SPATIO-TEMPORAL MODEL TO ESTIMATE THE ADOPTION OF ROOFTOP SOLAR PHOTOVOLTAIC SYSTEMS

Rommel Eduardo Morales, Sergio Patricio Zambrano, Alberto Rios Villacorta, Trajano Javier Gonzalez

Abstract


The trend for renewable energies has motivated residential consumers around the world to have a rapid penetration in the installation of rooftop solar photovoltaic systems. For this reason, power utility companies must plan the inclusion of rooftop solar photovoltaic systems in their distribution grid. The proposed method projects the quantity and location of these systems. The method is divided into 3 modules: temporal, spatial, and potential modules. In the case of the temporal module, it uses census data by dividing the area into districts, and also, it calculates the number of residential customers, which can be converted into rooftop solar photovoltaic systems. On the other hand, the spatial module adjusts the temporal module based on the interaction and spatial influence of neighbours for each district. Finally, the potential module calculates their energy potential according to the geographical location of the districts and evaluates it with the forecast number of customers from the spatial module. The performance of the method is assessed in the service area of an Ecuadorian power utility. The results show that in Cuenca the greatest influence on adoption is given by two variables, the number of heads of households with permanent employment and the district's electrical power. The customers and energy results produced represent for each scenario only the 7% and 9% of the energy demanded, this concentration is shown through thematic maps that allow identifying the districts that have rapid adoption of solar panels. The results are important tools for the planning of the distribution company, the company will have the areas of highest rooftop solar photovoltaic systems penetration to evaluate its distribution system and maintain its reliability levels.


Keywords


spatial temporal, logistic growth model, geographically weighted regression, solar potential, solar panel.

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v12i4.13319.g8609

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