Solar hydrogen generation is one of the new topics in the field of renewable energy. Recently, the rate of investigation about hydrogen generation is growing dramatically in many countries. Many studies have been done about hydrogen generation from natural resources such as wind, solar, coal etc. In this work we evaluated global scientific production of solar hydrogen generation papers from 2001 to 2014 in any journal of all the subject categories of the Science Citation Index compiled by Institute for Scientific Information (ISI), Philadelphia, USA. Solar hydrogen generation was used as keywords to search the parts of titles, abstracts, or keywords. The published output analysis showed that hydrogen generation from the sun research steadily increased over the past 14 years and the annual paper production in 2013 was about three times 2010-paper production. The number of papers considered in this research is 141 which have been published from 2001 to this date. There are clear distinctions among author keywords used in publications from the five most high-publishing countries such as USA, China, Australia, Germany and India in solar hydrogen studies. In order to evaluate this work quantitative and qualitative analysis methods were used to the development of global scientific production in a specific research field. The analytical results eventually provide several key findings and consider the overview hydrogen production according to the solar hydrogen generation.
This paper presents a decentralized demand response (DR) framework that, incorporating optimal multi-objective energy management strategies, addresses uncertainties in power networks. The power industry faces challenges in operational optimization due to uncertainties in generation and consumption while evaluating environmental impacts and long-term economic implications. This research introduces an innovative approach by combining advanced DR techniques with a robust energy management strategy designed for uncertain conditions, enhanced by sensitivity analysis to key system parameters. The article considers a network with distributed generating resources, including wind turbines, microturbines, photovoltaics, energy storage systems (ESS), and diesel generators, where generation is controlled hourly based on load fluctuations. Energy consumption optimization requires not only distributed energy generation but also DR to variations in demand, ensuring system reliability under diverse scenarios. Consumers play a crucial role in optimizing energy usage through incentive-based participation. To achieve the research goal of reducing generation and purchasing costs in power grids through optimal energy management and DR to fluctuations, a stochastic approach is employed to obtain the best outcomes. This paper proposes a novel method for optimizing energy consumption in power networks by integrating stochastic techniques to manage uncertainties and variable conditions. The findings show improved network efficiency and cost reduction, achieving a 15.62% decrease in voltage deviation, 37.08% reduction in load demand, 62.05% decrease in active losses, 81.25% reduction in reactive losses, and 33-45% reduction in Expected Energy Not Supplied (EENS).