Open AccessRegional‐scale allocation of fast charging stations: travel times and distribution system reinforcements
Author(s) -
Andrade José,
Ochoa Luis F.,
Freitas Walmir
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.1786
Subject(s) - metropolitan area , productivity , capital cost , scalability , transport engineering , computer science , scale (ratio) , environmental science , environmental economics , operations research , engineering , economics , electrical engineering , geography , economic growth , cartography , archaeology , database
Electric vehicle (EV) fleet is constantly increasing over the years and higher adoption is expected in the coming decades. A key aspect to support and boost the EV uptake is the adequate availability (number, locations and sizes) of fast charging stations (FCSs) to enable inter‐ and intra‐city travels. Since these studies require modelling large regions with uncertainties, a methodology able to provide the least‐cost solution is needed. This study proposes a scalable methodology that integrates high‐resolution traffic flow and multi‐phase electrical simulations to find the number, locations and sizes of FCSs at the least societal cost considering the uncertainties in driving patterns. It determines potential FCSs locations based on traffic flow and progressively explores these FCSs quantifying capital (equipment and land) and indirect (loss of productivity and reinforcements) costs to identify the least‐cost solution. Results from a Brazilian case study comprising of a metropolitan region with six cities and 26 primary substations show that, with high adoption of EVs, the investments in equipment represent the most significant components of societal cost. Moreover, for metropolitan regions, the societal least‐cost solution is found with more but smaller FCSs. Finally, is found that neglecting the loss of productivity can significantly affect the results.