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In contemporary days, the research and development enterprises have been focusing to design intelligently the battery swap station (BSS) architecture having the prospects of providing a consistent platform for the successful installation of the large-scale fleet of hybrid and electric vehicles (i.e. xEVs). The BSS may calibrate its subsystem for the electric vehicle (EV) deployment by accomplishing similar idea as in existing gasoline refuelling stations, in which the discharged batteries are being replaced or swapped by partially or fully charged ones by spending a few minutes. The BSS approach has arisen as a promising technology to the traditional EV recharging station approach as it provides a broader experience of business prospects for the specific stakeholders. This work deals with the introduction to BSS including infrastructure, techniques, benefits over charging station and key challenges associated with BSS. Furthermore, an S34X-smart swapping station for xEVs is proposed and finally, the key thrust is research for BSS is discussed. To the authors’ knowledge, this is the first kind of review work on BSS.

As electric vehicles gain popularity, there has been a lot of interest in supporting their continued development with the aim of enhancing their dependability, environmental advantages, and charging efficiency. The scheduling of navigation and charging for electric vehicles is among the most well-known research topics. For optimal navigation and charging scheduling, the coupled network state between the transportation and power networks must be met; moreover, the scheduling outcomes might significantly impact these networks. To address climate challenges, relying only on fossil fuel-based infrastructure for electric car charging is insufficient. Consequently, Multi-Energy Integrated EV charging stations have emerged as a workable solution that seamlessly integrates grid power, renewable energy sources—particularly solar energy—and EV charging needs. The enhanced grey wolf optimised (GWO) ANFIS controller for Maximum Power Point Tracking (MPPT), standby battery systems, solar power, neural network-integrated grids, and sophisticated control algorithms like PID controller are all proposed in this article as energy-efficient charging terminals for electric vehicles. Moreover, authors had considered four conditional case study and with the help of MATLAB/Simulink 2018a software, the design is thoroughly examined and assessed, providing a viable route for an efficient and sustainable EV charging infrastructure.

The output of photovoltaic (PV) systems is significantly impacted by the vagaries of ambient temperature, solar irradiance, and environmental fluctuations. To achieve the utmost attainable power from PV systems, it is desired to be efficient at the maximum power point in diverse weather climates. Maximum power point tracking (MPPT) is used to schedule a designated location from where the highest power can be harvested. In the context of solar photovoltaic systems connected with DC microgrid platforms, this study introduces a recently developed drone squadron optimization (DSO) scheme that tracks the global maximum power point under PSCS difficulties. Furthermore, an exhaustive comparative analysis has been presented among particle swarm optimization (PSO), cuckoo search algorithm (CUSA), and grey wolf optimization (GWO) under different operating environments to endorse the supremacy of the nominated technique. The suggested method performs noticeably faster than many other methods currently in use, and in addition to offering the highest power, it can also use bidirectional power flow regulation in both constant and variable air conditions. Lastly, an MPPT system interfaced with the DC microgrid based on DSO ensures a sustainable and reliable architecture to provide at load in low power generating situations.

The concept of microgrids (MGs) as compact power systems, incorporating distributed energy resources, generating units, storage systems, and loads, is widely acknowledged in the research community. Globally, nations are adopting MGs to access clean, affordable, and reliable energy solutions. However, effective MG operation encounters several challenges: stability issues, power quality concerns, inadequate energy management, cybersecurity threats, regulatory complexities, economic barriers, market dynamics, and limited public acceptance. This paper presents a systematic literature review encompassing recent advancements in MG technology. It delves into MG architecture, diverse control objectives, associated methodologies, emerging control approaches, future challenges, and potential solutions. This review focuses on existing control methods, particularly those addressing frequency and voltage stability, energy management, threat mitigation and explores a spectrum of engineering and nonengineering challenges within MG systems, proposing viable solutions. This review paper also explores recent control strategies for frequency regulation in MG system, utilizing MATLAB simulations to demonstrate their effectiveness. Additionally, the paper examines the application of cutting‐edge technologies like machine learning, blockchain, reinforcement learning, neural networks, edge computing, and the internet of things (IoT) to mitigate concerns in expanding MG systems. The paper concludes by summarizing key findings, outlining avenues for future research, and offering a comprehensive perspective on the current state and future directions of MG research.

This review explores the transformative potential of digital technologies in advancing green finance initiatives to support net‐zero energy transitions. It examines how innovations in FinTech, blockchain, artificial intelligence, and the Internet of Things drive innovation in financing mechanisms, enhance transparency, and improve operational efficiencies across critical sectors, including renewable energy, carbon reduction, lower energy consumption, green industrial transformation, low‐carbon technologies, and sustainable infrastructure including electric vehicles and green buildings. Further, this review addresses challenges associated with implementing these technologies, such as data security risks, regulatory inconsistencies, and unequal access to digital tools. Additionally, this article provides several real‐life case studies and success stories, as well as actionable recommendations that can help develop scalable, equitable, and transparent green finance solutions. The results of this study further highlight the critical interplay between digital technologies and low‐carbon financing initiatives, which can play a critical role in driving a sustainable, low‐carbon future by reducing carbon emissions. This study aims to provide actionable recommendations for leveraging digital innovation for the achievement of scalable, equitable, and transparent Net Zero Energy Transition by offering actionable recommendations. As a result of this comprehensive analysis, the review highlights the critical interplay between digital technologies and GF as vital drivers of a sustainable and low‐carbon future.

A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel‐based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions. It is imperative that electric vehicle charging stations be equipped with solar power and standby batteries. Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller.

Residential cooking with non‐renewable energy sources, such as firewood, charcoal, natural gas, participate in the emission of more than a gigaton of CO2 per year, which represents 2% of the global CO2 emissions. Additionally, toxic particles including sulfur dioxide, carbon monoxide, and mercury are released leading to elevated levels of indoor air pollution, and adversely affecting the health of the inhabitants. The residential sector's non‐renewable energy cooking devices also pose significant problems, consuming approximately 30%–40% of global energy usage, with over 80% dedicated to cooking applications. To mitigate the negative impacts of traditional cooking on health and the environment, various renewable energy‐based cooking technologies have been developed recently. The primary contributions of our paper are to: (a) present a comprehensive review of concentrated solar thermal cooking technologies, assessing their social, economic, and environmental impact across different climatic zones in developing countries like India; (b) classify and compare different solar cooking technologies, highlighting their advantages and limitations in various scenarios; (c) evaluate the energy efficiency of diverse solar cooking technologies; (d) analyze the impact of solar cookers on communities in developing countries; and (e) identify the challenges and future directions for solar cooker technologies, particularly in solar community kitchens. Our novel findings demonstrate that using solar cooking devices can reduce energy consumption by up to 56% in Indian schools. Moreover, the payback period ranges from 3 to 6 years, contingent on the technology's cost, climatic conditions, and available subsidies. Consequently, significant positive impacts on society, the economy, and the environment are observed when traditional cooking devices are replaced by solar cooking devices. This study provides a unique and thorough analysis, contributing to the growing body of knowledge on sustainable cooking solutions and their potential to transform energy consumption patterns in developing regions. Detailed classification with working mechanism of a parabolic dish solar cooker.

Nanotechnology has been applied in the electrical industry for the enhancement of insulation properties. The compactness of the electrical machines has resulted in the requirement and creation of next‐generation insulating fluid with inflated dielectric properties. In this study, the magnetic nanoparticles are used in different concentrations to form stable nanofluids comprising ester‐based oils and two different electrode structures. The host fluids are synthetic ester oil and rapeseed oil, and magnetic nanoparticles used are iron (II, III) oxide, cobalt (II, III) oxide, and iron phosphide. Furthermore, the breakdown tests are analysed using Weibull statistical distribution.

Energy is always needed more and more as civilization advances. Since the supply of traditional fuels is gradually depleting, renewable energy sources are essential for meeting energy needs. The goal of the research is to maximize the electricity that can be produced from renewable resources. Solar energy performed better than any resource regarding efficiency, cleanliness, and pollution‐free nature. However, the primary drawback of the resource is its erratic nature. The system must integrate the maximum power point (MPP) tracking (MPPT) method to overcome intermittency and produce continuous optimal power. The novelty of this article is the development of a sliding mode MPPT controller for photovoltaic (PV) systems working in sunny and shaded. Also, this article introduces the meta‐heuristic algorithm mountain gazelle optimization (MGO), which is incorporated to optimize the parameters of the sliding mode controller (SMC) to ensure the solar PV (SPV) MPP extraction. The stability of the mentioned control topology is assessed in terms of error parameters. The modified perturb and observe (MPb&O) method is incorporated with MGO, called (MGO‐MPb&O) for performing a better tracking ability and to overcome the inability of conventional Pb&O tracking during the shaded conditions. The suggested approach looks for every maximum point across a lengthy number of cycles to find the global maximum point after introducing (MGO‐MPb&O), and the results of the MATLAB/Simulink show that the algorithm performs well under the arbitrary changes of the physical parameters of the proposed system and ambient scenario. Also, the proposed hybrid (MGO‐MPb&O) is compared with two other hybrid control topologies that are (PSO‐MPb&O) and (cuckoo search algorithm [CSA]‐MPb&O) in terms of maximum power extracted, efficiency, and convergence time of the objective function. Results depict that the proposed algorithm outperforms in every aspect and also justify the robustness of SMC. The proposed algorithm was also tested in various shading fashion (SF) in partial shading conditions for analyzing the transient response. These two performance figures for various transition instances demonstrate that the suggested MPPT algorithm can determine the global MPP for the new shading pattern (SP) when it shifts from a uniform state to a partially shaded condition at 4s (middle of the x ‐axis).

Because of the fluctuating demands for electricity and the growing awareness of the need to protect the environment from global warming and the depletion of nonrenewable natural resources, battery-powered electric vehicles, or EVs, are being used in the transportation sector as an alternative to internal combustion engine vehicles. However, charging these EVs with conventional fossil fuels is neither economically sustainable nor structurally viable. Therefore, this manuscript proposes a renewable energy-powered EV charging station featuring a combination of solar energy, standby battery systems, sophisticated control techniques such as neural network-integrated grids, the enhanced Cuckoo Search Algorithm for Maximum Power Point Tracking, and the Proportional-Integral-Derivative controller. This idea beats current methods and presents a viable way to drive the EV revolution while lessening environmental effects. It maximizes energy management and guarantees a steady power supply even in erratic weather. Grid integration ensures the consistency of power supplies at charging terminals. When compared to other algorithms that have been investigated in the literature, the designed algorithm exhibits excellent performance. Grid integration, in addition to the standby battery, is essential in ensuring that the charging station has a constant power supply, even during unpredictable weather.