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A direct drive wave power generation system (DDWPGS) has the advantages of a simple structure and easy deployment, and is the first choice to provide electricity for islands and operation platforms in the deep sea. However, due to the off-grid, the source and load cannot be matched, so accommodation is an important issue. Hydrogen storage is the optimal choice for offshore wave energy accommodation. Therefore, aiming at the source-load mismatch problem of the DDWPGS, an electric-hydrogen hybrid energy storage system (HESS) for the DDWPGS is designed in this paper. Based on the characteristics of the devices in the electric-hydrogen HESS, a new dynamic power allocation strategy and its control strategy are proposed. Firstly, empirical mode decomposition (EMD) is utilized to allocate the power fluctuations that need to be stabilized. Secondly, with the state of charge (SOC) of the battery and the operating characteristics of the alkaline electrolyzer being considered, the power assignments of the battery and the electrolyzer are determined using the rule-based method. In addition, model predictive control (MPC) with good tracking performance is used to adjust the output power of the battery and electrolyzer. Finally, the supercapacitor (SC) is controlled to maintain the DC bus voltage while also balancing the system’s power. A simulation was established to verify the feasibility of the designed system. The results show that the electric-hydrogen HESS can stabilize the power fluctuations dynamically when the DDWPGS captures instantaneous power. Moreover, its control strategy can not only reduce the start-stop times of the alkaline electrolyzer but also help the energy storage devices to maintain a good state and extend the service life.

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the ‘Electric Vehicles–Power Grid–Traffic Network’ fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners’ charging behavior.

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.

Long queues of vehicles are often found at signalized intersections, which increases the energy consumption of all the vehicles involved. This paper proposes an enhanced eco-approach control (EEAC) strategy with consideration of the queue ahead for connected electric vehicles (EVs) at a signalized intersection. The discharge movement of the vehicle queue is predicted by an improved queue discharge prediction method (IQDP), which takes both vehicle and driver dynamics into account. Based on the prediction of the queue, the EEAC strategy is designed with a hierarchical framework: the upper-stage uses dynamic programming to find the general trend of the energy-efficient speed profile, which is followed by the lower-stage model predictive controller to computes the explicit solution for a short horizon with guaranteed safe inter-vehicular distance. Finally, numerical simulations are conducted to demonstrate the energy efficiency improvement of the EEAC strategy. Besides, the effects of the queue prediction accuracy on the performance of the EEAC strategy are also investigated.

Abstract Operating in island mode when failure occurs in the active distribution network(ADN) is an effective measure to maintain an uninterrupted power supply to significant loads. A two-stage approach that integrates optimal island partition and power dispatch is proposed in this paper, considering photovoltaics (PVs), batteries (BEs) and electric vehicles (EVs) as the power sources. In the first stage, energy indices are defined to describe the energy demand and the maximum energy that these distributed energy resources (DERs) can provide, and islands are partitioned based on an energy constraint. Considering the variability the loads and PVs, the energy constraint is a necessary but not sufficient condition for island operation, so in the second stage, a power dispatch model is proposed as a test for the island partition result. Sequential power flow is also simulated to guarantee a feasible and optimized island status. The situations when the tests are not passed are analyzed and classified, and corresponding modifications for the first stage model are provided. Multiple levels of constraints based on the energy index are established for the island partition model. The proposed approach has been validated through simulation using a modified IEEE 69-bus system which is divided into three districts with different load variability characteristics.

Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), plays a critical role for global energy transition and decarbonization. In this context, coordination of EV routing and charging activities through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled power-transportation networks (CPTN). This work examines the non-cooperative pricing competition between self-interested EV charging service providers (CSP), taken into account the complex interactions between CSPs' pricing strategies, EV users' decisions and the operation of CPTN. The modeling of CPTN environment captures the prominent type of uncertainties stemming from the gasoline vehicle and EV origin-destination travel demands and their cost elasticity, EV initial state-of-charge and renewable energy sources (RES). An enhanced multi-agent proximal policy optimization method is developed to solve the pricing game, which incorporates an attention mechanism to selectively incorporate agents' representative information to mitigate the environmental non-stationarity without raising dimensionality challenge, while safeguarding the commercial confidentiality of CSP agents. To foster more efficient learning coordination in the highly uncertain CPTN environment, a sequential update scheme is also developed to achieve monotonic policy improvement for CSP agents. Case studies on an illustrative and a large-scale test system reveal that the proposed method facilitates sufficient competition among CSP agents and corroborates the core benefits in terms of reduced charging costs for EV users, enhancement of RES absorption and cost efficiency of the power distribution network. Results also validate the excellent generalization capability of the proposed method in coping with CPTN uncertainties. Finally, the rationale of the proposed attention mechanism is validated and the superior computational performance is highlighted against the state-of-the-art methods.

Improving the resilience of the distribution grid to extreme rainstorms can effectively reduce the resulting outage losses. Based on the massive number of electric vehicles (EV) with objective capacity in the distribution network environment, it is essential to study the discharge of EVs to make up for the shortage load in the distribution network’s post-disaster recovery. In this paper, we first establish a coupling network integrating the distribution grid and road network, simultaneously coupling information from different levels. Then establish a model describing the characteristics of EV travel behavior based on the travel chain and probability distribution function to portray the spatial distribution of EVs in the time dimension. Then establish a model of EVs in this area and cross-area EV participating in discharge response based on the area after EV clustering. Also, establish the extreme storm weather affected EV discharge willingness model to describe the uncertainties of EVs. Finally, two faults set in the IEEE33 node are simulated and verified, and the model description and analysis of the influencing factors of EV participation in discharge are given.

Freight demand (FD) is closely related to industrial structure (IS), and the determination of this relationship is conducive to the development of the transport industry. This research proposed an input–output structural decomposition analysis (IO-SDA) model to explain the growth of FD with the changes in economic aggregate, structural factor, transport demand intensity, and industry connection. This method enables us to quantify and compare the contribution of industrial development to FD growth of five freight modes and determine the role of the structural factor in the growth of FD. An empirical study was conducted based on China’s 2002 to 2017 input–output tables. The results showed that the construction industry was the most important sector leading to the growth of FD, and the role of the service industry in promoting the growth of FD became more evident. There was a clear trend of concentration to the road freight industry, which may undermine the coordination of the transport industry. The structural factor became an important factor in reducing FD in 2007 to 2017. Based on the findings, it is suggested that China should promote the upgrading of IS and emphasize the importance of green road transport and multimodal transport.