Are GIS and EAM Systems Smart Grid-ready?

By Jeff Hanna and Paul Yarka, Accenture

As electric utilities around the world continue to pilot and implement smart grids, many are facing a pivotal question that will shape their approaches and impact the speed at which they can progress: Are our systems and databases that enable transmission and distribution (T&D) business operations smart grid-ready? More specifically, are the geographical information systems (GIS) and enterprise asset management (EAM) systems that underpin and support network operations sufficiently integrated to support and enable effective smart grid operations?

Key Considerations

This is a complex and challenging question. It raises a wide array of considerations, including a number of core operations-enabling systems, associated data and integration issues that more and more utilities are now encountering in their smart grid programs. Our experience with these projects has highlighted at least seven considerations that are especially relevant in determining smart grid readiness. These are:

  • 100 percent network model connectivity: Today’s utilities need to think a little differently about their data. It is important that an organization now has an as-built network model connectivity of nearly 100 percent, such that a fully connected model ties transmission to substation to distribution primary and distribution secondary accurately. Connectivity models of the smart grid must encompass a complete system.
  • 100 percent phase and engineering data: In addition, phase information and engineering data, such as wire size, impedance, cable lengths, configuration and other information, must be available. Transitioning power system calculations, such as volt/VAR optimization, away from engineering tools to real-time systems requires that more network engineering details are captured as part of the as-built network. This poses a challenge to the traditional GIS model when determining where and how device control settings get captured.
  • Comprehensive asset register: It is vital to have a comprehensive asset register containing not only the information needed to support technical maintenance and inspection but also data that are required to integrate with other asset systems. The asset register should contain an inventory of all assets requiring maintenance with attribute data that supports differing care regimes, as needed. It is important that all technical maintenance policies and strategies leverage the asset register as the system of record for the asset to be maintained and inspected.
  • Network and maintenance model for smart sensors and equipment: This is a common gap for utilities—how to add or relate sensors and intelligent electronic devices (IEDs) with other assets requiring maintenance. A variety of decisions will need to be made to properly maintain sensors and IEDs (i.e., changing batteries, upgrading firmware). Where to store and maintain smart meters is a common industry question. The ultimate system of record for utility maintenance is the enterprise asset management (EAM) system and a preferred asset data location is the asset register. Typical questions include: Where should the power system model and maintenance model entities for smart sensors and smart equipment be stored? What attributes can be stored? And what do the processes for creating and maintaining that data look like?
  • Synchronized, integrated asset information and comprehensive asset performance data: Has the organization synchronized and integrated its asset information? This is important when integrating high latency asset data with real-time data for condition-based monitoring and maintenance and asset analytics. Synchronization of asset register data with spatial asset data is required. Having synchronized its asset information, the utility is now able to create new assets once, replicate them using automation, and then use the composite asset data for diverse applications across the utility.
  • Synchronized linkage to temporal, historical and real-time data: Has the utility developed the ability to link assets in their databases that support T&D operations to temporal, historical, and real-time data in the smart grid operational data store, originating from devices such as remote terminal units (RTUs), network sensors, IEDs and smart equipment?
  • Consistently sharing an as-built network model across the enterprise: Finally, a common consideration is whether the utility has assembled a complete and authoritative as-built power system model, and then shared the model with consuming applications, such as a distribution management system (DMS), outage management system (OMS), a network analysis/system planning tool and other applications that require a transformed, as-built network model as an input.

Multi-dimensional Asset Intelligence

Although this may appear to be a daunting list of requirements, multi-dimensional asset intelligence provides one of the keys to meeting the afore-mentioned requirements. The integration of electric utility assets across GIS and EAM systems is a powerful enabler of smart grid readiness, and the foundation for a multi-dimensional asset intelligence capability.

Optimal EAM and GIS integration consists of the following five key elements:

  • A shared asset data model with data ownership and systems of record for each asset and attribute.
  • Integrated workflows and business processes to manage the creation, update and viewing of asset data across EAM and GIS.
  • A capability for administering the asset, attribute and relationship mapping across EAM and GIS, along with process-enabling configuration in support of the shared asset data model.
  • Application integration components to automate, validate and maintain the integration of asset data between EAM and GIS
  • Composite EAM and GIS services for reporting, analysis and enabling other applications.

With these elements in place, EAM–GIS integration allows asset information to be leveraged across several desired dimensions—spatial, technical, financial, real-time and performance. Unfortunately, our experience suggests that most organizations have yet to properly solve the challenge of asset-level data integration.

Asset Lifecycle Business Process Options

To achieve the required degree of integration, a utility can choose from multiple business process options (see Figure 1). The option at the top left is an enterprise resource planning- (ERP-) centric model, where a utility creates new assets in conjunction with the design/estimating process, the as-built process, and through the maintenance and inspection process. In this case the utility’s EAM system is a system of record for new assets, and the GIS learns of a new asset subsequent to its creation in EAM- which is frequently integrated with a broader ERP environment.

The GIS-centric model at the top right in Figure 1 works quite differently, and involves a different approach for process-enabling integration. With ERP-based EAM systems, this approach may prove difficult, as the ability to trigger asset creation in the EAM system from an external system may not be supported. Assets are introduced solely through the design/estimating and as-built processes. The GIS is the system of record for new assets, and the EAM system is made aware of assets requiring technical maintenance as they are introduced to it.

The third option, bottom left in Figure 1, illustrates an integrated approach. Procurement, design/estimating, as-built, and maintenance and inspection processes are involved in creating assets, using a balanced, integrated approach across EAM and GIS. The system of record can be either EAM or GIS depending on the business process involved, the individual asset class and specific asset attributes. As the vertical arrow between EAM and GIS indicates, information is also exchanged in an automated, synchronized manner between the two core enabling systems.

The choice between these workflow options is not always clear-cut. While one of the models might fit a particular utility, it is possible that none of them will do so. Alternatively, a hybrid version might be required. We focus on helping utilities solve this conundrum by using roughly twenty configurable, pre-built EAM-GIS integration components. Throughout, the key objective is to assemble and configure the “puzzle pieces in a way that meets the utility’s business process requirements and enables flexible integration between EAM and GIS. Flexibility is extremely important—and we believe it will become even more so as smart grid-driven process change accelerates.

EAM–GIS Integration through the Asset Management Lifecycle

Integration of EAM and GIS starts with the core asset business processes, and then extends outward to encompass enhanced asset information, applications and analytics. From the perspective of the asset management lifecycle, it is clear that both EAM and GIS play critical roles across the design, build, operate, maintain, and retire stages. Figure 2 illustrates these roles at various points in the lifecycle. It also shows that, while EAM and GIS progress along the lifecycle against the same assets or sets of assets, they fill slightly different purposes and provide differing perspectives at each point.

During the design stage, EAM supports estimate creation based on the materials, resources and costs involved in a particular network design. EAM and ERP may then support procurement of the required materials. In contrast, the role of GIS during the design stage is to plan and lay out the work from a spatial network perspective. Moving on to the build phase, EAM supports the scheduling of the work and its management during field construction. Once construction is completed, the EAM asset register entries are created, along with the equipment records and the financial information that is associated with the task. GIS is used to produce the construction drawings for the field, and then is used to determine whether the information held in the GIS reflects what was actually built.

During the operate phase, EAM supports activities including maintenance, inspections, surveys, repairs and replacements, while the GIS can be used to produce inspection and survey maps and routes and is the system of record for maintaining the as-built record of the network. At the retire stage, EAM will be used to deactivate or archive the asset at the end of its life, whereas the GIS will be employed to abandon or remove the retired asset.

From the diagram, it is clear that the integration between EAM and GIS operates through the lifecycle, as indicated by connecting yellow bars between them. Exceptions occur during the design and build phases, where EAM and GIS both maintain separate views of the assets. The point of as-built, when the assets become real is the trigger for full integration. At this point the assets exist in the field, are on the network and energized, and enter the maintenance and operation cycle. This enables the information to be linked and synchronized between EAM and GIS.

As utilities approach the smart grid era, they know that an accurate and consistent view of network infrastructure is a foundational underpinning of any effective smart grid. They also understand that, in the future, new transformation drivers will emerge requiring flexible asset-level integration that can be adapted to support ongoing and incremental process change. Both imperatives demand deep and agile integration of a utility’s GIS and EAM systems, as they are used to support an evolving asset management lifecycle. In Accenture’s view, such a capability will differentiate and drive the high performers in the smart grid-enabled utility industry of tomorrow.

Paul Yarka is the global lead for Accenture Smart Grid Services’ T&D asset management practice. Jeff Hanna is a senior manager, T&D asset management, with Accenture Smart Grid Services.

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