An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station (CS) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking (MPPT) controlled subsystem, photovoltaic (PV) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles (PHEVs) simultaneously to fulfil the grid-to-vehicle (G2V), vehicle-to-grid (V2G), grid-to-battery storage system (G2BSS), battery storage system-to-grid (BSS2G), battery storage system-to-vehicle (BSS2V), vehicle-to-battery storage system (V2BSS) and vehicle-to-vehicle (V2V) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate (AdapPID) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.

In this study, the energy management and switching control of plug-in hybrid electric vehicles (PHEVs) in a hybrid smart micro-grid system was designed. The charging station in this research consists of real market PHEVs of different companies with different sizes. The rate of charging of PHEVs is managed via switching control to receive maximum benefits from renewable energy sources and reduce the consumption of electricity from the grid. To support the optimum utilization of sustainable power, charging time and network stability, seven scenarios were developed for different interaction among the proposed micro-grid system and PHEVs. The proposed hybrid smart micro-grid system consists of three renewable energy sources: photovoltaic (PV) array controlled via an intelligent fuzzy control maximum power point subsystem, a fuel cell stack and a microturbine set controlled by proportional integral differential/proportional integral subsystems. A hybrid energy storage system (super-capacitor, battery storage bank and hydrogen) and residential load are also included in the proposed architecture. The hybrid smart micro-grid system is checked in terms of voltage regulation, frequency deviation and total harmonic distortion (THD). It was found that these are in limits according to the international standards. The simulations verify the feasibility of the proposed system and fulfill the requirement of vehicle-to-grid and grid-to-vehicle operations in a smart grid environment.

In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT.

Integration of different energy sources and power converters is required to meet the load demands adequately under various natural conditions. This research work focuses on the hybrid power system combining renewable energy sources, namely, a photovoltaic (PV) array and a solid oxide fuel cell (SOFC) and a hybrid energy storage system, i.e., a battery bank and hydrogen storage tanks in the proposed architecture. The complete layout is connected to the national grid via power electronics converters to enhance the continuity and reliability of power. In the proposed system, the PV is taken as the primary energy source to satisfy the load demands. The fuel cell and electrolyzer are added to ensure long-term energy balance by using the hydrogen technology. The battery is utilized as a high energy density device to keep the DC-bus voltage constant. The dynamic behaviour of the proposed system is checked under different solar radiation, temperature and load conditions for the simulation of 24 Hrs. The proposed system exhibits excellent performance in terms of grid stability and voltage regulation. All the energy sources and their controllers are designed in Matlab/Simulink.

Renewable energy sources experience problems such as deregulation when they are used as stand-alone energy sources. This paper presents an optimal power sharing and power control strategy combining a photovoltaic (PV) array, a fuel cell (FC) stack, an ultra-capacitor (UC) module, and a set of loads. The photovoltaic is the prior energy source while the fuel cell (FC) system is added as a backup source to meet the excess power demand. The ultra-capacitor (UC) is utilized as a buffer storage to compensate the slow dynamic response of the FC during transient and regulate the DC-bus voltage. The power control strategy is designed to work on a two-level arrangement. The top level controls the entire power management, which generates references to low level individual subsystems depending upon solar radiation, temperature, and load conditions. Based on the command signals, each local controller controls the PV, FC, electrolyzer, and UC. The top level also controls the load scheduling during low solar radiation in order to sustain the system operation for 24 h. The performance of the system is tested under real-world record of solar radiation, temperature, and load conditions for Bahria town at Islamabad, Pakistan. The effectiveness of the proposed model in terms of voltage regulation, power transfer, load tracking, and grid stability is verified by Matlab simulation results.