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.

The lifetime of shipboard energy storage systems (ESSs) have great impacts on the operating cost of all-electric ships (AESs) since their high investment costs. Additionally, those ESSs are designed to have multiple battery packs with high capacity redundancy to cope with various navigation scenarios and distributed in different electric zones onboard to avoid operating risks. To extend the battery lifetime while fully addressing the redundant capacity and distributed locations, an optimal hierarchical management model for shipboard multi-battery ESS is proposed, which consists of three levels. In the first level, a practical quadratic degradation cost model is reformulated from a non-linear data-driven model, which considers both the depth of discharge (DoD) and mean state-of-charge (MSOC). Then in the second level, the obtained quadratic model is implemented into the shipboard generation scheduling which views the multi-battery ESS as a “single battery pack”. In the third level, a multi-battery management strategy is implemented to split the “single battery pack’s” power to each battery group by iteratively limiting their MSOCs. To bring the proposed energy management method into practical usage, a real-time simulation experiment is designed to test its validity. The case study shows that the proposed method is suitable for real-time usage and reveals that iteratively regulating the MSOC of batteries will greatly facilitate their lifetime. As a result, the operating cost of AESs can be reduced due to battery lifetime extension.

Energy storage system (ESS) management benefits the operation of all-electric ships (AESs) since its ability to balance the generation and demand. However, current works rarely consider the influence of navigation uncertainties, i.e., uncertain water waves and winds, and fail to address two characteristics of shipboard ESS, i.e., redundant capacity and distributed locations. In this paper, a two-stage robust optimal shipboard microgrid operation method is proposed to mitigate the pre-voyage and intra-voyage navigation uncertainties. The first stage is to regulate the onboard generation and voyage variables for addressing the worst pre-voyage navigation uncertainties and the second stage is an on-line recourse action which acts on intra-voyage uncertainty realization. To address the characteristics of shipboard ESS, the onboard batteries are separated into two groups and a multi-battery management strategy is proposed for the battery lifetime extension. An AES with 4 DGs and 4 batteries is used in the case studies. The simulation results show that the proposed method can well adapt to the navigation uncertainties and the multi-battery management can extend the battery lifetime by iteratively using different battery groups.