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Spatial microgrid planning with renewable energy and electric vehicles

Abstract

Microgrids include distributed energy resources (DER), such as distributed generation (DG), energy storage (ES), and onsite loads. This kind of grid can operate in either grid-connected or island autonomous mode. Among the benefits of implementing microgrids, the improvement of generation efficiency in a sustainable power distribution system have attracted the attention of several research groups to the optimal topology design of microgrids. Several loop-based microgrid topology methodologies have been presented in the specialized literature, because this topology can guarantee network reliability, satisfying a desired level of generation efficiency. In general, these methodologies consider two stages: graph partitioning for microgrid topology planning and loop-based structure design. For the first stage, the necessary clusters of microgrid nodes based on available DERs should be identified. The optimal electrical loop-based path is found in the second stage. In most power distribution networks, renewable energy (RE) equipment and electric vehicles (EVs) are distributed in a dispersed manner. This dispersed distribution allows the use of geographic information systems (GIS) in order to visualize socioeconomic characteristics, spatial distribution of energy resources, weather impacts on power distribution networks, and the geographical location of load centers within the studied zone, thus providing useful spatial information for loop-based microgrid topology studies. GIS provides tools to perform spatial clustering considering several characteristics of the study area. In addition, the definition of the connection point of the microgrid with the power distribution network can be defined using GIS. Therefore, this research project proposes the development of GIS methodologies to assist with the spatial planning of loop-based microgrid topologies through spatial partitioning and optimal electrical planning algorithms. These methodologies should allow to efficiently use distribution grids to supply the high demand required by EV recharging stations over a planning horizon, considering DERs in distribution feeders in the context of microgrids. (AU)

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Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MORRO-MELLO, IGOOR; PADILHA-FELTIRN, ANTONIO; MELO, JOEL D.; HEYMANN, FABIAN. Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory. ENERGY, v. 235, NOV 15 2021. Web of Science Citations: 0.
VENTURA, LUCAS DE OLIVEIRA; MELO, JOEL D.; PADILHA-FELTRIN, ANTONIO; FERNANDEZ-GUTIERREZ, JUAN PABLO; SANCHEZ ZULETA, CARMEN C.; PIEDRAHITA ESCOBAR, CARLOS CESAR. A new way for comparing solutions to non-technical electricity losses in South America. UTILITIES POLICY, v. 67, DEC 2020. Web of Science Citations: 0.
MEJIA, MARIO A.; MELO, JOEL D.; ZAMBRANO-ASANZA, SERGIO; PADILHA-FELTRIN, ANTONIO. Spatial-temporal growth model to estimate the adoption of new end-use electric technologies encouraged by energy-efficiency programs. ENERGY, v. 191, JAN 15 2020. Web of Science Citations: 1.
MORRO-MELLO, IGOOR; PADILHA-FELTRIN, ANTONIO; MELO, JOEL D.; CALVINO, AIDA. Fast charging stations placement methodology for electric taxis in urban zones. ENERGY, v. 188, DEC 1 2019. Web of Science Citations: 0.
RODRIGUES, JOAO L.; BOLOGNESI, HUGO M.; MELO, JOEL D.; HEYMANN, FABIAN; SOARES, F. J. Spatiotemporal model for estimating electric vehicles adopters. ENERGY, v. 183, p. 788-802, SEP 15 2019. Web of Science Citations: 0.

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