|
|
|
Enterprise Integration
|
Strategy for spatially integrated distribution information system in energy delivery utility mergers
Strategy for a spatial DIS model
It is obviously useful to have a geographically referenced data model to support
dispatch operations, for example, to determine the affected electrical equipment
and service area based on imprecise location information from a non-customer or
passerby telephone call, before a trouble crew is dispatched. More strategically
important, the spatial-oriented data integration can support customer-driven
engineering and operations planning. Such planning is particularly beneficial to
utility mergers as the combined company takes a strategic look at the diversified
service areas and customer bases of the merged utilities.
Figure 2. DIS Data Integration Facilitates Communication
Among Distribution Management Functions and Provides
for
Other Business Solutions in the Future.
In the deregulated and competitive environment, utilities must increasingly focus
on customers and costs. Decision making from planning to operations and
maintenance is no longer just to find the most economical solution to meet fixed
engineering criteria. Rather, it involves determining the customer value
expectations and finding the best business approach to meet the expectations.
For many years, distribution planners have used spatial land use analysis
techniques to forecast the energy demand of customers for each geographic
small area (Willis, 1993). Now, to ensure that the delivered service quality would
meet the customer needs, while keeping costs to a minimum, the utility needs to
know not just what the system wide expectations of service quality are, but also
where the expectations would be higher, and where they could be lower.
Expectations of energy service reliability and quality vary depending on the type
of customers. Because there are spatial patterns in the locations of different
customer classes (WNis, 1993), there are spatial patterns to the variations in
customer expectations across the utility's service areas.
Similarly, the capital and O&M costs required to meet the demands and
expectations also vary geographically. In an area where the system is utilized at
or near its capacity, the incremental costs of providing for additional customers
may be high because of the potential need for major facility upgrades, for
example, a new distribution substation. The cost of service to an area would be
higher, and the expected service quality there lower than average if that area is
far from existing distribution substations. Besides circuit conditions, such as
equipment age and the degree of automation, service reliability also depends on
the location and environment; it would be worse in areas far from the utility's
service centers and in areas where there are more tree trimming problems, etc.
Because the characteristics and expectations of customers and the costs of
energy delivery and service to the customers both vary geographically, the utility
should exploit modern spatial information technology in its DIS integration to take
advantage of the spatial variations. The utility planner can use spatial
information technology to analyze the customer energy demands and service
expectations against the costs of meeting the demands and expectations. So, the
utility can strategically spend its capital and O&M budget where it counts the
most toward customer expectations, i.e., toward areas where customers would
likely pay more for the service. As illustrated in Figure 3, this type of customer driven
planning helps the utility decide business tactics for each geographic area,
for example, whether to market electricity or gas, whether to upgrade the energy
delivery network or build distributed generation, whether to increase or cut O&M
expenditures, etc.
Figure 3. A Spatial DIS Supports Strategic,
Customer-Driven Planning.
Summary and conclusion
As energy delivery utilities go through mergers, the combined company explores
the operational savings achievable by consolidating the distribution operations
and management, and integrating the information technologies to support the
consolidation. An integrated DIS that facilitates the combined operations of
different functional organizations and that has an integrated data model to
support different data requirements, rates, and qualities of data flows among the
different information technologies involved is needed. While data integration is
critical to the success of a DIS, incorporation of spatial information as part of the
integrated model is key to enabling strategic, customer-driven planning to provide
additional, long-term business benefits.
References
- Engelken, L., Gay, A., Tram, H., 1999, "Development of an IT Strategy and
Architecture for Energy Delivery Utility Mergers," IEEE T&D Conference, New
Orleans.
- Foster, G., Gay, A., Harms, A., Lonski, T., Tram, H., 1999, "implementation of a
Unified Data Model for Distribution Management Systems," DistribuTech
Conference, San Diego.
- Juhi, G., 1998, "Trends in the Utility Industry: Will They Make or Break You," G/S
Wor/d, pp. 42-46.
- Lonski, T., 1997, "Database Integration: Criteria and Techniques," Utility
Automation.
- Tram, H., Engelken, L., Gay, A., 1999, "implementation of an Information
Technology Architecture for Energy Delivery Utility Mergers," DistribuTech
Conference, San Diego.
- Willis, L., Tram, H., et. al., 1993, "IEEE Tutorial on Distribution Planning."
|
|
|
|
