Due to the recent emphasis on a more accurate representation of the electric distribution grid, several utilities now have extensive geographic information system (GIS) databases on distribution feeder equipment and conductor segments. These GIS databases have the ability to manage great amounts of geographical data constructed with spatial information obtained from expensive and labor-intensive manual work. By leveraging these GIS databases, more accurate distribution feeder models can be developed to address the needs of the utilities to improve distribution system modeling for future smart distribution systems. However, there are significant errors found in the GIS models of the secondary distribution circuits, which need to be resolved before using advanced power system GIS analysis strategies. At the same time, the continued increased number of distributed energy resources (DERs) and advanced metering infrastructure (AMI) placed in the secondary feeder, and data acquisition systems (DAS) placed in the distribution system can further complicate the secondary distribution feeder’s GIS model generating more errors. Some of these errors in the GIS data include erroneous geographical location of elements, mismatched element parameters, and incorrect network connectivity. These errors impact the management, maintenance, response, and operation of the distribution system.
Many utilities are making efforts to reduce the errors in the GIS databases. These efforts include standard operating procedures to update changes associated to system assets in the field, as well as line inspection patrols to correct topology errors. However, little effort is being directed towards increasing the accuracy of the coordinate locations of elements such as loads and photovoltaic (PV) systems in the GIS databases. A correct set of coordinates for these elements is essential to obtain a more accurate representation of the secondary lines which connect the loads and distribution transformers as well as to correlate field measurements from AMI and DAS databases with their physical location in the feeder.
Researchers at Arizona State University have developed an automated tool for secondary network topology construction. The tool provides an accurate distribution system topology by assigning loads and distributed energy resource (DER) nodes of a power distribution framework to their corresponding geographical customer locations. The tool can include a three-stage framework: the first stage reads and processes the raw input data, the second stage works automatically using a machine learning based clustering algorithm and a specialized optimization algorithm with no human intervention to assign the load and DER nodes to their associated location, and the third stage provides the load and DER coordinates and physical address.
- Tool for electric utilities with distribution systems that utilize GIS data
Benefits and Advantages:
- Corrects GIS coordinates of the secondary network topology
- Requires minimal human intervention
- Utilizes commonly available input data: municipal parcel GIS delimitation data and utility secondary feeder topology database