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Multiport power converters integrate three or more energy devices into a single (potentially highly controllable and efficient) hub. These characteristics suggest that multiport power converters may be valuable for the decarbonisation of distribution networks, where the increase of converter-interfaced devices has degraded system reliability and efficiency. This review analyses the suitability of a wide range of multiport power converter solutions for four example distribution network applications (where previous studies have focussed on a limited range of topologies or applications) and the research areas that can progress their maturity. A review of grid codes and standards overviews the base capability that multiport power converters are likely to require, some of which are carried forward as requirements for a novel comparison tool. The comparison tool is developed to qualify and score reviewed topologies in terms of a range of features that are weighted for the applications. Isolated and partially-isolated topologies perform well due to their flexibility to be configured for the specifications and their operational capabilities (including modularity and voltage decoupling). Further research should focus on the complex control interactions between ports and scaling of these topologies for medium voltages. In contrast, many direct current non-isolated topologies do not qualify due to their low flexibility to be configured for the applications. This suggests that future research could focus on the development of a more flexible non-isolated multiport power converter configuration to take advantage of the high efficiency and low footprint that these topologies might otherwise offer for low voltage applications. This project was funded by the Innovate UK Horizon Europe Guarantee application number 10039599. This work was also funded by the European Union's Horizon Europe research and innovation programme in the framework of the project “iPLUG” with grant agreement 101069770. . Peer Reviewed

This paper investigates how social outcomes from urban transport projects typically play out by reflecting on multi-scale spatial changes induced by projects over time, and the extent to which such changes meet varied interests in project outcomes. We use a multi-methods case study approach using two exemplars, a metro project in London and Seoul, which established extensive public transport networks to support urban growth. Our study highlighted that urban transport network expansion does not always enhance life opportunities for all due to intermediate and cumulative impacts of spatial changes induced by projects. Immediate benefits such as enhanced accessibility were often undermined by long-term consequences of incremental spatial changes at local scales. This study also indicated that differential patterns of spatial changes around nodes between centre and periphery could be attributed to multiple negative impacts on people living in the most deprived areas. To enhance social outcomes, we suggest an integrated approach to urban transport and spatial development that focuses on scale and temporal dimensions of spatial transformation enacted by projects. In conclusion, achieving sustainable and equitable effects from urban transport infrastructure requires careful examination of broader societal consequences of long-term spatial changes and locational contexts, especially function and socio-economic conditions.

This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode control (ASMC). First, a three-degree-of-freedom (3-DOF) vehicle dynamics model was established. Next, under the fuzzy adaptive Unscented Kalman filter (UKF) theory, the vehicle state parameter estimation and road adhesion coefficient (RAC) observer were designed to estimate vehicle speed (VS), yaw rate (YR), sideslip angle (SA), and RAC. Then, a layered control concept was adopted to design the path-tracking controller, with a target VS, YR, and SA as control objectives. An upper-level adaptive sliding mode controller was designed using RBF neural networks, while a lower-level tire force distribution controller was designed using distributed sequential quadratic programming (DSQP) to obtain an optimal tire driving force. Finally, the control strategy was validated using Carsim and Matlab/Simulink software under different road adhesion coefficients and speeds. The findings indicate that the optimized control strategy is capable of adaptively adjusting control parameters to accommodate various complex conditions, enhancing the tracking precision and robustness of vehicles even further.

Understanding the conversion characteristics of pore water is crucial for investigating the mechanism of hydrate accumulation; however, research in this area remains limited. This study conducted methane hydrate formation experiments in unconsolidated sands using an in-house low-field nuclear magnetic resonance (NMR) system. It focused on pore water conversion characteristics and influencing factors such as initial water saturation and sand particle sizes. Results show that methane hydrate formation enhances the homogeneity of the effective pore structure within sand samples. The conversion rate of pore water is significantly influenced by differences in heat and mass transfer capacity, decreasing as initial water saturation and sand size increase. Pore water cannot be fully converted into hydrates in unconsolidated sands. The final conversion ratio of pore water in water-poor sand samples nears 97%, while in water-rich sand samples, it is only 65.80%. Sand particle size variation has a negligible impact on the final conversion ratio of pore water, with ratios exceeding 94% across different particle sizes, differing by less than 3%.