring in more information about assets where it is not practical to manually collect the information—including meters and lower voltage distribution lines (Figure 3).

Getting people comfortable with technologies
All of these efforts are important for spatially enabling the intelligent grid, but since people are involved in the success of these efforts, utilities need to ensure that people will use these technologies. Utilities need to develop business processes that encourage people to look at spatial information when making decisions about the grid.
Beyond just business process change, although maps and other spatial displays are inherently more intuitive, utilities also must ensure that personnel are comfortable with seeing spatial information and understand the elements comprising a map.

Building stronger connections between spatial information sources will not only allow for better QuiDs today, but provide better access for more advanced analytics in the future. VeQuiDs will require substantial amounts of real-time information and the spatial components of that information will be important for advanced analytics to effectively predict and adjust to network changes.

H. Christine Richards

 is senior research analyst with 
Energy Insights (an IDC Company)
; e-mail: 
hrichards at

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