• Energy Research
• 2021-2025close
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The Galapagos Islands have been declared a World Heritage site due to their unique biodiversity, which makes them a living museum and a natural laboratory for humankind. However, to fulfill the energy needs of its habitants and foreign visitors, the islands have depended on fossil fuel energies that have produced levels of lead and chemical agents that are affecting the islands’ air quality, flora, and fauna. Therefore, zero-carbon initiatives have been created to protect the islands, wherein solar and wind power plants have been studied as reliable alternatives. In this way, Geographical Information Systems based on Multicriteria Decision Methods constitute a methodology that minimizes the destruction and disturbance of nature in order to assess the best location for the implementation of these alternative energy sources. Therefore, by exploring the geographical information along with the Analytical Hierarchical Processes and the Ordered Weighted Average methods, it was possible to identify the potential for solar power plants of 10 MW on each island; likewise, for wind power plants, it was found that the islands possess implementation potential that has been analyzed in the field, showing that the best location is on Baltra Island, but is not limited to it.

Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

The proposed anthraquinone-bromate cell combines the advantages of anthraquinone-bromine redox flow batteries and novel hybrid hydrogen-bromate flow batteries. The anthraquinone-2,7-disulfonic acid is of interest as a promising organic negolyte due its high solubility, rapid kinetics of electrode reactions and suitable redox potentials combined with a high chemical stability during redox reactions. Lithium or sodium bromates as posolytes provide an anomalously high discharge current density of order ~A cm−2 due to a novel autocatalytic mechanism. Combining these two systems, we developed a single cell of novel anthraquinone-bromate flow battery, which showed a power density of 1.08 W cm−2, energy density of 16.1 W h L−1 and energy efficiency of 72% after 10 charge–discharge cycles.

Marine fuel oil stability has always been an issue for bunkering companies and ship owners all around the world and the problem has become even more apparent with the introduction of the Global Sulphur Gap by the International Maritime Organization (IMO) in 2020. In this article, the historical background and the technical reasons why marine fuel oils lose their stability, as well as methods for preventing such instability from occurring, are presented. While it is possible to make fuel compositions stable by adjusting their composition in such a way that the components of the fuel are compatible, considering that marine fuel oils are often comprised of the least value-added products, the method of adding special fuel oil stabilizers (also known as “asphaltene dispersants”) is usually preferred. An overview of such stabilizers is presented; their chemical composition, based on the information provided by the manufacturers and/or inventors is studied. In addition, the experimental research of the produced marine fuel oil and its components is carried out. The results of the model composition studies show that adding even as little as 10% of residual asphaltene-rich components can make a composition with a high stability reserve unstable. It was also shown that the content of the asphaltene-rich component in a stable fuel can be increased from 3% to 10% by introducing stabilizers in low amounts (up to 2000 ppm), thus lowering the amount of higher value-added, mostly naphthene-paraffinic-based components. Different methods of fuel stability evaluation were studied and tested, most of them being in correlation with one another. Several types of stability enhancers were tried out on unstable fuel, with stabilizers based on alkylphenol formaldehyde resin showing the best results.

The beginning of the 21st century for the fuel and energy complexes of most countries is characterized as a period of active restructuring and a fundamental shift in developmental priorities. The basis of these changes is technological development. Industry 4.0 technologies have particular importance in achieving maximum optimization of production processes. In the same way, they are applicable in establishing effective interaction between the energy sector and other sectors of the economy. The authors outline an approach to assessing the country’s fuel and energy balance state through the selected properties: sustainability, accessibility, efficiency, adaptability and reliability. Hence, a model of the fuel and energy complex was created on the example of the Russian Federation, considering the country’s territorial and functional division. The methodology is based on scenario modeling of the influence level of external challenges in conjunction with the accompanying technological development. The mathematical model allowed forecasting changes in the properties of the energy system. The scientific significance of the work lies in the application of a consistent hybrid modeling approach to forecast the state of the fuel and energy balance. The results of the study are useful in compiling scenarios for the regional and entire development of the fuel and energy complex. Further model improvements should include an expansion of the number of counted industries and their relations.

The radioactive gas radon is ubiquitous in the environment and is a major contributor to the human inhalation dose. It is the second leading cause of lung cancer after smoking. Radon concentrations are particularly high in the air of radon-hazardous facilities—uranium mines and radioactive waste repositories containing radium. To reduce the dose load on the staff, air in these premises should be continuously or periodically purified of radon. Carbon adsorbers can be successfully used for this purpose. The design of sorption systems requires information on both equilibrium and kinetic parameters of radon dynamic adsorption. The traditional way of obtaining such characteristics of the sorbent is to analyze the breakthrough curves. The present paper proposes a simple alternative method for determining parameters of dynamic radon adsorption (Henry’s constant and equilibrium adsorption layer thickness) from the results of a layer-by-layer gamma-spectrometric measurement of the sorbent. The analytical equation for smooth distribution of radon activity in the sorbent layer is obtained based on equilibrium adsorption layer theory for elute chromatography (pulsed injection of radon into the column). Using the dynamic adsorption of 222Rn on AG-3 activated carbon as an example, both equilibrium (Henry’s constant) and kinetic (thickness of the equilibrium adsorption layer) parameters of the adsorption dynamics were calculated. It was shown that the exposure duration of the column bed in the air flow and superficial gas velocity do not affect the result of the Henry’s constant calculation. The dependence of the equilibrium adsorption layer thickness on the superficial gas velocity over a wide range of values (5–220 cm/min) is described by the van Deemter equation. It was shown that the optimum air flow velocity, which corresponds to the maximum effectiveness of the bed, is 15–30 cm/min. This corresponds to the minimum of the equilibrium adsorption layer thickness (about 0.6 cm). The developed mathematical model makes it easy to define both equilibrium and kinetic parameters of dynamic adsorption of radon based on discrete distribution of its activity over the sections of the adsorption column. These parameters can then be used to calculate and design gas delay systems. It can be useful for studying the sorption capacity of various materials relative to radon.

The transformation of energy markets is at a crossroads in the search for how they must evolve to become ecologically friendly systems and meet the growing energy demand. Currently, methodologies based on bibliographic data analysis are supported by information and communication technologies and have become necessary. More sophisticated processes are being used in energy systems, including new digitalization models, particularly driven by artificial intelligence (AI) technology. In the present bibliographic review, 342 documents indexed in Scopus have been identified that promote synergies between AI and the energy transition (ET), considering a time range from 1990 to 2024. The analysis methodology includes an evaluation of keywords related to the areas of AI and ET. The analyses extend to a review by authorship, co-authorship, and areas of AI’s influence in energy system subareas. The integration of energy resources, including supply and demand, in which renewable energy sources play a leading role at the end-customer level, now conceived as both producer and consumer, is intensively studied. The results identified that AI has experienced notable growth in the last five years and will undoubtedly play a leading role in the future in achieving decarbonization goals. Among the applications that it will enable will be the design of new energy markets up to the execution and start-up of new power plants with energy control and optimization. This study aims to present a baseline that allows researchers, legislators, and government decision-makers to compare their benefits, ambitions, strategies, and novel applications for formulating AI policies in the energy field. The developments and scope of AI in the energy sector were explored in relation to the AI domain in parts of the energy supply chain. While these processes involve complex data analysis, AI techniques provide powerful solutions for designing and managing energy markets with high renewable energy penetration. This integration of AI with energy systems represents a fundamental shift in market design, enabling more efficient and sustainable energy transitions. Future lines of research could focus on energy demand forecasting, dynamic adjustments in energy distribution between different generation sources, energy storage, and usage optimization.

Rising levels of non-synchronous generation in power systems are leading to increasing difficulties in primary frequency control. In response, there has been much research effort aimed at providing individual electronic interfaced generators with different frequency response capabilities. There is now a growing research interest in analyzing the interactions among different power system elements that include these features. This paper explores how the implementation of control strategies based on the concept of virtual inertia can help to improve frequency stability. More specifically, the work is focused on islanded systems with high share of wind generation interacting with battery energy storage systems. The paper presents a methodology for modeling a power system with virtual primary frequency control, as an aid to power system planning and operation. The methodology and its implementation are illustrated with a real case study.