Joint Energy Management and Voyage Scheduling for All-Electric Ships Using Dynamic Real-Time Electricity Price of Onshore Power

Unlike a land-based standalone microgrid, the shipboard power system of an all-electric ship (AES) needs to shut down generators during berthing at the port for exanimation and maintenance. Therefore, the cost of shore 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 voyage scheduling and power generation of an AES. Different from previous studies which only consider the operation cost of the ship itself, a novel coordinated framework is developed in this paper to address the uncertainty of the real-time electricity price of shore-side electricity to optimize the AES's navigation. A deep learning-based forecasting method is utilized to predict the electricity price of various places for ship operators. Then, a multi-stage hybrid optimization algorithm is designed to solve the proposed multi-objective joint scheduling problem. A navigation route in Australia is used for case studies and simulation results demonstrate the accuracy of the forecasting method, the high energy utilization efficiency of the proposed method and the necessity of on-shore power influence on the AES voyage.