Asset Management Deployment Scenario

As recovery of money spent on asset related operations is not guaranteed, it is critical that asset related costs be managed wisely. Inevitably this leads to a need for analytic applications including:

· Asset Risk Management – what is the risk associated with operating an asset

· Calculation and optimization of asset lifecycle costs

· Calculation of asset availability – what is the projected reliability of an asset

· Asset replacement calculations – when to replace a given asset

· Asset maintenance and diagnosis – when to optimally schedule asset maintenance

· Asset performance – what is the value of an asset

· Capital plan management –future asset related investment forecasting

· Asset utilization analysis – how can assets be more fully used

· Financial analysis – how markets affect asset valuation[21]

This section describes how a deployment of the CIM and GID can be used to create a platform for data warehousing. In this case, we consider a complementary project to the application integration project with apparently different goals. The project consists of substation asset data analysis and integrates the following data:

o Asset/Equipment data

o Historical measurements

o Power system network models

Frequently, this type of data is in a database. With regard to what data is exchanged, one can suggest that the value to the utility of how a database natively models is low. Again, analysis applications may only need to browse the lineage of data (where it came from) for auditing/validation but not detail about native semantics. Consequently, data integration provides a scenario where the semantics of each database may be assimilated into the common model.

With regard to how data is exchanged, generally IntelliGrid Architecture based data integration seeks to abstract data access technology from the underlying storage technology. Specifically, instead of using a cross industry data access API such as ODBC to collect data, IntelliGrid Architecture based data integration employs a CIM enabled such as IEC61970 Generic Data Access interfaces which is independent of backend schemas and storage technology. Note that both cross industry and common model enabled data access API’s are generic in that they can be applied to any data type and do not hard code applications specific semantics into the API. This architecture is illustrated in Figure 18.

Figure ‑18 CIM/GID Based Data Warehouse With Message Bus

As described previously, it is important to note that GDA includes the ability to notify clients when data has been updated in the server. This functionality provides an important piece of the puzzle when constructing an infrastructure that enables a single point of update for model changes. For example, changes in an EMS modeling server can be used to drive the configuration of an archive or implement a synchronization routine with an asset management system.

The capability for the warehouse to be kept in sync via GDA Model Change Events addresses a key interoperability issue. There is no widely used cross vendor/open standard interface for the propagation of data changes into data warehouses. Furthermore, reuse of these events to keep applications in sync for the application integration can provide a significant savings when integrating the utility.

By sharing a common CIM/GID design framework, a message based application integration and data warehouse solution can be built simultaneously. Fortunately this approach reuses shared application wrappers to leverage the investment in each without requiring all data to be copied to a data warehouse. Separately, the cost of developing individual wrappers for data warehousing and for application integration can be prohibitive. By exposing application wrappers directly via CIM/GID without requiring an intervening copy of all the data in a warehouse, flexibility is maximized while costs are minimized.

Not necessarily copying all the data into a data warehouse while still providing analysis application the appearance that all the data is local is called “Virtual Data Warehousing”. A Virtual Data Warehouse enables distributed access to disparate, remote data sources with the ability to run federated queries across such sources. To meet emerging business requirements, data warehouses need to support lower data latency, reduce storage of rarely used data, and allow access to remote structured and unstructured data sources. The solution to these demands lies in the federation aspect of information integration. Federation makes it possible to avoid bringing all the data together by maintaining a logical view of a single warehouse. That doesn’t mean that data is never duplicated centrally, only that duplication is minimized and not stored in a warehouse optimized for a particular asset analysis application. The diagram below illustrates the asset analysis project components.

Figure ‑19 Asset Management Integration Example

In this diagram, a collection of databases including Asset Management System (AMS), Outage Management System (OMS), Work Management System (WMS), and others are integrated using the GID Server. The databases are aggregated with power system modeling data supplied via the message bus. The databases are tied directly to the Virtual Data Warehouse and not to the message bus for performance. By avoiding the XML messaging required by the message bus and only using the binary interface-to-interface remote procedure calls, query performance of the analysis applications is maximized. This architecture highlights one of the advantages of using a transport neutral interface such as GDA. In this architecture, links are optimized to meet project goals while still enabling a single standard off-the-shelf wrapper for applications. Application vendors can supply a single standard wrapper for data warehousing and message based application integration.

For example, an off the shelf Condition Based Monitoring Application can connect directly into the CIM/GID integration infrastructure using the GID interfaces. Periodically, this application examines current transformer loading and temperatures and after running calculations, publishes results on to the bus. The Condition Based Monitoring Application obtains required asset and power system information about equipment from the GID server. Figure 20 depicts the combined system.

Figure ‑20 Combined Application Integration And Data Integration Architecture

Figure 21 illustrates the specific GID interfaces required to integrate the applications and databases involved:

Figure ‑21 GID Interfaces Used to Integrate Applications and Data

Using CIM/GID application vendors can “shrink wrap” a CIM/GID compliant wrapper, the use of the CIM and GID can lower the cost of integration to utilities by fostering the market for off-the-shelf connectors supplied by application vendors or 3rd parties. The time and money associated with data warehousing/application integration wrapper development and maintenance is high. Typically, most money spent on integration is spent on the wrappers. An off-the-shelf CIM/GID wrapper can replace the custom-built “Extraction and Transformation” steps of an ETL process. The availability of off-the-shelf CIM/GID compliant wrappers is a key to lowering data warehouse construction costs very significantly.

It is clear that utilities are under greater pressure to simultaneously lower costs while at the same increase reliability and meet shareholder expectations. As a capital-intensive industry, attention naturally focuses on optimization of assets. Effective and efficient use of assets implies minimizing the total cost of ownership, i.e. minimizing the purchase, installation, operation, and de-commission cost of assets. More fundamentally this means utilities must more effectively manage:

· Asset operations

· Work management operations related to asset life cycles

While “return on investment” (ROI) is a more commonly known metric for the value of an investment, “return on assets” (ROA) more accurately represents the value of asset related activity and expenditures. ROA includes the actual return from cost savings, increased asset utilization and productivity.

If ROA provides a concise metric, physical variations in, as well as the geographic and organizational distribution of assets, make it difficult to effectively manage assets or establish a uniform ROA calculation method. In order to glean meaningful information so that intelligent decisions and metrics can be derived from the mass of data, analysis applications must be constructed. Thus, key to increasing ROA is analysis of asset and work management operations. This paper discusses how a utility may increase ROA via the deployment of a platform for asset related analysis applications. This platform depends on the use of standards and off-the-shelf EPRI applications. Specifically, the proposed solution describes how international standards such as the EPRI/IEC Common Information Model (CIM) and Generic Interface Definition (GID) can be combined with off the shelf analysis applications to create a utility asset analysis platform designed to maximize ROA.