IEEE Power & Energy Magazine - March/April 2017 - 53

Errors related to the geographical position of objects
bring a different challenge, ensuring that the physical
connectivity of any electrical element is reflected in the GIS.
fed by a single-phase branch. In general, these and similar
data errors can be mostly solved manually using filters, typical data, and the engineering knowledge of the DNO.
Errors related to the geographical position of objects
bring a different challenge: ensuring that the physical connectivity of any electrical element is reflected in the GIS. For
instance, differences in the coordinates of loads and service
cables, transformers and MV lines/cables, and end points of
connected segments of lines/cables are common errors. To illustrate this, Figure 4 shows the disconnection of a load from
the service cable, of a distribution transformer from the LV
circuit, and of two LV line segments. Given the volume of the
data, solving these issues requires not only automatic but sophisticated approaches that exploit the topological characteristics of distribution networks. To tackle this, the University
of Costa Rica adopted a series of rules that use kdtree (a data
structure to organize and manipulate spatial data) and graph
theory-based algorithms to search for disconnected electrical
elements in the GIS and to reconnect them whenever possible. Errors in the order of millimeters were common in the
analyzed GIS data from Costa Rican DNOs.
Finally, it is important to highlight that the creation of
OpenDSS models (or any other software package) from GIS
data requires defining the electrical characteristics of the
network components (e.g., impedances of conductors and
transformers). This information, however, is not normally
available in the GIS of Costa Rican DNOs, and it needs to
be extracted from other databases (e.g., from typical data or
manufacturer data sheets). Depending on the type of network studies to be carried out, additional information might
be needed. For instance, the short circuit capacity at the substations-critical in fault analyses-were not in the GIS and
were requested from the DNOs.

Tools in Action
As previously mentioned, in early 2016 the Ministry of Energy
and Environment requested all DNOs to carry out detailed network studies to identify the distributed generation hosting capacity of the circuits. This led to a collaboration between the DNOs
and the University of Costa Rica to develop a platform that integrates the corresponding GIS with distribution network analysis
software. This platform is comprised of the four tools presented
as blocks in Figure 3 and described in detail below. These tools
were developed with QGIS, OpenDSS, and Python and designed
to assist DNOs with data correction, modeling, network studies,
and result visualization. Prior to the use of these tools, users are
required to combine all network, meteorological, and socioecomarch/april 2017

nomic data within one single QGIS project and enable the COM
interface in QGIS. Although the tools are run sequentially, the
correction and modeling tools are necessary only the first time
the data are being processed. The network analysis and visualization tools are run as many times as the required studies.
✔✔ Tool 1-GIS data correction: This tool detects common errors in network data and corrects the ones that
can be resolved without user intervention. The exact location and type of errors that could not be automatically
solved are reported so that users can manually modify
the GIS data accordingly. This tool will be needed only
when the network data has not been corrected before;
multiple runs will not solve more errors.
✔✔ Tool 2-Translating GISs to electrical models: This
tool uses advanced algorithms to connect and produce
the corresponding models of distribution transformers, MV and LV lines, and loads. For each customer,
the tool creates and assigns a time-series load profile
using the corresponding location and monthly energy consumption. Given that the demand in Costa
Rica changes primarily from weekdays to weekends
(seasonal changes are limited, and hence the demand
varies little from one month to the other), only one
profile for weekdays and one for weekends is produced. To create these profiles, the tool uses a statistical characterization of demand derived from power
quality monitoring data of more than 1,000 residential
and 400 industrial/commercial customers nationwide
collected by ARESEP in collaboration with the University of Costa Rica. Tool 2 also models the existing PV systems, provided a GIS layer with location,
capacity, and type of panel (e.g., standard, thin-film,
or premium) is available (Costa Rican DNOs are mandated to store this information). The creation of these
files is further explained in the article "Distribution
Network Model Builder for OpenDSS in Open Source
GIS Software." The input data are the corrected GIS
layers of the circuit (resulting from Tool 1). This tool is
needed only once and when the network model has not
been created before; multiple runs will not produce
different models.
✔✔ Tool 3-Distribution network analyzer: This tool
integrates OpenDSS with QGIS. Crucially, it allows
more PV systems to be added to the network for carrying out impact studies with higher penetration levels
from the existing one so that eventually the hosting capacity of the circuit can be quantified. For this, the user
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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - March/April 2017