Methodology for Defining Priority Locations for Carrying out a Forest Inventory in Points with Conflict between Urban Tree Planting and the Electricity Grid

William de Oliveira Sant Ana, Jefferson de Faria, Mauro dos Santos Zavarize, Daniel Pazini Pezente, Vanessa de Castro Barbosa, Anderson Diogo Spacek, Marcelo Pinto Vianna and Oswaldo Hideo Ando Junior
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William de Oliveira Sant Ana: Technological Center, Association of the Coal Industry of Santa Catarina (SATC), Criciúma 88805-380, Brazil
Jefferson de Faria: Technological Center, Association of the Coal Industry of Santa Catarina (SATC), Criciúma 88805-380, Brazil
Mauro dos Santos Zavarize: Technological Center, Association of the Coal Industry of Santa Catarina (SATC), Criciúma 88805-380, Brazil
Daniel Pazini Pezente: Technological Center, Association of the Coal Industry of Santa Catarina (SATC), Criciúma 88805-380, Brazil
Vanessa de Castro Barbosa: Technological Center, Association of the Coal Industry of Santa Catarina (SATC), Criciúma 88805-380, Brazil
Anderson Diogo Spacek: Technological Center, Association of the Coal Industry of Santa Catarina (SATC), Criciúma 88805-380, Brazil
Marcelo Pinto Vianna: Electric Power Distribution Company (CEEE/Equatorial), Porto Alegre 91410-400, Brazil
Oswaldo Hideo Ando Junior: Academic Unit of Cabo do Santo Agostinho, Federal Rural University of Pernambuco (UFRPE), Recife 54518-530, Brazil

Energies, 2022, vol. 15, issue 3, 1-14

Abstract: The Association of the Coal Industry of Santa Catarina (SATC), together with the State Electric Energy Company (CEEE/Equatorial) of the Brazilian state of Rio Grande do Sul (RS), seeks solutions to reduce conflicts between vegetation and electrical networks. This study was carried out having as an area for pilot the city of Porto Alegre, RS, where the distributor is responsible for the supply of electricity. The objective of this proposal was to define locations for carrying out a vegetation inventory through a predictive model. From satellite images, the existing vegetation in the distribution network was digitally extracted and, from this, five prioritization factors were associated via Hierarchical Process Analysis (HPA), resulting in an equation with consistency ratio where each factor is given a specific weight, resulting in a priority map. The final model is explained by 36% by the number of power outages, 21.5% by vegetation close to the grid, 21% by the affected population, 16.9% by the type of consumer, and 4.6% by the area from the neighborhood. This combination of factors resulted in ‘red zones’ in the pilot area, of which 100 points were chosen for carrying out a forest inventory, with an expected sampling significance of at least 97%. This makes it possible to develop a more assertive and spatially oriented forest inventory, and this model can be replicated in different urban centers.

Keywords: vegetation; power system; predictive modeling; power quality (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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