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Charging station location optimization of electric ship based on backup coverage model

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Języki publikacji
EN
Abstrakty
EN
In terms of electric ship energy requirement in navigation, the ship charging station location is especially important. In this paper, a multi-period ship charging station location optimization model is pro-posed to make location decision in overall, from initial possible station sites chosen to the capacity determination for the final location sites. In the first phase, from the perspective of external environment, find out all possible ship charging station candidate sites through the feasible analyze. In the second phase, taking the ship charging demands into consideration, the final ship charging station sites can be selected among the candidate sites based on backup coverage model. In the last phase, regarding the cost of construction and service capability for different grade as the main factor in capacity determination, the optimal capacity of each final ship charging station are determined by means of optimization method. Finally, an example of Yanqi lake in China is used to verify the validity of the proposed methodology. The reasonable location of charging station could ensure the electric energy supply and avoid congestion caused by ship charging gathering. The model can be easily generalized to other problems regarding facility allocation based on user demand.
Twórcy
autor
  • Intelligent Transportation System Research Center(ITSC) ,Wuhan University of Technology, Wuhan, China
  • National Engineering Research Center for Water Transport Safety(WTSC), Wuhan, China
autor
  • Intelligent Transportation System Research Center(ITSC) ,Wuhan University of Technology, Wuhan, China 
  • National Engineering Research Center for Water Transport Safety(WTSC), Wuhan, China
autor
  • Intelligent Transportation System Research Center(ITSC) ,Wuhan University of Technology, Wuhan, China 
  • National Engineering Research Center for Water Transport Safety(WTSC), Wuhan, China
Bibliografia
  • [1] Hogan  K,  ReVelle  C.  Concepts  and  applications  of  backup coverage. Management Science 1986;32:1434–44 
  • [2] Araz C, Selim H, Ozkarahan I. A fuzzy multi‐objective  covering‐based  vehicle  location  model  for  emergency  services[J].  Computers  &  Operations  Research,  2007,  34(3):705‐726. 
  • [3] Zhu Z H, Gao Z Y, Zheng J F, et al. Charging station  location problem of plug‐in electric vehicles[J]. Journal  of Transport Geography, 2016, 52:11‐22. 
  • [4] Xiang Y, Liu J, Li R, et al. Economic planning of electric  vehicle charging stations considering traffic constraints  and  load  profile  templates[J].  Applied  Energy,  2016,  178:647‐659. 
  • [5] Liu  Zhipeng,  Wen  Fushuan,  Xue  Yusheng,  et  al.  The  Optimal  Location  and  Constant  Capacity  of  Electric  Vehicle  Charging  Station  [J].  Automation  of  Electric  Power Systems, 2012, 36(3):54‐59. 
  • [6] Hamaide  B,  Albers  H  J,  Busby  G.  Backup  coverage  models  in  nature  reserve  site  selection  with  spatial  spread  risk  heterogeneity[J].  Socio‐Economic  Planning  Sciences, 2014, 48(2):158‐167.
  • [7] Cruz‐Zambrano M, Corchero C, Igualada‐Gonzalez L, et  al.  Optimal  location  of  fast  charging  stations  in  Barcelona: A flow‐capturing approach[C]// International Conference  on  the  European  Energy  Market.  IEEE,  2013:1‐6.
  • [8] Yang  S,  Wu  M,  Yao  X,  et  al.  Load  Modeling  and  Identification Based on Ant Colony Algorithms for EV  Charging  Stations[J].  IEEE  Transactions  on  Power  Systems, 2015, 30(4):1997‐2003. 
  • [9] Wang Sunwei,Zhou Ronggui,Zhang Gaoqiang,Li Wei,et  al.A  Method  of  optimizing  the  locations  of  Flooding  Emergency  Resource  Stations:  Journal  of  Transport  Information & Safety, 2015.33(5):119‐127.
  • [10] Zhang J, Yan X, Zhang D,Haugen S,Yang X. “Safety  management  performance  assessment  for  Maritime  Safety Administration (MSA) by using generalized belief  rule  base  methodology”[J].  Safety  Science,  2014,  63(4):157‐167. 
  • [11] Fu S, Yan X, Zhang D,C Li,E Zio. Framework for the  quantitative  assessment  of  the  risk  of  leakage  from  LNG‐fueled vessels by an event tree‐CFD[J]. Journal of  Loss  Prevention  in  the  Process  Industries,  2016,  43,pp.42–52.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
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Bibliografia
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