The all-electric ship (AES) usually employs battery energy storage systems (ESSs) in the shipboard microgrid. However, the battery-only storage usually experiences frequent deep discharging or charging to meet the sudden load variations in a voyage, which may lead to significant degradation of battery lifetime. This paper, hybridizes two types of ESSs and proposes a two-step multi-objective optimization method for hybrid ESS (HESS) management. The first step regulates the HESS with the onboard diesel generators to simultaneously optimize both the economic and environmental objectives, and the second step is to split the active power of HESS into two individual ESSs for minimizing the battery cycle degradation. The first step is formulated as a bi-level optimization model through constraint decomposition. Then, a normal boundary intersection method combining with the column-and-constraint generation algorithm is developed to solve the proposed model. Extensive simulations demonstrate that the HESS can effectively resolve the power-density shortage of the battery-only system, and its integration into AES is able to extend the battery lifetime and improve both the economic and environmental indices.

Given the rapid development of the ship industry and increasing ship operating cost and gas emissions, this article studies the optimal operation of a multienergy ship (MES) microgrid to simultaneously reduce the ship operating cost and gas emissions. Apart from the conventional diesel generators, electric energy storage, and photovoltaic cells, the combined cooling heat and power (CCHP) unit is introduced for higher energy utilization efficiency. Besides, to flexiblize the CCHP operation and enhance the multienergy interaction, the thermal storage and power-to-thermal conversion unit are also included in the MES microgrid. Then, a multiobjective coordinated energy dispatch and voyage scheduling method are presented. After the constraints linearization, the coordination method is formulated as a mixed-integer linear programming problem, which is solved by the augmented ϵ-constraint method through Cplex solver. Finally, the proposed method is verified on a testing MES microgrid with necessary sensitivity analysis, and by comparing it with traditional benchmarks, its advantages are demonstrated. The simulation results indicate that the proposed method is effective for reducing the operating cost and emission, and it could provide references for the optimal operation of the emerging multienergy cruising ships.

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 examination 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.

Fully electrified ships, which is known as the “all-electric ships (AESs)”, have the potentials to bring great economic /environmental benefits. To further improve the energy efficiency of AESs, PV generations are gradually integrated, which introduces uncertainties to the AES operation. However, current researches mostly focus on sizing problem whereas rarely concern the operation. In this perspective, a data-driven robust coordination of generation and demand-side is proposed to properly address the onboard PV generation uncertainties as well as reducing the fuel cost of AESs, which consists of an extreme learning machine (ELM) based PV uncertainty forecasting method and a two-stage operating framework, where the first stage for the worst PV generation case and the second stage targets at the uncertainty realization. A 4-DG AES is implemented into the case study and the simulation results show that the ELM-based method can well characterize the PV uncertainties, and the two-stage operating framework can well accommodate the onboard PV uncertainties. Further analysis also demonstrates the proposed method has enough flexibility when facing working condition variations.

Under increasingly stringent emission regulations, carbon capture system (CCS) is a feasible alternative to reduce the shipping greenhouse gas (GHG) emission before the maturity of renewable energy technology. In this sense, this article proposes an optimal sizing method to determine the capacity of shipboard CCS under strict energy efficiency operating index (EEOI) constraint. The proposed model is formulated as a two-stage planning problem, where the first stage is to determine the capacity of CCS and the expanded capacity of energy storage system to sustain the operation of CCS, and the second stage is a joint shipboard generation and demand-side management model to address the power shortage issue led by the CCS integration. Extensive simulations demonstrate that under EEOI constraint, the CCS integration is feasible to reduce the shipping GHG emission, and the proposed joint generation and demand-side management method is able to relieve the power shortage issue of shipboard CCS. The corresponding average carbon capture level increases 11.9% with the joint management.