IEEE Power & Energy Magazine - March/April 2017 - 56

One key aspect to quantify these effects in a much
more realistic way was the use of socioeconomic
data when locating and sizing PV systems.
Finally, Tool 4 is executed to visualize the different problems that might be caused by 1,500 kW of PV systems in this
circuit. A summary of the results is reported via the log message panel in QGIS showing the number of customers with
problems (3.75% in this case) as well as the number of overloaded lines (four in MV) and MV/LV transformers (one). Although these impacts can be considered minor, they represent
a bottleneck (i.e., above the hosting capacity) unless the DNO
takes actions to solve the corresponding issues. To further understand the nature and location of the impacts, DNOs can use
the heat map and color-based ranking features to visualize areas with problems at specific times of the day. For instance,
Figure 9 shows in red the buses with voltages above 1.06 p.u.
at noon (color-based ranking feature) in which a cluster of customers with problems has been highlighted. The multiple PV
installations, some of which have relatively high capacities (up
to 20 kW), considered in that particular LV feeder was the result of the economic assessment and is aligned with what also
happens in different parts of the world: more affluent customers with high energy consumption are more likely to afford
and benefit from PV systems. This also demonstrates the value
to DNOs in combining GIS information to adequately assess
future PV adoption. Indeed, the results will vary significantly
in other circuits as they will depend on the corresponding socioeconomic characteristics of the customers.

Supporting Future Smart Grids
This article discussed some of the practical challenges when
integrating GIS databases from Costa Rican DNOs and
other organizations with distribution network analysis software to assess the impacts of rooftop PV systems. One key
aspect to quantify these effects in a much more realistic way
was the use of socioeconomic data (energy consumption and
national census data) when locating and sizing PV systems.
In the short to medium terms, the modeling and analysis
tools will be adapted to cater to smart meter data so even
more realistic assessments can be undertaken. This impact
analysis tool will also incorporate aspects related to power
quality and the coordination of protection devices.
The use of integrated GISs and network analysis software
will help DNOs in adequately assessing the impacts of other
low-carbon technologies. For instance, for electric vehicles,
DNOs could also use transport data as well as the location
of strategic points (e.g., commercial and leisure areas). Furthermore, solutions can also be explored. The network analyses component can incorporate smart grid schemes that,
for instance, involve the use of smart inverters, the active
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management of network elements (e.g., on-load tap changers), and electric vehicle management. In fact, DNOs in Costa
Rica are now interested in extending the existing platform to
cater to the optimal allocation of fast charging stations for
electric vehicles as well as to investigate solutions to improve
the operation of distribution networks.
The collaboration among the Costa Rican DNOs, the
Ministry of Energy and Environment, and the University of
Costa Rica has demonstrated the tangible benefits that can
be achieved when industry and academia join forces. The
positive feedback received from DNOs and the ongoing
improvements are likely to result in a standardized procedure in which Costa Rican DNOs will be required to use the
developed tools to carry out network impact assessments.

For Further Reading
B. Meehan, GIS for Enhanced Electric Utility Performance.
Boston, MA: Artech House, 2013.
I. J. Ramírez-Rosado, L. A. Fernandez-Jimenez, C. Monteiro, V. Miranda, E. Garcia-Garrido, and P.J. ZorzanoSantamaria, "Powerful planning tools," IEEE Power Energy
Mag., vol. 3, no. 2, pp. 56-63, Mar./Apr. 2005.
P. Quesada, A. Argüello, J. Quirós-Tortós, and G. Valverde, "Distribution network model builder for OpenDSS in
open source GIS software" in Proc. IEEE PES Transmission
and Distribution Conf., Morelia, Latin America, Sept. 2016,
pp. 1-6.
G. Valverde, J. D. Lara, A. Lobo, J. D. Rojas, A. Arguello
and C. Montiel, "Techno-economic analysis of distributed
generation in CNFL (in Spanish)," Tech. Rep., Ministry of
Energy and Environment, Costa Rica, 2015.
R. González, A. Argüello, G. Valverde, and J. QuirósTortós, "OpenDSS based distribution network analyzer in
open source GIS environment," in Proc. IEEE PES Transmission and Distribution Conf., Morelia, Latin America,
Sept. 2016, pp. 1-6.

Biographies
Jairo Quirós-Tortós is with the University of Costa Rica,
Costa Rica.
Gustavo Valverde is with the University of Costa Rica,
Costa Rica.
Andrés Argüello is with the University of Costa Rica,
Costa Rica.
Luis (Nando) Ochoa is with The University of Melbourne, Australia, and The University of Manchester, United
p&e
Kingdom.
march/april 2017

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