To augment the photovoltaic (PV) power generation conversion, a maximum power point tracking (MPPT) technique plays a very significant role. This article introduces a hybrid MPPT algorithm integrating modified invasive weed optimization (MIWO) and perturb and observe (P&O) technique under the rapid weather change and partial shading scenarios for the efficient extraction of the maximum power from the standalone PV-based hybrid system. MIWO handles the initial stages of MPPT followed by the application of the P&O algorithm at the final stages in view of acquiring the rapid global peak and maximal PV power. The studied microgrid comprises of the PV system, battery, electrolyzer, fuel cell, and load. A coordinated dc-voltage regulation and power management strategy between each subsystem of the hybrid microgrid is implemented to save the battery from the undesirable charging/discharging operation. Additionally, with the monitoring of dc voltage, the dc/dc converter associated with the battery and dc link plays as an MPPT circuit of the PV without the requirement of an extra dedicated circuit. Takagi–Sugeno (TS) fuzzy controller is adopted for suppressing/mitigating the voltage oscillations of the microgrid during the variations in the solar irradiance/temperature and power demand. The results clearly exhibit the superior performance of the proposed methodology compared with some of the existing techniques.

The increasing adoption of renewable energy sources (RES), such as solar photovoltaics and wind turbines, is transforming electricity generation. However, integrating RES within DC microgrids (DCM) for applications such as fast DC charging in electric vehicles (EVs) presents challenges, including low inertia, power fluctuations, and voltage instability. This study addresses these challenges with novel control strategies and optimization algorithms. A hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach is used to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. In addition, a comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. Crucially, a hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging.

This study presents an improved power management control strategy of a hybrid direct current (DC) micro‐grid (MG) system consisting of photovoltaic cell, wind turbine generator, battery energy storage (BES), fuel cell (FC), and electrolyser. Based on the voltage and state of charge of BES, FC, and electrolyser, the proposed control scheme improved the dynamics of the DC‐link voltage and contributes a better power management between each generation/source and load. A gain control technique is implemented in the grid‐side inverter controller to regulate the modulation index and improving the voltage stability of the DC‐link. Furthermore, the PI‐controller gains of BES are tuned dynamically based on the deviation in voltage and its derivative using Takagi–Sugeno‐fuzzy control to enhance the transient response of the voltage. For a reliable operation of the DC MG under standalone or prolonged islanding mode of operation, a priority‐based load shedding algorithm is proposed for maintaining proper power coordination between different energy sources and storage devices. Owing to smoother and faster voltage response, the proposed control schemes can comply with the grid code requirements of the changing configuration of the modern renewable energy integrated DC MG. The effectiveness of the proposed control strategy is tested by comparing the existing scheme through MATLAB/Simulink ^® .