Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles.

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

New Emerging Fast Charging Microscale Electrode Materials.

Fast charging lithium (Li)-ion batteries are intensively pursued for next-generation energy storage devices, whose electrochemical performance is largely determined by their constituent electrode materials. While nanosizing of electrode materials enhances high-rate capability in academic research, it presents practical limitations like volumetric packing density and high synthetic cost. As an alternative to nanosizing, microscale electrode materials cannot only effectively overcome the limitations of the nanosizing strategy but also satisfy the requirement of fast-charging batteries. Therefore, this review summarizes the new emerging microscale electrode materials for fast charging from the commercialization perspective. First, the fundamental theory of electronic/ionic motion in both individual active particles and the whole electrode is proposed. Then, based on these theories, the corresponding optimization strategies are summarized toward fast-charging microscale electrode materials. In addition, advanced functional design to tackle the mechanical degradation problems related to next generation high capacity alloy- and conversion-type electrode materials (Li, S, Si et al.) for achieving fast charging and stable cycling batteries. Finally, general conclusions and the future perspective on the potential research directions of microscale electrode materials are proposed. It is anticipated that this review will provide the basic guidelines for both fundamental research and practical applications of fast-charging batteries.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems.

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms.

On-Off Scheduling for Electric Vehicle Charging in Two-Links Charging Stations Using Binary Optimization Approaches.

In this study, we deal with the problem of scheduling charging periods of electrical vehicles (EVs) to satisfy the users' demands for energy consumption as well as to optimally utilize the available power. We assume three-phase EV charging stations, each equipped with two charging ports (links) that can serve up to two EVs in the scheduling period but not simultaneously. Considering such a specification, we propose an on-off scheduling scheme wherein control over an energy flow is achieved by flexibly switching the ports in each station on and off in a manner such as to satisfy the energy demand of each EV, flatten the high energy-consuming load on the whole farm, and to minimize the number of switching operations. To satisfy these needs, the on-off scheduling scheme is formulated in terms of a binary linear programming problem, which is then extended to a quadratic version to incorporate the smoothness constraints. Various algorithmic approaches are used for solving a binary quadratic programming problem, including the Frank-Wolfe algorithm and successive linear approximations. The numerical simulations demonstrate that the latter is scalable, efficient, and flexible in a charging procedure, and it shaves the load peak while maintaining smooth charging profiles.

Strategies for Building Robust Traffic Networks in Advanced Energy Storage Devices: A Focus on Composite Electrodes.

The charge transport system in an energy storage device (ESD) fundamentally controls the electrochemical performance and device safety. As the skeleton of the charge transport system, the "traffic" networks connecting the active materials are primary structural factors controlling the transport of ions/electrons. However, with the development of ESDs, it becomes very critical but challenging to build traffic networks with rational structures and mechanical robustness, which can support high energy density, fast charging and discharging capability, cycle stability, safety, and even device flexibility. This is especially true for ESDs with high-capacity active materials (e.g., sulfur and silicon), which show notable volume change during cycling. Therefore, there is an urgent need for cost-effective strategies to realize robust transport networks, and an in-depth understanding of the roles of their structures and properties in device performance. To address this urgent need, the primary strategies reported recently are summarized here into three categories according to their controllability over ion-transport networks, electron-transport networks, or both of them. More specifically, the significant studies on active materials, binders, electrode designs based on various templates, pore additives, etc., are introduced accordingly. Finally, significant challenges and opportunities for building robust charge transport system in next-generation energy storage devices are discussed.

T-S fuzzy model predictive speed control of electrical vehicles.

This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi-Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand.