In this paper, the utility grid is integrated with hybrid photovoltaic (PV)/wind/fuel cells to overcome the unavailability of the grid and the single implementation of renewable energy. The main purpose of this study is smart management of hydrogen storage tanks and power exchange between the hybrid renewable energy and the grid to minimize the total cost of the hybrid system and load uncertainties. PV and wind act as the main renewable energy sources, whereas fuel cells act as auxiliary sources designed to compensate for power variations and to ensure continuous power flow to the load. The grid is considered a backup system that works when hybrid renewable energy and fuel cells are unavailable. In this study, the optimal size of the components of the hybrid energy system is introduced using two methods: the marine predators’ algorithm (MPA) and the seagull optimization algorithm (SOA). The optimal sizing problem is also run accounting for the uncertainty in load demand. The results obtained from the proposed optimization are given with and without uncertainty in load demand. The simulation results of the hybrid system without uncertainty demonstrate the superiority of the MPA compared with SOA. However, in the case of load uncertainty, the simulation results (the uncertainty) are given using the MPA optimization technique with +5%, +10%, and +15% uncertainty in load, which showed that the net present cost and purchase energy are increased with uncertainty.

Realising an accurate estimation of model parameters for solar cells and the Photovoltaic modules has serious importance for enhancing the performance of their control systems. Three neoteric metaheuristic methods of Artificial Ecosystem-based optimisation, Coot Bird-based optimisation, and Equilibrium optimiser have been applied and evaluated concerning the accurate estimation of various Photovoltaic models. The validation of the applied methods has occurred for valuing the model parameters of R.T.C. France solar cell, and Thin-film ST40 Photovoltaic module. The objective function has been formulated as the Root Mean Square Error between the actual and estimated data. Matlab/Simulink has been used for the verification of the optimisation methods. The outcomes demonstrate that: (1) The three optimisation algorithms can resolve the problem of the Photovoltaic parameter estimation; (2) There are small distinctions between the three algorithms concerning their best value of the impartial function; this distinction between the best and worst algorithm is 10-9 for R.T.C. France solar cell for SDM; (3) The best algorithm considering the best value of the objective function is Artificial Ecosystem-based optimisation for R.T.C. France solar cell; (4) Statistical results prove that the three algorithms have tracking efficiencies of 100%, 99.999%, and 98.285% for Artificial Ecosystem-based optimisation, Coot Bird-based optimisation, and Equilibrium optimiser, respectively, based on 10 individual runs for R.T.C. France solar cell for SDM. Moreover, the simulation results show that the I/V curves obtained employing Artificial Ecosystem-based optimisation, Coot Bird-based optimisation, and Equilibrium optimiser techniques were also matched with the corresponding datasheet curves with the Artificial Ecosystem-based optimisation and Coot Bird-based optimisation's predominance in standings the convergence speed, tracking efficiency, statistical indices, and solution accuracy.

Many studies have been made in the field of load frequency control (LFC) through the last few decades because of its importance to healthy power system. It is important to maintain frequency deviation at zero level after a load perturbation. In decentralized control, the multi-area power system is decomposed into many single input single output (SISO) subsystems and a local controller is designed for each subsystem. The controlled subsystems may have slow poles; these undesired poles may drive the aggregated overall system into the instability region. Thus, it is required to relocate these poles to much more stable places to avoid their effect upon the overall system stability. This study aims to design a new load frequency controller based on the powerful optimal linear quadratic regulator (LQR) technique. This technique can be applied over subsystem level to shift each subsystem undesired poles one by one into a prespecified stable location which in turn shift the overall system slow poles and reduce the effect of the interaction between the interconnected subsystems among each other. This procedure must be applied many times as the number of undesired poles (pairs) until all the desired poles are achieved. The main objective is considered to get a robust design when the system is affected by a physical disturbance and ±40% model uncertainties. LQR can be applied again over the aggregated system to enhance the stability degree. Simulation results are used to evaluate the effectiveness of the proposed method and compared to other research results.

In this paper, the neoteric metaheuristic methods of artificial ecosystem-based optimization (AEO), Coot Bird-based optimization (COOT), Equilibrium optimizer (EQO), Runge-Kutta Method (RUN), Hunger games search (HGS), and Weighted Mean of Vectors (INFO) have been applied and evaluated to discover a preferable estimation for the Proton Exchange Membrane Fuel Cells (PEMFCs) model. The validation of the applied methods has been done for valuing the model parameters of BCS 500W-PEM, 500W-SR-12PEM, and 250W-stack. The objective function has been formulated as the sum of square errors (SSEs) between the measured and estimated data. MATLAB has been used for the verification of the optimization techniques. The results show that for BCS 500W-PEM:(1) the six algorithms can accurately solve the problem of the FC parameter assessment;(2) there are small distinguished between the six algorithms concerning their best value of the objective function; this distinction between the best and worst techniques is 2.4 × 10 −9 ; (3) the best algorithm is INFO with 0.011556306 while the worst algorithm is RUN with 0.011556308; (4) statistical results prove that the six algorithms have the tracking efficiencies of 99.99961329%, 99.97114568%, 99.83529736%, 100%, 99.89195368%, and 99.90200833% for AEO, COOT, EQO, INFO, HGS, and RUN, respectively based on 30 individual runs.