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.

The increasing penetration of volatile renewable energy poses a significant challenge for islanded microgrids in maintaining the seasonal power balance on a long-term timescale. To support renewable integration, seasonal energy storage techniques are expected to coordinate with short-term storage systems to compensate for power mismatches on multiple timescales. However, hybrid storage sizing is often hindered by the coupling of different timescales, which will lead to a large number of variables and greater computational complexity. Thus, in this article, a novel optimal sizing framework is proposed for a hybrid hydrogen-battery storage system, considering a year-round time horizon. To ensure reliable planning of hydrogen storage, a “seasonal-trend decomposition based on LOESS (STL)” technique is applied to preserve long-term power fluctuation characteristics during scenario clustering. Moreover, a least-squares-based scenario approximation method is developed to improve the accuracy of the clustering results. On this basis, a scenario-transformation solution method is proposed to avoid a large number of variables due to year-round hourly operation. The case studies verify the advantages and efficiency of the proposed method.

With the extensive electrification introduced into the shipboard power systems, the navigation routine has become more important in an electric propulsion and solar power integrated ships since various sailing paths and speeds will lead to different operation performances. Unlike traditional navigation, which solves a shortest path problem, a data-driven optimization scheme is developed in this paper for a photovoltaic (PV) dependent navigation routing. In order to minimize the total cost and greenhouse gas emissions of an all-electric ship (AES), a new coordinated optimization framework is proposed to jointly optimize the energy storage system (ESS) sizing and voyage scheduling, while considering solar power variations. Several cases are compared to verify the proposed method on a cargo ship and numeral results reveal that the deep-learning based forecasting method can better characterize the on-board solar power variations and the proposed joint optimization framework can well accommodate the onboard PV generation.

Owing to the global concern for serious energy crises and increased greenhouse gas emissions, the wind energy capacity has grown rapidly in recent years. However, due to weather variations, dramatic power fluctuations exacerbate the uncertainty of wind power generation. To enhance stability and maximize utilization, the precise prediction of wind power is critical for providing a reliable reference for economic dispatch and energy efficiency improvement. In this article, a novel hedge backpropagation-based online LSTM architecture is developed to predict ultra-short-term wind power outputs. The proposed architecture consists of a hedge backpropagation mechanism, an online deep learning algorithm and a concept drift detection scheme to adaptively enhance prediction accuracy by updating the capacity of the conventional LSTM model. Furthermore, several types of historical data from an offshore wind farm are used as inputs to extract the intrinsic relationships between wind power and environmental factors in a case study. The numerical results demonstrate that the proposed proactive model yields better prediction accuracy than traditional offline algorithms such as recurrent neural networks and gated recurrent unit networks and is expected to improve forecasting performance and support the operation of wind farms.

Energy storage system (ESS) is a critical component in all-electric ships (AESs). However, an improper size and management of ESS will deteriorate the technical and economic performance of the shipboard microgrids. In this article, a joint optimization scheme is developed for ESS sizing and optimal power management for the whole shipboard power system. Different from traditional ESS sizing methods, which solve a minimum cost problem, a novel coordinated framework is proposed to jointly optimize the ESS size and voyage route for an AES, considering the distributionally robust ambiguity onshore electricity prices. The main goals of this method are to reduce the capital and operational cost, greenhouse gas emissions, and navigation time. Furthermore, the dynamic electricity prices are adopted to assist ship operators to select the optimal voyage routine. Several cases are compared to verify the proposed algorithm and numeral simulation results clearly demonstrate the high efficiency of the proposed method and necessity of navigation route scheduling.