Photovoltaic (PV) is a highly promising energy source because of its environment friendly property. However, there is an uncertainty present in the modeling of PV modules owing to varying irradiance and temperature. To solve such uncertainty, the fuzzy logic control-based intelligent maximum power point tracking (MPPT) method is observed to be more suitable as compared with conventional algorithms in PV systems. In this paper, an isolated PV system using a push pull converter with the fuzzy logic-based MPPT algorithm is presented. The proposed methodology optimizes the output power of PV modules and achieves isolation with high DC gain. The DC gain is inverted into a single phase AC through a closed loop fuzzy logic inverter with a low pass filter to reduce the total harmonic distortion (THD). Dynamic simulations are developed in Matlab/Simulink by MathWorks under linear loads. The results show that the fuzzy logic algorithms of the proposed system efficiently track the MPPT and present reduced THD.

With the increasing demand for electrical energy and the challenges related to its production, along with the need to be environmentally friendly to achieve sustainability for future generations, proton exchange membrane fuel cells (PEMFCs) are emerging as a clean energy source that can effectively replace conventional energy sources, in various fields of application and especially in the field of transportation exploiting electric vehicles (EVs). To improve the development and control of the PEMFCs, the precise determination of its mathematical model remains an essential task. Indeed, the accuracy of such a model depends on the ability to overcome the constraints associated with the nonlinearity and the numerous involved unknown parameters. The present paper proposes a new Dandelion Optimizer (DO) to accurately identify, for the first time, the parameters of the PEMFC model. The DO addresses the weaknesses of the majority of metaheuristic algorithms related to the self-adaptation of parameters, the stagnation of convergence to local minima, and the ability to refer to the whole population. The high ability of the proposed method is investigated using both steady-state and dynamic situations. The DO-based parameters estimation approach has been assessed through a specific comparative study with the most recently published techniques including GWO, GBO, HHO, IAEO, VSDE, and ABCDESC is performed using two typical PEMFC modules, namely 250 W PEMFC and NedStack PS6. The results obtained proved that the proposed approach obtained promising achievements and better performances comparatively with well-recognized and competitive methods.

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 $\times {10}^{-3}$, 1.387641 $\times {10}^{-3}$, 1.719946 $\times {10}^{-3}$, and 1.686104 $\times {10}^{-3}$, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.