Unlike a land-based standalone microgrid, a shipboard microgrid of an all-electric ship (AES) needs to shut down generators during berthing at the port for exanimation and maintenance. Therefore, the cost of onshore power plays an important role in an economic operation for AESs. In order to fully exploit its potential, a two-stage joint scheduling model is proposed to optimally coordinate the power generation and voyage scheduling of an AES. Different from previous studies that only consider the operation cost of the ship itself, a novel coordinated framework is developed in this article to address the shore-side electricity price variations on the ship navigation route. A deep learning-based forecasting method is utilized to predict the electricity price in various harbors for ship operators. Then, a hybrid optimization algorithm is designed to solve the proposed multiobjective joint scheduling problem. A navigation route in Australia is adopted for case studies and simulation results demonstrate the high energy utilization efficiency of the proposed algorithm and the necessity of on-shore power influence on the AES voyage.

中文翻译：

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基于预测岸电价格的全电动船舶协调优化能源管理和航次调度

与陆基独立微电网不同，全电动船舶（AES）的船载微电网在靠泊港口时需要关闭发电机进行检查和维护。因此，陆上电力的成本在 AES 的经济运行中起着重要作用。为了充分发挥其潜力，提出了一种两阶段联合调度模型来优化协调AES的发电和航行调度。不同于以往的研究只考虑船舶本身的运营成本，本文开发了一种新的协调框架来解决船舶航行路线上的岸侧电价变化。利用基于深度学习的预测方法为船舶运营商预测各个港口的电价。然后，设计了一种混合优化算法来解决所提出的多目标联合调度问题。以澳大利亚的一条航路为例进行了研究，仿真结果证明了该算法的高能量利用效率以及岸电对AES航程影响的必要性。