Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems

Yoro, Kelvin O.; Daramola, Michael O.; Sekoai, Patrick T.; Wilson, Uwemedimo Nyong; Eterigho-Ikelegbe, Orevaoghene

doi:10.1016/j.rser.2021.111506

Cited by 54 publications

(19 citation statements)

References 131 publications

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“…Familiarity and technical expertise of the software's user determine to a high extent the simulations' outcome, necessitating their evaluation with due objectivity. Moreover, the software's accuracy is enhanced by the quality of data imported and their pertinent detail [107,108]. Due to the fact that the simulations performed were based on hourly distributions of solar irradiation and wind speed, they are considered as the most appropriate input data for the analysis of a pertinent project.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Renewable Energy Systems’ Optimisation. A Review and Extended Comparison of the Most-Used Software Tools

Kavadias

Triantafyllou

2021

Energies

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To help stakeholders plan, research, and develop Hybrid Renewable Energy Systems (HRES), the elaboration of numerous modelling techniques and software simulation tools has been reported. The thorough analysis of these undoubtedly complex systems is strongly correlated with the efficient utilisation of the potential of renewable energy and the meticulous development of pertinent designs. In this context, various optimisation constraints/targets have also been utilised. This specific work initially carries out a thorough review of the modelling techniques and simulation software developed in an attempt to define a commonly accepted categorisation methodology for the various existing HRES simulation methods. Moreover, the widely utilised optimisation targets are analysed in detail. Finally, it identifies the sensitivity of two commercial software tools (HOMER Pro and iHOGA) by examining nine case studies based on different wind and solar potential combinations. The results obtained by the two commercial tools are compared with the ESA Microgrid Simulator, a software developed by the Soft Energy Applications and Environmental Protection Laboratory of the Mechanical Engineering Department of the University of West Attica. The evaluation of the results, based on the diversification of the renewable energy potential used as input, has led to an in-depth assessment of the deviances detected in the software tools selected.

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Section: Discussionmentioning

confidence: 99%

Hybrid Renewable Energy Systems’ Optimisation. A Review and Extended Comparison of the Most-Used Software Tools

Kavadias

Triantafyllou

2021

Energies

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“…Technical infrastructure and integration require investments that are just a small fraction of renewable energy project costs. , Compliance and legal hurdles need implementation of long-term sustainable policy in this sector . There may be challenges in this sector, but their mitigation can result in much more economic gains for the stakeholders …”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

208

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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“…In addition, the study in Shahzad et al (2022) aimed to identify and prioritize the obstacles that entrepreneurs face in developing RE projects in Pakistan. Finally, the review in Yoro et al (2021) critically assessed current achievements in energy modeling and simulation, with limited insights into its methodologies, problems, and opportunities in a few renewable and sustainable energy systems (RSES). In addition, the idea of model validation in RSES is thoroughly examined using both in-sample and out-of-sample techniques, with prospective data sources for model validation in RSES being emphasized.…”

Section: Introductionmentioning

confidence: 99%

A review of Yemen’s current energy situation, challenges, strategies, and prospects for using renewable energy systems

Al-Wesabi

Fang

Bosah

et al. 2022

Environ Sci Pollut Res

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Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems

Cited by 54 publications

References 131 publications

Hybrid Renewable Energy Systems’ Optimisation. A Review and Extended Comparison of the Most-Used Software Tools

Hybrid Renewable Energy Systems’ Optimisation. A Review and Extended Comparison of the Most-Used Software Tools

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

A review of Yemen’s current energy situation, challenges, strategies, and prospects for using renewable energy systems

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