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AbstractHybrid ships have recently garnered increasing attention as a promising solution for energy shortages and environmental pollution. However, changes in the navigation environment can greatly affect the energy efficiency of these vessels. Therefore, improving the energy efficiency of hybrid ships continues to be a key issue. Our study examined the energy efficiency of a diesel‐electric series hybrid ship operating on an inland river, and a method to optimise series hybrid ship energy efficiency was proposed. The energy consumption model, power demand model, and decision function with minimum fuel consumption as the evaluation criteria were established according to the ship's characteristics and hybrid system. By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. Afterwards, the optimised speed of the ship and the optimised output power of each power source were determined using the grasshopper optimization algorithm. The ship's energy efficiency levels before and after using the energy efficiency enhancement method were compared and analysed, and the effects of this optimization method on the ship's energy efficiency in several typical voyages were analysed. Our findings demonstrated that this optimization method can distribute the output of the power source in a better way, thereby optimising the speed of the vessel and maintaining stable sailing. More importantly, the proposed method can reduce fuel consumption by 17.04%.

Air traffic control systems play a critical role in ensuring the sustainable and resilient flow of air traffic. The air traffic sector serves as a fundamental topological unit and is responsible for overseeing and maintaining the system’s sustainable operation. Examining the structural characteristics of the air traffic sector network is a useful approach to gaining an intuitive understanding of the system’s sustainability and resilience. In this paper, an air traffic sector network (ATSN) was established in mainland China using the complex network theory, and its motif characteristics were analyzed from a microscopic perspective. Additionally, subgraph resilience was defined in order to describe the network topology by analyzing changes in subgraph motif concentration and subgraph residual concentration. Our empirical findings indicated that motifs exhibit high connectivity, while anti-motifs are found in subgraph structures with low connectivity. The motif concentration of subgraphs can efficiently reflect the distribution of heterogeneous subgraph structures within a network. During the process of resilience evaluation, the subgraph motif concentration remains relatively stable but is sensitive to the transition state of the network from disturbance to recovery. The resilience of the system at the macroscopic scale is aligned with the resilience of each heterogeneous subgraph structure to some extent. Topological indicators have a more significant impact on the resilience of the ATSN than air traffic flow characteristics. This study has the outcome of uncovering the preference for connection among nodes and the rationality of sector structure delineation in ATSNs. Additionally, this research addresses the fundamental mechanism behind the network disturbance recovery process, and identifies the connection between network macro- and microstructure in the resilience process.

An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation.

The annual reporting procedures of the United Nations Framework Convention on Climate Change (UNFCCC) have now produced greenhouse gas (GHG) emission inventories from 40 so-called Annex I countries for 18 years. This article analyses a subset of these data: emissions from road transport. The article compares the reported data with the technical guidance on GHG emission inventories provided by the Intergovernmental Panel on Climate Change (IPCC). The analysis suggests that some countries use the IPCC's default emission factors, whereas other countries use country-specific values. In the case of diesel-fuelled road transport, the estimated emissions appear to be generally comparable between all countries for all years. For CO2 emissions from gasoline-fuelled road transport, the picture is less clear. The results suggest that the default emission factor for CO2 from motor gasoline as provided by the IPCC is about 3-5% too low. Countries that seem to apply this default value might therefore underestimate their emissions by the same percentage. The effect of this possible underestimate on trends is, however, very small. Despite the possible problem with the default emission factor, the quantification of the trend in emissions is only slightly influenced by this. © 2011 Earthscan.

Accurate vessel traffic flow prediction is significant for maritime traffic guidance and control. According to the characteristics of vessel traffic flow data, a new hybrid model, named DWT–Prophet, is proposed based on the discrete wavelet decomposition and Prophet framework for the prediction of vessel traffic flow. First, vessel traffic flow was decomposed into a low-frequency component and several high-frequency components by wavelet decomposition. Second, Prophet was trained to predict the components, respectively. Finally, the prediction results of the components were reconstructed to complete the prediction. The experimental results demonstrate that the hybrid DWT–Prophet outperformed the single Prophet, long short-term memory, random forest, and support vector regression (SVR). Moreover, the practicability of the new forecasting method was improved effectively.

In the context of achieving carbon peaking and carbon neutrality goals, focusing on coordinated efficiency in loss and carbon reduction, and promoting comprehensive green transformation of economic and social development are critical strategies. Line loss is an economic and technical indicator for measuring losses in a power system, and loss reduction is one of the important ways to achieve the carbon peaking and carbon neutrality goals in the power system. However, with the continuous increase in the power grid scale and the increasingly complex operation mode of the system, it is difficult to clearly quantify the carbon reduction benefits brought by system loss reduction. In order to synergize grid loss reduction and system carbon reduction, and generate economic and environmental benefits at the same time, this paper proposes a carbon market trading model that considers multi-layer reactive power compensation strategies. Based on the carbon emission flow model, a node carbon cost pricing is formed, and multi-layer reactive power compensation measures are set in the distribution network nodes to obtain an optimal loss reduction strategy, with the carbon market trading cost minimization as the objective. The effectiveness of the model is verified by simulating and analyzing four scenarios. Compared with the original system that does not consider carbon trading and reactive compensation, the model proposed in this paper can reduce losses by 20% and reduce carbon emissions by 5.7%. This paper is of great value for reactive power loss reduction management in distribution networks of a low-carbon background.

Hybrid systems that integrate wave energy converters (WECs) with floating offshore wind turbines (FOWTs) are considered to be key equipment to deeply exploit marine renewable energy. The power take-off (PTO) system is an important component of the hybrid system, whose parameters also have a significant impact on the hybrid system’s performance. In this paper, a wind-wave hybrid system using hydraulic PTO systems is proposed. A numerical simulation framework based on the linear wave theory and basic equations of hydraulic components is built and verified. The influence of six critical hydraulic parameters on the wave energy capture and motion response performance of the hybrid system is investigated. Specifically, the parameters of piston area, motor displacement, and equivalent generator damping affect the performance of the hybrid system similar to changing the damping term of the PTO system. The parameters of the initial gas volume and the pre-charged pressure of the accumulator affect the wave power capture only for short wave periods, while the motion response of the hybrid system increases with the increase of these two parameters. The parameter of orifice area of the throttle valve affects the performance of the hybrid system slightly only when it is small. The optimal value of partial hydraulic parameters and their corresponding peak performance are also discussed.

High penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV charging depends on the incentive pricing mechanism and the load shifting protocols. In this study, a genetic algorithm-based multi-objective optimization model is developed to generate hourly dynamic Time-of-Use electricity tariffs and facilitate the decision making in load scheduling. As an illustrative example, a case study was carried out to examine the effect of applying demand response for EVs in Beijing, China. With the assumptions made, the maximum peak load can be reduced by 9.8% and the maximum customer savings for the EVs owners can reach 11.85%, compared to the business-as-usual case.