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.

中文翻译：

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用于更高效全电动船舶的联合光伏相关导航路由和储能系统尺寸方案

随着舰载电力系统的广泛电气化，由于不同的航行路径和速度会导致不同的操作性能，因此在电力推进和太阳能发电集成的船舶中，航行程序变得更加重要。与解决最短路径问题的传统导航不同，本文针对光伏（PV）相关导航路由开发了一种数据驱动的优化方案。为了最大限度地减少全电动船舶 (AES) 的总成本和温室气体排放，提出了一种新的协调优化框架，以在考虑太阳能变化的同时联合优化储能系统 (ESS) 的尺寸和航程调度。比较了几个案例来验证在货船上提出的方法，