Coordinated Optimal Energy Management and Voyage Scheduling for All-Electric Ships Based on Predicted Shore-Side Electricity Price

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.

Automated summary

This study proposes a two-stage joint scheduling model to coordinate power generation and voyage scheduling for all-electric ships. It utilizes deep learning for electricity price prediction and designs a hybrid optimization algorithm to solve the multiobjective joint scheduling problem. Simulation results demonstrate the high energy utilization efficiency of the algorithm and the importance of on-shore power for all-electric ships during voyages.