Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets.

Wireless charging systems for electric vehicles (EVs) have emerged as a key enabler for sustainable transportation, offering enhanced convenience, safety, and system integration compared to traditional plug-in charging methods. This paper presents a comprehensive review of wireless power transfer (WPT) technologies, highlighting their operating principles, system architecture, and recent advancements in both static and dynamic charging applications. Various WPT techniques, including inductive, capacitive, magnetic gear, and resonant inductive methods, are analyzed with respect to frequency range, efficiency, electromagnetic interference, and suitability for electric vehicle wireless charging systems (EVWCS). Emphasis is placed on compensation topologies such as series–series (SS), series–parallel (SP), parallel–parallel (PP), and hybrid LCC/LCL networks, which are instrumental in mitigating power fluctuations and improving efficiency under coil misalignment conditions. Moreover, control strategies—ranging from proportional–integral and passivity-based approaches to model predictive and sliding-mode control—are reviewed for their role in enhancing system stability and dynamic response. The integration of artificial intelligence, edge computing, and dual-side control architectures is also discussed as a pathway toward adaptive and intelligent power management in dynamic wireless charging systems (DWCS). Finally, the paper examines environmental, economic, and grid-integration considerations, concluding that the continued evolution of hybrid compensation networks and intelligent control algorithms will pave the way for efficient, scalable, and autonomous wireless charging infrastructure for next-generation EVs.

This article presents a unique controller combining fuzzy proportional-integral (PI) and fractional-order proportional-fractional filtered derivative action (FOPDFλ) with the intention of improving the frequency performance of a hybrid interconnected dual-area power grid incorporating a unified power flow controller (UPFC) and superconducting magnetic energy storage (SMES) devices in the two regions. In the proposed fuzzy PI-FOPDF ^λ controller, the positive aspects of fuzzy logic control (FLC), PI, and FOPD regulators, as well as the added flexibility afforded by fractional calculus for the derivative filter, have all been incorporated. The authors used a recently developed metaheuristic optimization strategy (i.e., the Zebra Optimization Algorithm, or ZOA) to fine-tune the suggested controller parameters for the two-area hybrid power grids studied in this paper. The system model depicts physical constraints, such as the reheat turbine and the generation rate constraint (GRC). The superiority of ZOA is demonstrated by comparing its results to those of the recently developed optimizations Osprey Optimization Algorithm (OOA) and Jellyfish Search Optimizer (JSO). By comparing the performance of the proposed ZOA-based fuzzy PI-FOPDF ^λ controller to that of existing ZOA-tuned fuzzy-based controllers (such as fuzzy PIDF and fuzzy PIDD2), it has been demonstrated that the proposed controller is more effective. In order to achieve higher levels of system stability and validate the robustness of the proposed fuzzy PI-FOPDF ^λ controller, various load patterns were applied to the investigated power system. Step load fluctuation (SLF), pulse load fluctuation (PLF), and sinusoidal wave load fluctuation (SWLF) were among these load patterns. In addition, non-conventional sources such as wind turbines and solar photovoltaic (PV) units have been incorporated into the power system. In addition, a test scenario has been developed to demonstrate how the SMES and UPFC units enhance the overall efficacy of the system. MATLAB simulation results indicate that the proposed fuzzy PI-FOPDF ^λ controller, which is based on the ZOA, has the potential to enhance system stability despite the aforementioned challenges.

This paper examines the specifications of lithium battery cells, which are considered one of the most vital sources for electrical energy storage units. The specifications have been covered to associate battery performance with its usage for electrically powered motor vehicles. With the motivation of rapid deployment of electric vehicles (EVs) around the world, the key contribution of this study is to provide a comparative investigation of well-known commercially available Li-ion battery cells used as a pack for electric race car. Five lithium cells from different manufacturers were analyzed for start voltage, end voltage, current, and the use of active cooling under different test conditions. Thermal imaging was used to provide more comprehensive analysis of tested battery packs. The outcomes of this experimental investigation are described in the sections below in the order in which the analyses were conducted. The key findings of this study are presented in the conclusion section.

This study aims to improve the low-voltage ride-through (LVRT) capabilities of wind power plants (WPPs) during various types of grid faults, such as line-to-ground (L-G), double line-to-ground (LL-G), line-to-line (L-L), and three-phase-to-ground (LLL-G) faults. The paper proposes an enhanced cascaded Fractional-Order Proportional-Integral and Proportional-Integral (FOPI-PI)-controlled static synchronous compensator (STATCOM). The Dandelion Optimizer (DO), a novel optimization approach, is employed to fine-tune the STATCOM being studied. The DO optimizer was selected for its exceptional performance and robustness. The efficacy of the DO algorithm was assessed in comparison to three well-established methods such as the Water Cycle Approach (WCA), the Particle Swarm Optimizer (PSO), and a novel hybrid technique that combines WCA and PSO. The analysis focuses on two test systems: the 9MW wind power plant connected to an infinite bus system over a 30 km transmission line, and the IEEE 39 bus system. The results are displayed using MATLAB (R2018b) using time-domain simulation. The suggested system monitors multiple LVRT metrics, including WPP active and reactive power, voltage, and speed as well as DC link capacitor voltage. Upon conducting a comparative analysis, it was found that both the proposed cascaded (FOPI-PI) and conventional PI controller-based DO outperform other methods, such as the PI controller-based WCA algorithm, PSO optimizer, and even a combination WCA/PSO algorithm. The findings indicate that the FOPI-PI controller demonstrates superior performance compared to the PI controller. When the 9MW system has a fault, the recommended FOPI-PI controller restricts the voltage drop of the WPP to specific percentages. Specifically, the voltage drop is limited to 3% for L-G faults, 5% for L-L faults, 6% for LL-G faults, and 10% for LLL-G faults. Finally, the performance of wind power plants is much improved by adopting the proposed FOPI-PI controller optimized by DO in comparison to all other controllers in various test scenarios.

The most recent advancements in renewable energy resources, as well as their broad acceptance in power sectors, have created substantial operational, security, and management concerns. As a result of the continual decrease in power system inertia, it is critical to maintain the normal operating frequency and reduce tie-line power changes. The preceding issues sparked this research, which proposes the Fuzzy Tilted Fractional Order Integral Derivative with Fractional Filter (FTFOIDFF), a unique load frequency controller. The FTFOIDFF controller described here combines the benefits of tilt, fuzzy logic, FOPID, and fractional filter controllers. Furthermore, the prairie dog optimizer (PDO), a newly developed metaheuristic optimization approach, is shown to efficiently tune the suggested controller settings as well as the forms of the fuzzy logic membership functions in the two-area hybrid power grid investigated in this paper. When the PDO results are compared to those of the Seagull Optimization Algorithm, the Runge Kutta optimizer, and the Chaos Game Optimizer for the same hybrid power system, PDO prevails. The system model incorporates physical constraints such as communication time delays and generation rate constraints. In addition, a unified power flow controller (UPFC) is put in the tie-line, and SMES units have been planned in both regions. Furthermore, the contribution of electric vehicles (EVs) is considered in both sections. The proposed PDO-based FTFOIDFF controller outperformed many PDO-based traditional (such as proportional integral derivative (PID), proportional integral derivative acceleration (PIDA), and TFOIDFF) and intelligent (such as Fuzzy PID and Fuzzy PIDA) controllers from the literature. The suggested PDO-based FTFOIDFF controller has excellent performance due to the usage of various load patterns such as step load perturbation, multi-step load perturbation, random load perturbation, random sinusoidal load perturbation, and pulse load perturbation. Furthermore, a variety of scenarios have been implemented to demonstrate the advantageous effects that SMES, UPFC, and EV units have on the overall performance of the system. The sensitivity of a system is ascertained by modifying its parameters from their standard configurations. According to the simulation results, the suggested PDO-based FTFOIDFF controller can improve system stability despite the multiple difficult conditions indicated previously. According to the MATLAB/Simulink data, the proposed method decreased the total fitness function to 0.0875, representing a 97.35% improvement over PID, 95.84% improvement over PIDA, 92.45% improvement over TFOIDFF, 83.43% improvement over Fuzzy PID, and 37.9% improvement over Fuzzy PIDA.