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A Joint Photovoltaic-Dependent Navigation Routing and Energy Storage System Sizing Scheme for More Efficient All-Electric Ships

  • Shuli Wen
  • , Tianyang Zhao
  • , Yi Tang
  • , Yan Xu
  • , Miao Zhu
  • , Yuqing Huang
  • Shanghai Jiao Tong University
  • Jinan University
  • Nanyang Technological University
  • Harbin Engineering University

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

With the extensive electrification introduced into the shipboard power systems, the navigation routine has become more important in an electric propulsion and solar power integrated ships since various sailing paths and speeds will lead to different operation performances. Unlike traditional navigation, which solves the shortest path problem, a data-driven optimization scheme is developed in this article for a photovoltaic (PV)-dependent navigation routing. In order to minimize the total cost and greenhouse gas emissions of an all-electric ship (AES), a new coordinated optimization framework is proposed to jointly optimize the energy storage system (ESS) sizing and voyage scheduling while considering solar power variations. Several cases are compared to verify the proposed method on a cargo ship, and numeral results reveal that the deep-learning-based forecasting method can better characterize the onboard solar power variations, and the proposed joint optimization framework can well accommodate the onboard PV generation.

Original languageEnglish
Article number9165810
Pages (from-to)1279-1289
Number of pages11
JournalIEEE Transactions on Transportation Electrification
Volume6
Issue number3
DOIs
StatePublished - Sep 2020
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • All-electric ship (AES)
  • deep learning
  • energy storage system (ESS)
  • joint sizing and routing optimization
  • photovoltaic (PV) generation

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