In today's world, the need for energy is growing at an alarming rate. An electric vehicle (EV) is considered pollution-free because of this. As the world's population grows, so does the need for more electric vehicles. In the move toward electrifying transportation, electric vehicles have taken on a leading role. When the energy used to refuel a battery powered automobile is generated from technologies such as solar, wind, or hydropower option that is both green and clean alternative to the current transportation system may be feasible. Photovoltaic grid growth could be facilitated by deploying solar systems to power battery charging stations for electric vehicles while simultaneously storing energy in the batteries of electric vehicles. As electric car batteries and solar systems become more prevalent, the strain on power grids will be lessened, and photovoltaic systems' self-consumption will be increased (Tran and Islam 2019). The charger's synchronicity and smooth mode shifting regulation ensure that it runs smoothly. does not interfere with EV charging or residential power when transitioning between modes. This means the charger can support the utility grid and V2H (vehicle to the house) power transfer to sustain local loads in an isolated situation (Singh and Verma 2020). The installation of a photovoltaic electric vehicle charging station was completed successfully. To connect the solar array to the grid array, a power converter is necessary (mainly boost converter). A direct DC connection to the solar PV array is made here. Its key advantages include reduced circuit complexity, lower converter costs, and no impact on PV array performance.
This architecture can be added to the current charging infrastructure as a retrofit option as it only requires a little adjustment to the technology (Verma and Singh 2020; Simon and Sood 2019). A pv system electric charging station and a Storage Battery (BESS) are proposed for the existing scheme (Biya and Sindhu 2019).To cope with actual EV recharging and renewable power needs, the Dynamic Charging Scheduling Scheme (DCSS) uses the Model Predictive Control (MPC) technique (Zhang and Cai 2018).This entails a battery charging system that charges the battery during off-peak hours and distributes electricity to the home load during peak hours. In addition, this article investigates the integration of home solar panels into the existing grid infrastructure, as well as the use of electric vehicles (Akshya and Ravindran 2017).A converter converts the network to a DC bus, which is linked to electric automobiles through battery chargers. Individual automobile charging methods are decentralised, and a separate control handles power transmission from the AC towards the Grid network interface. Linking a photovoltaic Solar producing plant with a fast charger to counterbalance overall grid rapid progress recharging provides a power management strategies focused on optimal reactive power (Ali ,Mahmoud and Lehtonen 2021).In relation to the P&O algorithm, the suggested While obtaining a higher output The ANN approach tackles the slip flow by accurately tracking the optimal true MPP(Tuyet-Doan and Cho 2020; Abdod and Al-Majidi 2019).
The performance of the incremental conductance(IC) approach in comparison in comparison to the usual utilized perturb and observe (P And O) Optimization technique, with the advantages and disadvantages of the various examined(Elgendy and Zahawi 2013; Mei and Shan 2011). The layout of a quick charging point for electric cars grid-connected is adopted in this paper, which offers quality transmission lines with low harmonic currents. A charging station model has been proposed for faster DC charging(Wajahat khan and Furkan Ahmad 2019). The power point tracking mppt approach for a PMSG wind turbine system is described in this study. This study employs adaptive network-based radial basis function (RBF) artificial neural network (ANN) technology to analyze the generator's ideal rotational properties under fluctuating wind velocity conditions. The modeling results prove that the proposed regulator gathers the maximum possible power from the wind turbine system while maintaining the optimum power coefficient for altering the operational settings at will. (Raman and Rahim 2020).
A wind energy conversion system having variable speed has been created in this research. To order to overcome the various restrictions, we used a novel optimized, and accurate MPPT strategy (wind speed conditions). To enhance the traditional PI-based OTC analysis, based on PI, these studies suggest a novel design (MPPT-OTC) according to artificial neural networks (ANN). The proposed approach (OTC-ANN) was tested on a wind turbine with a permanent magnet synchronous connected to the network (Meghni and Chojaa 2020). To address the problem of losing data and latency between entities in a V to G network, this research recommends a fuzzification and ANN-based intelligent controller composed of DICC, ANN, and FLC. This study uses a two intelligence regulator that includes a data integrity and corrective test blocks during the first stage and a regulating fuzzy system (Fuzzy logic control) with in later to manage demand and reduce the danger of EV refueling upon that network (sah and Bose 2020). The effectiveness of a Power Converter as well as a Buck - boost converter for optimum power monitoring of a solar Pv using Artificial Neural Networks (ANN) is investigated in this work because an ANN-based MPPT model allows exceptionally faster increasing monitoring speed and performance. The recommendations of this study will minimize the time and effort required to design an appropriate Converter operation(Islam and Ahmed 2020).
The postulated (neuro-fuzzy) controller was developed to overcome the limitations of conventional (MPPT) solutions, as an illustration the P&O method. To evaluate suggested (Neuro-Fuzzy controller's) system performance, it is important to compare it to that of the (FLC and traditional P&O) based (MPPT controllers) under fluctuating sun exposure and the temperature of the environment. With the novel approach, voltage variations and output power enable the (MPPT) to be obtained with negligible oscillations and a quick reaction time(Heelan and Al-Qrimli 2020). The study proposes a novel hybrid algorithm that operates in two modes: continual power (CP) model and peak power monitoring mode, and restricts PV formation in CP mode in conformance with the benchmark rated capacity while seamlessly transferring to MPPT mode to enable maximum power eviction from the Pv array. Controlling PV electricity generated at less than full power reduces overburden and improves grid stability, while simultaneously encouraging more PV system adoption.(Bhatia and Mittal 2020).