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Abstract Recent improvements in renewable energy sources (RESs) and their extensive use in the power industry have created significant issues for their operation, security, and management. Due to the ongoing reduction of power system inertia, maintaining operational frequency at its nominal value and minimizing tie-line power variations constitute essential variables for these improvements. A novel improved frequency stabilization approach based on modified fractional order tilt controller is presented for interconnected diverse power systems with integration of sea wave energy, photovoltaic, wind, energy storage system and biodiesel generator. The fractional order tilt integral double derivative (FOTIDD2) is a novel controller that is developed to address the frequency stabilization problems of a hybrid power structure that is integrated with sources of clean energy and devices for storing energy. A recent optimization algorithm inspired by human behavior called mother optimization algorithm (MOA) is applied to adjust the coefficient of the proposed cascaded controller. The FOTIDD2 controller was compared with other control methods in terms of its transient performance, in order to determine its effectiveness. Further, the mother optimization algorithm results are compared with those of sine cosine algorithm (SCA), fox optimization algorithm (FOA), and grey wolf optimization (GWO). FOTIDD2 controller was found to be more effective than PID controller in respect of reducing overshoot (Osh) by 79.31%, 83.78% and 67.99%, improving time settlement by 33.22%, 29.87%, and 22.45% and reducing undershoot (Ush) by 79.13%, 89.34%, and 86.90% for the (∆F1), (∆F2), and (∆Ptie), respectively.

One of the most important energy sources is non-renewable sources, including fossil fuels, the release of which causes many harmful consequences, including respiratory diseases, growth disorders, etc. On the other hand, renewable energies, including water, wind, sun, etc., are sustainable energy sources and compared to traditional fossil fuels, they have less adverse environmental effects due to the minimal emission of greenhouse gases. Due to the need to use renewable energy to reduce greenhouse gas emissions and combat climate change, the widespread use of these resources will bring about sustainable development. Therefore, creating the necessary infrastructure to use renewable energy as an important solution plays a key role in preserving natural resources. The purpose of this study is to review green and renewable fuels; Their sources, applications, advantages, and limitations. In this narrative review study, review articles, research articles, and cross-sectional descriptions were published until the beginning of February 2023 in English and Persian related to green and renewable fuels; Sources, applications, advantages, and limitations were investigated. These articles were searched in ScienceDirect, PubMed, Google Scholar, Web of Science, Scopus, and SID databases, using keywords such as renewable energy, fossil fuel, green fuel, and non-renewable energy with the aim of better understanding green fuels; Their sources, application, advantages, and limitations were obtained. After searching the databases with the mentioned keywords, 78 articles were included in the study. After removing duplicate articles, studying the abstract, and finally the full text of the articles, 35 related articles were included in the study. Endnote X7 resource management software was also used to organize, study titles and abstracts, and identify duplicate articles. Gren fuel is a non-toxic fuel that is essentially free of sulfur and aromatic compounds and is produced through various technologies such as biological and thermochemical processes. All the reviewed articles in the direction of producing green fuels such as biodiesel, natural gas, and hydrogen have pointed to the reduction of greenhouse gas emissions, improvement of air quality, stability of combustion in engines with dual fuel performance, and reduction of the negative effects of air pollution on health. Green fuel production is promising, but it faces limitations and challenges such as the need for land, water, and other resources, which can lead to changes in land use patterns, deforestation, and biodiversity loss. Also, the costs of producing biofuels depend on important factors such as raw materials, land, agricultural inputs, subsidies, and production technology. Biofuels are usually more expensive than fossil fuels and their production requires initial investment and maintenance costs. Considering the limitations and challenges of producing or using green fuel, using these renewable energy sources requires environmental planning and policy. Green fuels and renewable energy sources have emerged as promising solutions to deal with the environmental challenges caused by traditional fossil fuels. In general, green fuels offer a glimpse of a more sustainable future. However, their environmental effects need to be carefully investigated. Hence, a comprehensive understanding of diverse green fuel production methods and renewable energy technologies, along with their potential ecological impacts, is necessary to ensure the alignment of these alternative energy sources to achieve sustainable development.

Research has concentrated on promoting the development of environmentally friendly energies since air pollution, energy crises, and global warming are main challenges in modern era. One effective way to reduce environmental damage is to use Renewable Energy Resources (RER). The prominence of RER is increasing due to escalating global energy challenges. Power converter development and application are closely associated to the deployment of RER. One major problem with these power converters is the presence of extremely high harmonic components in their output voltage. Various PWM techniques are employed to suppress harmonics and no singular PWM technique is universally applicable to all circumstances. Firstly, this review paper provides readers with in-depth explanation of the significance of harmonic suppression as well as presentation of several important PWM strategies. Then, the advantages and disadvantages are explored, and the methods are compared in terms of the number of harmonics suppressed by each PWM technique. Further, this review also discusses and describes harmonics suppression, PWM categorization, Single PWM, Multiple PWM, Sinusoidal PWM, Modified Sinusoidal PWM, SVPWM, Hysteresis PWM, Selective Harmonic Elimination PWM, and Multiple Carrier PWM, as well as their benefits and drawbacks. Tables with comparative assessment based on various parameters are provided for each PWM technique. Lastly, conclusion with a thorough presentation of future tendencies is provided.

Adopting renewable energies at the individual level is required to mitigate greenhouse gas emissions, reduce reliance on fossil fuels, and transition to a society based on green energy. This study taps into this topic by exploring why and which consumers would adopt renewable energies using two psychological approaches (i.e., protected values and the halo effect) and by looking at the role of consumers’ demographics in their willingness to pay for renewable energy. Evidence of a comfort halo effect was found for consumers with high protected values (i.e., with a moral orientation toward the environment) using two-instance repeated-measures linear regressions in an MTurk experiment: Those consumers are likely to perceive more comfort at home when they know their electricity is sourced from renewable energies. This effect does not hold for consumers who have low protected values. Consumers with high protected values were not willing to pay more for renewable energies, though. Conversely, consumers with low protected values were willing to pay a premium for non-renewable energy. Overall, consumers with high education expressed a willingness to pay more for renewable energies, but consumers with high income and younger consumers did not. This research highlights the importance of shifting from fossil fuels to a low-carbon future by stressing the role of the factors that may affect consumer decision making.

The global energy industry fundamentally transformed towards renewable energy sources, driven by the sustainability paradigm. This shift was crucial in addressing the challenges of climate change and resource scarcity. Machine Learning (ML) played a pivotal role in enhancing the efficiency and reliability of renewable energy systems. This study conducted a comprehensive analysis of scientific production at the intersection of ML and renewable energy generation, focusing on Latin America. Employing a methodology based on documentary research and bibliometric processes, utilizing the Scopus database with the support of R and VOSviewer software, our research revealed a significant increase in interest and investment in research related to ML and renewable energies since 2020. This exponential growth scenario in this knowledge area had significant implications for Latin America and the world, fostering technological advancements and the adoption of renewable energies. Countries such as China, India, the United States, South Korea, and Saudi Arabia represented 61% of the global scientific production in this field, underscoring its global relevance. This growth indicated a growing interest and investment in ML applications in renewable energies, aligning with the 2030 Agenda for Sustainable Development. This research aligns with the Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure). It contributed to progress toward a more sustainable future, benefiting society through more efficient and sustainable energy systems, the energy industry through increased innovation and the adoption of clean technologies, and Latin America, which could leverage these findings to sustainably drive its economic and environmental development.

Abstract With a high penetration of renewable energies, scenario generation for wind and solar power is essential for the operation of modern power systems. Beyond the typical scenarios, extreme scenarios like full‐capacity generation for consecutive days should also be taken into account. However, developed data‐driven methods are unlikely to capture the characteristics of these extreme scenarios because of limited data. To this end, a three‐staged extreme scenario generation method is proposed for renewable energies to effectively and efficiently generate extremely high power output scenarios. First, an extreme data augmentation algorithm is designed to push the original data distribution towards the extreme case. Then, based on the extreme value theory, the tail of the augmented dataset is modelled by Generalised Pareto Distribution (GPD). Last, with the augmented dataset and the conditional labels sampled from the fitted GPD, a conditional generative adversarial network is trained with the modified loss function. A case study on a real‐world dataset shows that the authors’ proposed method has superior performance over the state‐of‐the‐art generative models in terms of extreme scenario generation. Besides, the generated samples successfully capture the temporal and spatial correlation of real scenarios of renewable energies.

This paper shows an example of how to create morphological and geological maps of the seabed combining high-resolution bathymetric data and onshore geological information. The mapped area corresponds to the BIMEP offshore test site, located in the south-eastern part of the Bay of Biscay, in the northern part of the Iberian Peninsula. In the Morphological map, using semi-automatic methods, the seafloor is categorized in 10 classes, obtained by the combination of terrain variables and a decision table. In the Geological map, onshore-defined geological units are extended into the continental shelf, and bedding traces, folds, and fractures are depicted. These maps could serve as valuable tools for stakeholders involved in marine energy projects, enabling informed decision-making and promoting sustainable development practices.