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One of the most powerful modern methods of calculating complex building structures is the finite element method in theform of a displacement method for discrete systems, which involves the creation of a finite element model, that is, splittingthe structure into separate elements within each of which the functions of displacements and stresses are known. On the basisof the displacement method and the methods of iterations and half-division, an algorithm for stability calculation of the firstkind equilibrium form of compressed reinforced concrete columns with hinged fixing at the ends, considering the stiffnesschanging has been developed. The use of the above methods enables to determine the minimum critical load or stress at thefirst bifurcation and their stability loss corresponding form. The use of matrix forms contributes to simplification of high order stability loss equation. This approach enables to obtain the form of stability loss that corresponds to the critical load.

Electric vehicles (EVs) are gradually becoming an integral part of the drive to accomplish sustainable energy standards. Due to their limited onboard battery capacity, EVs’ expanding popularity creates a need for widespread charging stations. However, fast charging stations, particularly Extreme Fast Charging (EFC), may impose a hassle on the electrical system due to overload during peak hours, frequent power gaps, and voltage sag. To flatten the power supply, the photovoltaic (PV) Hybrid Energy Storage Systems (HESS) and the uncertain and variable nature of PV systems always include solar and hybrid energy storage systems (HESS) such as batteries and supercapacitors. This research suggests a multi-port DC-DC converter (MPC) with a bidirectional DC-DC converter for battery ESS-integrated PV systems. The MPC can regulate the majority of active power through PV to a battery, PV to an EV charging station, HESS to an EV charging station, and PV to AC grid. Additionally, a PI controller is used for the MPC, taking both the PV and battery voltage variations into account. Therefore, the presented configuration can achieve the key benefits of greater integration, more efficiency, and reduced cost. Simulation results show the advantages of this multiport EV charging circuit with PV-HESS and design in different modes.

Medium and heavy-duty battery electric trucks (BETs) may play a key role in mitigating greenhouse gas (GHG) emissions from road freight transport. However, technological challenges such as limited range and cargo carrying capacity as well as the required charging time need to be efficiently addressed before the large-scale adoption of BETs. In this study, we apply a geospatial data analysis approach by using a battery electric vehicle potential (BEVPO) model with the datasets of road freight transport surveys for analyzing the potential of large-scale BET adoption in Finland and Switzerland for trucks with gross vehicle weight (GVW) of over 3.5 t. Our results show that trucks with payload capacities up to 30 t have the most potential for electrification by relying on the currently available battery and plug-in charging technology, with 93% (55% tkm) and 89% (84% tkm) trip coverage in Finland and Switzerland, respectively. Electric road systems (ERSs) would be essential for covering 51% trips (41% tkm) of heavy-duty trucks heavier than 30 t in Finland. Furthermore, range-extender technology could improve the trip electrification potential by 3–10 percentage points (4–12 percentage points of tkm).

Recently, a traction blockade in the depots of numerous electric multiple units (EMUs) of high-speed railways has occured and resulted in some accidents in train operation. The traction blockade is caused by the low-frequency oscillation (LFO) of the vehicle–grid (EMUs–traction network) system. To suppress the LFO, a scheme of EMUs line-side converter based on the H∞ control is proposed in this paper. First, the mathematical model of the four-quadrant converter in EMUs is presented. Second, the state variables are determined and the weighting functions are selected. Then, an H∞ controller based on the dq coordinate is designed. Moreover, compared with the simulation results of traditional proportional integral (PI) control, auto-disturbance rejection control (ADRC) and multivariable control (MC) based on Matlab/Simulink and the RT-LAB platform, the simulation results of the proposed H∞ control confirm that the H∞ controller applied in EMUs of China Railway High-Speed 3 has better dynamic and static performances. Finally, a whole cascade system model of EMUs and a traction network is built, in which a reduced-order model of a traction network is adopted. The experimental results of multi-EMUs accessed in the traction network indicate that the H∞ controller has good suppression performance for the LFO of the vehicle–grid system. In addition, through the analysis of sensitivity of the H∞ controller and the traditional PI controller, it is indicated that the H∞ controller has better robustness.

In this paper, we developed a linear programming model to determine the volume of vessels that will give an optimal return on investment. The solution to the developed model was carried out using the Interior Point algorithm with the help of the MATLAB package. The analysis shows that the production and transporting of the LNG with a vessel of capacity 178,5006m3 would give an optimal profit of 633,640 million USD. And from the results of the analysis, we observed that the decision to take the maximum modern capacity instead of lower capacities yields the highest profit.

Due to the increasing penetration of distributed generation and new high-power consumption loads - such as electric vehicles (EVs) - distribution system operators (DSO) are facing new grid security challenges. DSOs have historically dealt with such issues by making investments in grid reinforcement. However, an alternative solution, enabled by the expected roll-out of smart meters and high penetration of flexible loads, would be the increased use of flexibility services. Flexible loads, with EVs at their forefront, can modulate their consumption or even inject power back to the grid depending on current grid conditions. In return, flexibility provision should be remunerated accordingly. In this paper, the authors are interested in making an accurate description of the flexibility services at the distribution level which could be provided by EVs as well as their requirements, e.g. location, activation time and duration. Market design recommendations for enhancing the provision of DSO grid services by EVs are derived from the conducted analysis.

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

The European Commission (EC) has set ambitious CO2 emission reduction objectives for the transport sector by 2050. In this context, most decarbonisation scenarios for transport foresee large market penetration of electric vehicles in 2030 and 2050. The emergence of electrified car mobility is, however, uncertain due to various barriers such as battery costs, range anxiety and dependence on battery recharging networks. Those barriers need to be addressed in the 2020–2030 decade, as this is key to achieving electrification at a large scale in the longer term. The paper explores the uncertainties prevailing in the first decade and the mix of policies to overcome the barriers by quantifying a series of sensitivity analysis scenarios of the evolution of the car markets in the EU Member States and the impacts of each barrier individually. The model used is PRIMES-TREMOVE, which has been developed by E3MLab and constitutes a detailed energy-economic model for the transport sector. Based on model results, the paper assesses the market, energy, emission and cost impacts of various CO2 car standards, infrastructure development plans with different geographic coverage and a range of battery cost reductions driven by learning and mass industrial production. The assessment draws on the comparison of 29 sensitivity scenarios for the EU, which show that removing the barriers in the decade 2020–2030 is important for electrification emergence. The results show that difficult policy dilemmas exist between adopting stringent standards and infrastructure of wide coverage to push technology and market development and adverse effects on costs, in case the high cost of batteries persists. However, if the pace of battery cost reductions is fast, a weak policy for standards and infrastructure is not cost-effective and sub-optimal. These policies are shown to have impacts on the competition between pure electric and plug-in hybrid vehicles. Drivers that facilitate electrification also favour the uptake of the former technology, the latter being a reasonable choice only in case the barriers persist and obstruct electrification.