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 this paper, improved single- and multi-objective Harris Hawks Optimization algorithms, called IHHO and MOIHHO, respectively are proposed and applied for determining the optimal placement of distribution generation (DG) in the radial distribution systems. Harris hawks optimizer (HHO) is a anew inspired meta-heuristic optimization technique that is mainly based on the intelligence behavior of the Harris hawks in chasing prey. The IHHO and MOIHHO are applied for determining the optimal size and location of DG with the aim of minimizing the total active power loss, reducing the voltage deviation (VD), and increasing the voltage stability index (VSI) with considering operational constraints of distribution system. In IHHO, the performance of conventional HHO algorithm is improved using the rabbit location instead of the random location. In MOIHHO, a developed grey relation analysis is applied for identifying the best compromise solution among the non-dominance Pareto solutions. To verify the effectiveness of the proposed algorithms, IEEE 33-bus and IEEE 69-bus radial distribution systems are used, and the obtained results are compared with the other optimization techniques which utilized for the same problem. The results prove the efficiency of the proposed algorithms in terms of best solutions obtained so far for the single- and multi-objective scenarios.

In recent years, multilevel inverters (MLIs) have emerged to be the most empowered power transformation technology for numerous operations such as renewable energy resources (RERs), flexible AC transmission systems (FACTS), electric motor drives, etc. MLI has gained popularity in medium- to high-power operations because of numerous merits such as minimum harmonic contents, less dissipation of power from power electronic switches, and less electromagnetic interference (EMI) at the receiving end. The MLI possesses many essential advantages in comparison to a conventional two-level inverter, such as voltage profile enhancement, increased efficiency of the overall system, the capability of high-quality output generation with the reduced switching frequency, decreased total harmonic distortions (THD) without reducing the power of the inverter and use of very low ratings of the device. Although classical MLIs find their use in various vital key areas, newer MLI configurations have an expanding concern to the limited count of power electronic devices, gate drivers, and isolated DC sources. In this review article, an attempt has been made to focus on various aspects of MLIs such as different configurations, modulation techniques, the concept of new reduced switch count MLI topologies, applications regarding interface with renewable energy, motor drives, and FACTS controller. Further, deep insights for future prospective towards hassle-free addition of MLI technology towards more enhanced application for various fields of the power system have also been discussed. This article is believed to be extremely helpful for academics, researchers, and industrialists working in the direction of MLI technology.

In this paper, an efficient optimization technique called Chaotic Harris Hawks optimization (CHHO) is proposed and applied for estimating the accurate operating parameters of proton exchange membrane fuel cell (PEMFC), which simulate and mimic its electrical performance. The conventional Harris Hawks optimization (HHO) is a recent optimization technique that is based on the hunting approach of Harris hawks. In this proposed optimization technique, ten chaotic functions are applied for tackling with the studied optimization problem. The CHHO is proposed to enhance the search capability of conventional HHO and avoid its trapping into local optima. The sum of squared errors (SSE) between the experimentally measured output voltage and the corresponding simulated ones is adopted as the objective function. The developed CHHO technique is tested on four various commercial PEMFC stacks to assess and validate its effectiveness compared with other well-known optimization techniques. A statistical study is performed to appreciate the stability and reliability of the proposed CHHO technique. However, the results show the effectiveness and superiority of proposed CHHO compared with the conventional HHO and other competitive metaheuristic optimization algorithms under the same study cases.

Technological innovations have been a matter of contention, and their environmental consequences remain unresolved. Moreover, studies have extensively evaluated environmental challenges using metrics such as nitrogen oxide emissions, sulfur dioxide, carbon emissions, and ecological footprint. The environment has the supply and demand aspect, which is not a component of any of these indicators. By measuring biocapacity and ecological footprint, the load capacity factor follows a certain ecological threshold, allowing for a thorough study on environmental deterioration. With the reduction in load capacity factor, the environmental deterioration increases. In the context of the environment, the interaction between technological innovation and load capacity covers the demand and supply side of the environment. In light of this, employing the dataset ranging from 1980 to 2017 for the case of South Africa, the bound cointegration test in conjunction with the critical value of Kripfganz and Schneider showed cointegration in the model. The study also employed the ARDL, whose outcome revealed that nonrenewable energy usage and economic growth contribute to environmental deterioration, whereas technological innovation and globalization improve the quality of the environment. This study validated the hypothesis of the environmental Kuznets curve for South Africa, as the short-term coefficient value was lower than the long-term elasticity. Furthermore, using the frequency-domain causality test revealed that globalization and economic growth predict load capacity in the long term, and nonrenewable energy predicts load capacity factors in the long and medium term. In addition, technological innovation predicts load capacity factors in the short and long term. Based on the findings, we propose that policymakers should focus their efforts on increasing funding for the research and development of green technologies.