Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatiotemporal characteristics of the traffic flow simultaneously, we propose a novel spatiotemporal residual graph attention network (STRGAT). First, the network adopts a deep full residual graph attention block, which performs a dynamic aggregation of spatial features regarding the node information of the traffic network. Second, a sequence-to-sequence block is designed to capture the temporal dependence in the traffic flow. The traffic flow data with weekly periodic dependencies are also integrated and STRGAT is used for traffic forecasting of traffic road networks. The experiments are conducted on three real data sets in California, USA. Results verify that our proposed STRGAT is able to learn the spatiotemporal correlation of traffic flow well and outperforms the state-of-the-art methods.

Thermal issue is one of the major concerns for safe, reliable, and efficient operation of the vanadium redox flow battery (VRB) energy storage systems. During the design of the operational strategy for a grid-connected VRB system, a suitable mathematical model is needed to predict the dynamic behaviors under various operating conditions. However, conventional VRB models usually neglect the impact of temperature variations on system performance. This work develops an enhanced VRB model with the consideration of the coupling effects between the electrochemical and the thermal behaviors. The proposed model consists of two equivalent circuits. First, the electrochemical behaviors of the VRB are modeled by a second-order RC network taking account of the effects of concentration variation of the vanadium ions and the electrochemical activation. Second, a third-order Cauer network is used to model the heat transfer process in the VRB system, and the dynamic thermal behaviors of stacks, pipes and heat exchangers are characterized. Well-designed experiments and particle swarm optimization algorithm are use to identify the parametric values of the developed model. The proposed modeling method was validated experimentally using a 5kW/3kWh VRB platform, and the results show that the model is capable of accurately predicting the VRB performance under variable temperature conditions. The developed coupled electro-thermal model is then used for simulating and analyzing the performance of a VRB system operated in conjunction with a wind power plant under real-world conditions.

A multisource energy storage system (MESS) among electricity, hydrogen and heat networks from the energy storage operator's prospect is proposed in this article. First, the framework and device model of MESS is established. On this basis, a multiobjective optimal dispatch strategy of MESS is proposed. Considering the influence of time-of-use price, our dispatch strategy can achieve the unified effect of economy, efficiency, and stationarity. Ultimately, the proposed dispatch strategy is applied to a MESS to study the characteristic of the economic benefits, the influence of energy loss, and power fluctuation, etc. The results show that the MESS can be profitable through our optimized dispatch strategy. And the sale of hydrogen and heat can become the main income of the MESS. Furthermore, we find energy loss is the main challenge for MESS development and application. Considering the economy and efficiency of the system, the proposed dispatch strategy can achieve the optimal comprehensive benefits from the energy storage operator's prospect and support the sustainable development of the MESS.

The rapid development of renewable energy and the continuous growth of peak load bring new challenges to the dispatching capacity of generation side. In view of the possible mismatch between power generation of renewable energy and the load, we propose an integrated optimal dispatching strategy model of power generation and consumption interaction in this paper. Among them, the generation side resources include wind power, photovoltaic and battery energy storage and the load side dispatching resources include transferable load, interruptible load and electric vehicles. The model takes the benefits of generation side and the benefits of load side as objectives. Moreover, three different dispatching orders are put forward, which are random dispatching, interruption and transfer of load before electric vehicles charging and discharging, electric vehicles charging and discharging before interruption and transfer of load. In addition, we propose seven different dispatching combinations based on the optimal dispatching order to analyse the response ability of different load side dispatching resources. Multi-objective Genetic Algorithm and the method of technique for order preference by similarity to an ideal solution are applied in this study to obtain the optimal results. It can be identified that the response ability of different load side dispatching resources is different, and the overall dispatching can improve the benefits of generation side and the benefits of load side. Meanwhile, the proposed strategy considers the willingness of the load to the greatest extent and effectively improves the utilization rate of wind and solar power generation.

For the emergency evacuation of cruise ships in case of sudden fire, this research proposes a dynamic route optimization method based on the improved ${\mathit{A}}^{\ast }$ algorithm for real-time information, in order to obtain the real-time optimal evacuation route. Initially, a basic network topology diagram is established according to the internal structure of the cruise ship. Before the occurrence of the accident, the ${\mathit{A}}^{\ast }$ algorithm can be applied to obtain an a priori evacuation network consisting of all the optimal routes from each node to the exit. At the time of the accident, the dynamic diffusion of fire can be simulated using Fire Dynamics Simulator (FDS) based on the preliminary information of the fire, so as to estimate the impact of the fire domain on each node of the network. Then, according to the fire dynamic diffusion data, the evacuation route planning is carried out by the improved ${\mathit{A}}^{\ast }$ algorithm applying the breadth-first search strategy, so as to determine the optimal route from the current node to the safety exit and to reduce the possibility of casualties due to the uncertainty of the fire during the evacuation. This model allows for both people’s safety and evacuation time to dynamically avoid fire-affected nodes and helps people to reach the safe area as soon as possible. Finally, the evacuation model is established according to the open-source cruise ship structure, and the evacuation process of people under the dynamic spread of cruise ship fire is simulated. The results show that the route planning method proposed in this research works out well in evacuating mass people, which can effectively reduce the evacuation time and improve the safety of the evacuation process.

Hybrid hydrogen-electric vehicles (H2EVs) are developing rapidly in recent years. Evidently, the planning of H2EVs energy-supplying facilities (HVESFs) is important. It involves the coupled power flow of power network (PN), mass flow of hydrogen network (HN), and traffic flow of traffic network (TN), simultaneously. These coupled flows would indeed challenge economic and secure performances of HVESFs planning, especially when uncertain renewable energy is introduced. Therefore, a multinetwork framework considering coupled flow constraints is proposed for the planning issue of HVESFs, involving all the PN, HN, and TN. Therein, we first propose a modified maximum covering location method, in order to satisfy the energy demand of H2EVs. On this basis, a many-objective optimization-based bi-level HVESFs planning model is developed. Then, we use a hybrid differential evolution-interior point algorithm to solve this model, and obtain the planning solutions. Simulation results verify the feasibility and effectiveness of the proposed planning model, which has better performances compared to the existing approach.