Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems

This research examined the observed datasets and a theoretically derived model for estimating yearly optimum tilt angle (β), maximum incident solar radiation (Hmax), clean gain indicator (CGI), and soiling loss indicator (SLI) at Mumbwa, Zambia, the Mediterranean Region, and low latitude locations across the globe. The cleaned tilted collector emerged as the best performing collector due to Hmax and much higher energy gains compared with the soiled collector. CGI showed an appreciable performance of 0.4737% over -0.4708% on the SLI, indicating that soiling on the surface of photovoltaic (PV) modules significantly depreciates the overall performance of PV modules. Two established empirical models obtained from the literature were compared with the established theoretical model (β=φ). The result revealed that the two models overestimated the observed annual optimum tilt angle in this paper, simply because the models were developed with high latitude location datasets from the Asia continent. However, the newly established monthly and yearly global radiation indicator (GRI) models by the authors in their previous paper performed excellently in the selected representative cities in the Mediterranean region.Citation: Nwokolo, S., Obiwulu, A., Amadi, S., & Ogbulezie, J. (2023). Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems. Trends in Renewable Energy, 9(2), 120-136. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015

Exploring Cutting-Edge Approaches to Reduce Africa's Carbon Footprint through Innovative Technology Dissemination

This paper investigates the possibility of revolutionizing Africa's carbon footprint through innovative technology dissemination strategies for GHG emission reduction.  It highlights the importance of harnessing renewable energy sources to mitigate climate change and promote sustainable development in Africa. This paper also examined several technology diffusion theories in order to unleash Africa's climate-smart potential by tying them to the recommended techniques for dealing with technological diffusion concerns. These theories varied from diffusion of innovation theory to planned behaviour theory. By analysing these theories, it was found that the most appropriate technology diffusion theory for the assessment of innovative technology dissemination strategies for GHG emission reduction in Africa would be the Diffusion of Innovations Theory. This is due to the theory's emphasis on the dissemination and adoption of new ideas, technologies, or innovations by people or groups within a social system. It would give useful insights into the variables influencing the adoption and dissemination of novel technology for reducing GHG emissions in Africa. The paper also discusses the challenges and barriers faced in the diffusion of renewable energy technologies across the continent while proposing innovative strategies to overcome these obstacles and unlock Africa's untapped climate-smart potential. These strategies include promoting policy and regulatory frameworks that incentivize investment in renewable energy, fostering partnerships between governments, private sector entities, and international organizations to support technology transfer and capacity building, and implementing financial mechanisms such as green bonds and carbon pricing to mobilize funding for renewable energy projects. These proposed strategies were also used to develop seven policies required for innovative technology dissemination strategies for GHG emission reduction in Africa. These policies aim to address the unique challenges faced by African countries in adopting and implementing innovative technologies for GHG emission reduction. By focusing on capacity building, financial incentives, and knowledge sharing, these strategies seek to promote the widespread adoption of sustainable technologies across the continent. They emphasize the importance of collaboration between governments, private sector entities, and international organizations to ensure the successful implementation and long-term sustainability of these policies.Citation: Nwokolo, S. C., Eyime, E. E., Obiwulu, A. U., & Ogbulezie, J. C. (2023). Exploring Cutting-Edge Approaches to Reduce Africa's Carbon Footprint through Innovative Technology Dissemination. Trends in Renewable Energy, 10, 1-29. doi:10.17737/tre.2024.10.1.0016

Research Progress of Nanofluid Heat Pipes in Automotive Lithium-ion Battery Heat Management Technology

Power batteries are a crucial component of electric vehicles and other electric equipment. Their long-term high-rate discharge generates a lot of heat, which can lead to battery failure, shortened battery life, and even safety accidents if not managed properly. Due to its high thermal conductivity, the heat pipe can quickly conduct heat away from the battery and separate the heat source from the heat sink. In addition, due to its excellent isothermal performance, the heat pipe can also achieve the characteristics of low-temperature preheating and high-temperature cooling of the power battery by reducing the inhomogeneity of the battery temperature field to reduce the temperature difference. In this paper, we review the current state of the art in thermal management of automotive lithium-ion battery, and highlight the current state of thermal management of batteries based on the combination of nanofluids and heat pipes. Finally, the development of nanofluidic heat pipes in lithium-ion battery heat management systems is prospected.Citation: Wang, X., Zhao, Y., & Jin, Y. (2023). Research Progress of Nanofluid Heat Pipes in Automotive Lithium-ion Battery Heat Management Technology. Trends in Renewable Energy, 9(2), 137-156. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015

Optimized Lightweight Frame for Intelligent New-energy Vehicles

In this paper, a joint optimization method based on multi-objective response surface approximation model and finite element simulation program is proposed to realize the lightweight optimization of new-energy vehicle frames. Under the premise of satisfying the constraints of strength, frequency and vibration, the thickness of different important parts is optimized to achieve the goal of minimizing the quality of intelligent vehicles. In order to obtain the stress distribution of each part and the vibration frequency of the frame, various finite element analyses of the intelligent vehicle frame are analyzed. In order to achieve optimization, this paper adopts the response surface method for multi-objective optimization. Sample data was generated by the central composite design, and the response surface optimization method was used to filter out 5 design variables that had a large impact on the frame. As a result, the weight of the frame was reduced from 25.05 kg to 19.86 kg, a weight reduction of 20.7%, achieving a significant weight reduction effect. This method provides important reference value and guiding significance for the optimization of frame and its lightweight. In this way, the design of the frame can be better optimized to make it lighter, thereby improving the performance of the smart car. At the same time, this method can also be applied to optimization problems in other fields to achieve more efficient and accurate optimization goals. Citation: Wu, P. (2023). Optimized Lightweight Frame for Intelligent New-energy Vehicles. Trends in Renewable Energy, 9(2), 157-166. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015

Big Data and Neural Networks in Smart Grid - Part 2: The Impact of Piecewise Monotonic Data Approximation Methods on the Performance of Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Powerlines Networks

Τhe impact of measurement differences that follow continuous uniform distributions (CUDs) of different intensities on the performance of the Neural Network Identification Methodology for the distribution line and branch Line Length Approximation (NNIM-LLA) of the overhead low-voltage broadband over powerlines (OV LV BPL) topologies has been assessed in [1]. When the αCUD values of the applied CUD measurement differences remain low and below 5dB, NNIM-LLA may internally and satisfactorily cope with the CUD measurement differences. However, when the αCUD values of CUD measurement differences exceed approximately 5dB, external countermeasure techniques against the measurement differences are required to be applied to the contaminated data prior to their handling by NNIM-LLA. In this companion paper, the impact of piecewise monotonic data approximation methods, such as L1PMA and L2WPMA of the literature, on the performance of NNIM-LLA of OV LV BPL topologies is assessed when CUD measurement differences of various αCUD values are applied. The key findings that are going to be discussed in this companion paper are: (i) The crucial role of the applied numbers of monotonic sections of the L1PMA and L2WPMA for the overall performance improvement of NNIM-LLA approximations as well as the dependence of the applied numbers of monotonic sections on the complexity of the examined OV LV BPL topology classes; and (ii) the performance comparison of the piecewise monotonic data approximation methods of this paper against the one of more elaborated versions of the default operation settings in order to reveal the most suitable countermeasure technique against the CUD measurement differences in OV LV BPL topologies.Citation: Lazaropoulos, A. G., & Leligou, H. C. (2024). Big Data and Neural Networks in Smart Grid - Part 2: The Impact of Piecewise Monotonic Data Approximation Methods on the Performance of Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Powerlines Networks. Trends in Renewable Energy, 10, 67-97. doi: https://doi.org/10.17737/tre.2024.10.1.0016

Africa's Path to Sustainability: Harnessing Technology, Policy, and Collaboration

This paper explores the significant role of technological advancements, strategic policies, and collaborations in driving Africa towards a more sustainable future. It highlights how the continent's increasing adoption of innovative technologies, such as renewable energy solutions and digital infrastructure, coupled with well-crafted strategic policies and international collaborations, is transforming various sectors and fostering a sustainable future. These advancements have not only improved access to basic services like healthcare and education but have also created new opportunities for economic growth and job creation. The paper emphasizes the importance of ongoing collaborations between African countries and international partners in sharing knowledge, expertise, and resources to accelerate sustainable development efforts across the continent. The paper discusses different international organizations that have collaborated with and assisted Africa in the areas of technical innovation, finance, and knowledge exchange necessary to achieve a full-scale sustainable future. Despite their humanitarian efforts, Africa faces tremendous hurdles in attaining a sustainable future. These challenges range from a lack of access to technology and digital infrastructure in rural areas to difficulties in harnessing technological advancements due to infrastructure and connectivity constraints. These challenges have hindered Africa's ability to fully leverage the potential of technical innovation and digital solutions for a sustainable future. Limited financial resources and investment opportunities have further impeded progress in achieving the necessary infrastructure and connectivity upgrades. The continent is vulnerable to the impacts of climate change, which further hinders its development progress. Therefore, it is crucial for ongoing collaborations between African countries and international partners to address these challenges collectively and work towards long-term solutions for a sustainable future in Africa.Citation: Nwokolo, S., Eyime, E., Obiwulu, A., & Ogbulezie, J. (2023). Africa's Path to Sustainability: Harnessing Technology, Policy, and Collaboration. Trends in Renewable Energy, 10(1), 98-131. doi:http://dx.doi.org/10.17737/tre.2024.10.1.0016

Big Data and Neural Networks in Smart Grid - Part 1: The Impact of Measurement Differences on the Performance of Neural Network Identification Methodologies of Overhead Low-Voltage Broadband over Power Lines Networks

Until now, the neural network identification methodology for the branch number identification (NNIM-BNI) and the neural network identification methodology for the distribution line and branch line length approximation (NNIM-LLA) have approximated the number of branches and the distribution line and branch line lengths given the theoretical channel attenuation behavior of the examined overhead low-voltage broadband over powerlines (OV LV BPL) topologies [1], [2]. The impact of measurement differences that follow continuous uniform distribution (CUDs) of different intensities on the performance of NNIM-BNI and NNIM-LLA is assessed in this paper. The countermeasure of the application of OV LV BPL topology databases of higher accuracy is here investigated in the case of NNIM-LLA. The strong inherent mitigation efficiency of NNIM-BNI and NNIM-LLA against CUD measurement differences and especially against those of low intensities is the key finding of this paper. The other two findings that are going to be discussed in this paper are: (i) The dependence of the approximation Root-Mean-Square Deviation (RMSD) stability of NNIM-BNI and NNIM-LLA on the applied default operation settings; and (ii) the proposal of more elaborate countermeasure techniques from the literature against CUD measurement differences aiming at improving NNIM-LLA approximations.Citation: Lazaropoulos, A. G., & Leligou, H. C. (2024). Big Data and Neural Networks in Smart Grid - Part 2: The Impact of Piecewise Monotonic Data Approximation Methods on the Performance of Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Powerlines Networks. Trends in Renewable Energy, 10, 30-66. doi: https://doi.org/10.17737/tre.2024.10.1.0016

Artificial Intelligence, Machine Learning and Neural Networks for Tomography in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies

Until now, the neural network identification methodology for the branch number identification (NNIM-BNI) has identified the number of branches for a given overhead low-voltage broadband over powerlines (OV LV BPL) topology channel attenuation behavior [1]. In this extension paper, NNIM-BNI is extended so that the lengths of the distribution lines and branch lines for a given OV LV BPL topology channel attenuation behavior can be approximated; say, the tomography of the OV LV BPL topology. NNIM exploits the Deterministic Hybrid Model (DHM) and the OV LV BPL topology database of Topology Identification Methodology (TIM). By following the same methodology of the original paper, the results of the neural network identification methodology for the distribution line and branch line length approximation (NNIM-LLA) are compared against the ones of the newly proposed TIM-based methodology, denoted as TIM-LLA.Citation: Lazaropoulos, A. G., and Leligou, H. C. (2023). Artificial Intelligence, Machine Learning and Neural Networks for Tomography in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies. Trends in Renewable Energy, 9, 34-77. DOI: 10.17737/tre.2023.9.1.0014

A Review of Low Temperature Combustion Mode of Engine

Since the 21st century, people's increasing attention to fuel economy and environmental issues has prompted the engine research community to continuously develop new efficient and clean combustion theories and methods. In terms of combustion technology, many researchers have proposed different new engine combustion methods, such as homogeneous charge compression ignition combustion (HCCI), premixed charge compression combustion (PCCI), and reaction controlled compression ignition (RCCI), which are the three main low-temperature combustion methods. These combustion methods are different from the premixed combustion method of the spark ignition (SI) engine represented by the traditional gasoline engine and the diffusion combustion method of the compression ignition (CI) engine represented by the traditional diesel engine. The flame temperature affects the combustion and emission process of the engine, and realizes the efficient and clean combustion of the engine. This paper first briefly describes the conventional engine combustion method, and then briefly summarizes the comparison between these three low-temperature combustion methods and their respective combustion and emission characteristics as well as advantages and disadvantages, with respect to the conventional combustion method.Citation: Hao, Q. (2023). A Review of Low Temperature Combustion Mode of Engine. Trends in Renewable Energy, 9(2), 180-191. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0016

A Review of Research on Emission Characteristics of Ethanol-Diesel Blends in Diesel Engines

This paper reviews research on the emission characteristics of blended ethanol and other fuels. With the rapid development of modern industry, the extensive use of fuel engines has led to increasingly prominent contradictions between energy and the environment. In order to respond to sustainable development and reduce engine emissions in various countries, many scientific research institutions have conducted research on mixed fuels. The research of blended fuel mainly focuses on its sustainability, economy and environmental protection. Compared with gasoline engines, diesel engines have a lower fuel consumption rate and are widely used in heavy industry. But its fuel comes from refining crude oil, which is non-renewable and has poor cleanliness. As an emerging renewable fuel, ethanol is a fuel with good development prospects due to its good cleanliness, wide range of sources and renewable. If ethanol can be used as an alternative fuel for traditional internal combustion engines and diesel engines, it can save some traditional fuels and improve the emission problems of internal combustion engines to a certain extent. This paper introduces the research status of ethanol blended fuels, and the emission characteristics of engines (NOx, HC and CO) under different ethanol ratios and different operating conditions. It can be seen that with the increase of ethanol blending ratio, NOx content will increase, while CO and HC emissions will decrease.Citation: Chen, M. (2023). A Review of Research on Emission Characteristics of Ethanol-Diesel Blends in Diesel Engines. Trends in Renewable Energy, 9(2), 107-119. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015