In this paper, a modified bald eagle search optimization algorithm was applied for the first time to determine the parameters of the triple diode model (TDM) of perovskite solar cells (PSCs). Two experimental datasets are considered; the first is measured I–V points for a PSC at standard conditions. The second consists of the measured I–V points for a modified PSC. In contrast, the cost function to be minimized is the root mean square error (RMSE) between the experimental dataset and the calculated one. To prove the superiority of modified bald eagle search optimization (mBES), a comparison with the original bald eagle search optimization (BES), particle swarm optimizer (PSO), Hunger games search (HGS), and recent Coronavirus Disease Optimization Algorithm (COVIDOA) was implemented. Furthermore, statistical analysis of ANOVA and Tukey tests was performed. The results demonstrate the lead of the recommended mBES in identifying the parameters of the TDM for PSCs, where the RMSE achieved the least value among the used optimization algorithms in this study.

Perovskite solar cells (PSCs) are third-generation photovoltaic technology that has gained a lot of attention due to its technological and economic advantages. The PSCs are characterized with low cost, high absorption, long carrier diffusion length, and low fabrication temperature. To model the performance of PSCs, a new dynamic model is proposed to track the cells hysteresis by adding a variable voltage capacitor to the single, double, and triple diode models. The aim with adding the variable capacitor is to emulate the effect of charge accumulation at the interfaces of the device. Three modified models are presented based on one or more variable capacitors at single, double, and triple diode models. Where, the triple diode model with third order variable voltage capacitor model is the best among all models by 74.9% enhancement in comparison with single diode model. Also, the Equilibrium optimization algorithm (EOA) is proposed to determine the parameters of the proposed dynamic model based on the triple diode model. In comparison with the experimental data, for both forward and reverse scans, the findings showed that the suggested model accurately reflects cell performance. Added to that, the EOA finds well the optimal model parameters considering the root mean square as primary objective function. Based on the simulation results, it was proved that the proposed model gives very close results to reality.

The proton exchange membrane fuel cell (PEMFC) is a green energy converter that is based on the chemical reaction process. The behavior of this system can change with time due to aging and operating conditions. Knowing the current state of this system requires an accurate model, and an exact PEMFC model requires precise parameters. These parameters should be identified and used to properly fit the polarization curve in order to effectively replicate the PEMFC behavior. This work suggests a precise unknown PEMFC parameter extraction based on a new metaheuristic optimization algorithm called the modified bald eagle search algorithm (mBES). The mBES is an optimization algorithm based on the principles of bald eagle behavior that combines local search and global search to achieve a balance between the exploration and exploitation of search spaces. It is a powerful and efficient technique for optimization problems where accurate and near-optimal solutions are desired. To approve the accuracy of the proposed identification approach, the proposed algorithm is compared to the following metaheuristic algorithms: bald eagle search algorithm (BES), artificial ecosystem-based optimization (AEO), leader Harris Hawk’s optimization (LHHO), rain optimization algorithm (ROA), sine cosine algorithm (SCA), and salp swarm algorithm (SSA). This evaluation process is applied to two commercialized PEMFC stacks: BCS 500 W PEMFC and Avista SR-12 PEM. The extracted parameters’ accuracy is measured as the sum of square errors (SSE) between the results produced by the optimizer and the experimental data in the objective function. As a result, the proposed PEMFC optimizing model outperforms the comparison models in terms of system correctness and convergence. The proposed extraction strategy, mBES, obtained the best results, with a fitness value of 0.011364 for the 500 W BCS and 0.035099 for the Avista SR-12 500 W PEMFC.

Nowadays, water pumping systems based on photovoltaics as a source of electricity have widely increased. System cost and efficiency still require enhancement in order to spread their application. Perovskite solar cells (PSCs) are the most hopeful third-generation photovoltaic for replacing the silicon-based photovoltaic thanks to their high power conversion efficiency, reaching 25.8%; tunable band-gap; long diffusion length; low fabrication temperature; and low cost. In this work, for the first time, we proposed a high-power-density hybrid perovskite solar cell thermoelectric generator (TEG) array for feeding a synchronous reluctance motor (SynRM) driving a water pump for use in an irrigation system. A control technique was used to achieve two functions. The first function was driving the motor to obtain the maximum torque/ampere. The second was harvesting the maximum perovskite solar cell array output power on the basis of the maximum power point tracking (MPPT) algorithm using the perturbation and observation approach. Thus, the proposed hybrid perovskite solar cell–thermoelectric generator feeds the motor via an inverter without DC–DC converters or batteries. Accordingly, the short life problems and the high replacement cost are avoided. The proposed complete system was simulated via the MATLAB package. Moreover, a complete laboratory infrastructure was constructed for testing the proposed high-power-density hybrid perovskite solar cell–TEG array for the water pumping system. The results revealed that using the high-power-density hybrid perovskite solar cell–TEG array, both the motor’s output power and the pump’s flow rate were improved by 11% and 14%, respectively, compared to only using the perovskite solar cell array. Finally, both the simulation and experimental results proved the high-performance efficiency of the system in addition to showing its system complexity and cost reduction.

The reliable mathematical model construction of dye-sensitized solar cells (DSSCs) using the triple-diode model (TDM) is proposed but it is a challenge due to its complexity. This work implements a novel method incorporating a recent meta-heuristic optimizer called the heterogeneous marine predators algorithm (H-MPA) to identify the nine parameters of the triple-diode equivalent circuit of DSSCs. In the optimization procedure, the nine unknown parameters of TDM are employed as decision variables, but the objective function to be minimized is the root mean square error (RMSE) between the experimental data and the estimated data. To prove the superiority of the H-MPA, the obtained results are compared with the slime mold algorithm (SMA), Transient search optimizer (TSO), Manta-Ray Foraging Optimization algorithm (MRFO), Forensic-Based Investigation (FBI), Equilibrium optimizer (EO), and Artificial ecosystem-based optimizer. The primary findings demonstrated the superiority of the proposed strategy in building a consistent model of the triple-diode model of DSSCs.

This work proposes the techno-economic analysis of a three-phase, three wire grid integrated third-generation perovskite solar cells-wind energy conversion system (PSC-WECS) controlled by a two-level voltage source converter (VSC) which is driven by the application of polynomial zero-attracting least mean square (PZA-LMS) algorithm. The proposed algorithm enhances the dynamic performance of the system by introducing sinusoidal grid currents with low total harmonic distortion (THD) which enhances system performance by providing acceptable outcomes under dynamic loading, fluctuating wind velocity, and solar insolation by compensating for load reactive power and balancing load and power demands at the point of coupling. This work replaces the conventional silicon PVs with PSCs to improve the economics of power generation thanks to their higher efficiency. For relative comparisons, the performance of the proposed 36kWPSC-WECS is compared with the conventional silicon PV-WECS. The results reveal that the proposed system has a low net present cost (NPC) of $68843 compared to the conventional system which costs $160917, translating to a reduction of 58% in total cost. Sensitivity Analysis (SA) is performed to analyse the dependence of optimal solutions on the uncertainty of key variables namely NPC, cost of energy (COE), windspeed & insolation. The results show that the total NPC is more sensitive to the COE and the average wind speed as compared to the other variables. Moreover, the area covered by the proposed system is reduced by 50% while as the cost of power generation is reduced by 72% due to the high efficiency of PSCs. The results obtained in MATLAB/Simulink and HOMER to determine the optimum quantity of the renewable energy sources (RES) model demonstrate the effectiveness of the recommended system.