4.7 Article

Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory

期刊

ENERGY
卷 235, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.121380

关键词

Charging stations; Geographic information system; Graph theory; Power distribution system planning; Spatial analysis

资金

  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brazil (CAPES) [001]
  2. Sao Paulo Research Foundation-Brazil (FAPESP) [2015/21972-6, 2017/01909-3, 2017/22577-9, 2019/00466-6]
  3. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico-Brazil (CNPq) [310299/2020-9, 422044/2018-0]
  4. Instituto Nacional de Ciencia e Tecnologia de Energia Eletrica-Brazil (INCT-INERGE)

向作者/读者索取更多资源

This paper presents a methodology to assist electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. The methodology analyzes possible connection points with graph theory to find the least costly connection, and evaluates the operational limits of the electric distribution network after fast-charging stations have been connected.
Fast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles. (c) 2021 Elsevier Ltd. All rights reserved.

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