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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.

While addressing the issue of improving the performance of Photovoltaic (PV) systems, the simulation results are highly influenced by the PV model accuracy. Building the PV module mathematical model is based on its I-V characteristic, which is a highly nonlinear relationship. All the PV cells’ data sheets do not provide full information about their parameters. This leads to a nonlinear mathematical model with several unknown parameters. This paper proposes a new application of the Grasshopper Optimization Algorithm (GOA) for parameter extraction of the three-diode PV model of a PV module. Two commercial PV modules, Kyocera KC200GT and Solarex MSX-60 PV cells are utilized in examining the GOA-based PV model. The simulation results are executed under various temperatures and irradiations. The proposed PV model is evaluated by comparing its results with the experimental results of these commercial PV modules. The efficiency of the GOA-based PV model is tested by making a fair comparison among its numerical results and other optimization method-based PV models. With the GOA, a precise three-diode PV model shall be established.

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

Distributed energy storage (DES) plays an important role in microgrid operation and control, as it can potentially improve local reliability and resilience, reduce operation cost, and mitigate challenges caused by high penetration renewable generation. However, to ensure an acceptable economic and technical performance, DES must be optimally sized and placed. This paper reviews the existing DES sizing methods for microgrid applications and presents a generic sizing method that enables microgrid planners to efficiently determine the optimal DES size, technology, and location. The proposed method takes into consideration the impact of DES operation on its lifetime to enhance the obtained results accuracy and practicality. The presented model can be used for both grid-tied (considering both grid-connected and islanded modes) and isolated microgrids.

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Huge penetration of grid-tied wind generators into the existing electricity networks increased various challenges in modern power grids. Tremendous attempts are accomplished to properly enhance the behavior of the wind generation systems. This article exhibits a new self-tuned control approach for enhancing the performance of a permanent-magnet synchronous generator-based wind turbine, which is interlinked to the electricity network. The self-tuned technique relies on an improved multiband-structured subband adaptive filter (IMSAF) algorithm, which achieves less computational intricacy over the least-meansquare approach. The IMSAF algorithm-based self-tuned proportional-integral (PI) controller is employed to adjust the interface voltage source converters through a cascaded control structure. The IMSAF algorithm updates the multiple PI controllers' gains on-line without the necessity to optimize or fine-tune. To achieve realistic responses, practical wind speed data measured in Zaafarana wind farm, Egypt, are implemented in this study. The efficacy of self-tuned control approach is compared with that realized using an optimized PI control approach by the water cycle and the genetic algorithms, considering symmetrical and unsymmetrical faults, as the network disturbances. The validity of self-tuned control approach is widely confirmed by performing simulation analyses using MATLAB/Simulink software, and satisfactory responses are achieved. Notably, the IMSAF-based self-tuned control approach is realized to be an accurate means for improving the characteristic of grid-tied wind generators. INDEX TERMS Adaptive control, adaptive filter algorithm, power converters, power system control, power system dynamics, wind energy. NOMENCLATURE ABBREVIATIONS AF Adaptive filter CCS Cascaded control strategy CMPN Continuous mixed P-norm DFIG Doubly-fed induction generator FC Frequency converter GSI Grid-side inverter IMSAF Improved multiband-structured subband adaptive filter The associate editor coordinating the review of this manuscript and approving it for publication was Ruisheng Diao .

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