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An increase in human activities and population growth have significantly increased the world’s energy demands. The major source of energy for the world today is from fossil fuels, which are polluting and degrading the environment due to the emission of greenhouse gases. Hydrogen is an identified efficient energy carrier and can be obtained through renewable and non-renewable sources. An overview of renewable sources of hydrogen production which focuses on water splitting (electrolysis, thermolysis, and photolysis) and biomass (biological and thermochemical) mechanisms is presented in this study. The limitations associated with these mechanisms are discussed. The study also looks at some critical factors that hinders the scaling up of the hydrogen economy globally. Key among these factors are issues relating to the absence of a value chain for clean hydrogen, storage and transportation of hydrogen, high cost of production, lack of international standards, and risks in investment. The study ends with some future research recommendations for researchers to help enhance the technical efficiencies of some production mechanisms, and policy direction to governments to reduce investment risks in the sector to scale the hydrogen economy up.

In simulation studies, the precision of fuel cell models has a vital role in the quality of results. Unfortunately, due to the shortage of manufacturer data given in the datasheets, several unknown parameters should be defined to establish the fuel cell model for further precise analysis. This research addresses a novel application of the atom search optimization (ASO) algorithm to generate these unknown parameters of the fuel cell model and in particular of the polymer exchange membrane (PEM) type. The objective of this study is to establish an accurate model of the PEM fuel cells, which will provide accurate results of modeling and simulation in a steady-state condition. Simulations and further demonstrations were performed under MATLAB/SIMULINK. The viability of the proposed models was appraised by comparing its simulation results with the experimental results of number of commercial PEM fuel cells. In the same context, the obtained numerical results by the proposed ASO-based method were compared to other challenging optimization methods-based results. Finally, parametric tests were made which indicated the robustness of the ASO results as well. It can be stated here that ASO performs well and has a good capability to extract the unknown parameters with lesser errors.

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In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO2 emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO2 emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution.

This study presents an innovative strategy for load frequency control (LFC) using a combination structure of tilt-derivative and tilt-integral gains to form a TD-TI controller. Furthermore, a new improved optimization technique, namely the quantum chaos game optimizer (QCGO) is applied to tune the gains of the proposed combination TD-TI controller in two-area interconnected hybrid power systems, while the effectiveness of the proposed QCGO is validated via a comparison of its performance with the traditional CGO and other optimizers when considering 23 bench functions. Correspondingly, the effectiveness of the proposed controller is validated by comparing its performance with other controllers, such as the proportional-integral-derivative (PID) controller based on different optimizers, the tilt-integral-derivative (TID) controller based on a CGO algorithm, and the TID controller based on a QCGO algorithm, where the effectiveness of the proposed TD-TI controller based on the QCGO algorithm is ensured using different load patterns (i.e., step load perturbation (SLP), series SLP, and random load variation (RLV)). Furthermore, the challenges of renewable energy penetration and communication time delay are considered to test the robustness of the proposed controller in achieving more system stability. In addition, the integration of electric vehicles as dispersed energy storage units in both areas has been considered to test their effectiveness in achieving power grid stability. The simulation results elucidate that the proposed TD-TI controller based on the QCGO controller can achieve more system stability under the different aforementioned challenges.

Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

This present study describes a novel manta ray foraging optimization approach based non-dominated sorting strategy, namely (NSMRFO), for solving the multi-objective optimization problems (MOPs). The proposed powerful optimizer can efficiently achieve good convergence and distribution in both the search and objective spaces. In the NSMRFO algorithm, the elitist non-dominated sorting mechanism is followed. Afterwards, a crowding distance with a non-dominated ranking method is integrated for the purpose of archiving the Pareto front and improving the optimal solutions coverage. To judge the NSMRFO performances, a bunch of test functions are carried out including classical unconstrained and constrained functions, a recent benchmark suite known as the completions on evolutionary computation 2020 (CEC2020) that contains twenty-four multimodal optimization problems (MMOPs), some engineering design problems, and also the modified real-world issue known as IEEE 30-bus optimal power flow involving the wind/solar/small-hydro power generations. Comparison findings with multimodal multi-objective evolutionary algorithms (MMMOEAs) and other existing multi-objective approaches with respect to performance indicators reveal the NSMRFO ability to balance between the coverage and convergence towards the true Pareto front (PF) and Pareto optimal sets (PSs). Thus, the competing algorithms fail in providing better solutions while the proposed NSMRFO optimizer is able to attain almost all the Pareto optimal solutions.