Search

Briquetted biomass, like sugarcane bagasse, a by-product of sugar mills, is a renewable energy source. This study aimed at the production and characterization of bagasse briquettes. The production of briquettes was carried out with different blending ratios (5, 10, and 15%) and average particle sizes (0.75, 2.775, and 4.8 mm) with various binders of cow dung, waste paper, and admixture (molasses and wastepaper). The bagasse underwent drying, size reduction, sieving, binder addition, and densification using a manual press during the briquetting process. Characterization of the physical and combustion parameters of briquettes, such as density, shatter resistance, proximate, and calorific value, followed the American Society for Testing and Materials procedures. The result shows that the maximum density of briquettes was 0.804 g/cm3, while shatter resistance varied from 83.051 to 94.975% (4.8mm, 5% cow dung and 0.75mm, 5% admixture binders respectively). ANOVA analysis showed that the factors and their interactions had a significant influence (p value < 0.05) on the physical properties. The optimum parameters of briquettes achieved were 14.953% admixture binder, 0.776 mm particle size, 0.805 g/cm3 density, and 95.811% shatter resistance. Bagasse briquettes with a 5% cow dung binder achieved a high calorific value of 39927.05 kcal/kg. The ultimate analysis revealed a composition of 47.49% carbon (C), 5.133% hydrogen (H), 1.557% nitrogen (N), 0.374% sulfur (S), and 45.446% oxygen (O). Therefore, bagasse has a high calorific value and can be used for briquetting to replace fossil fuel and firewood in different applications. In addition, due to its availability, utilizing as fuel source has economic advantage. [ABSTRACT FROM AUTHOR]

Soil microbial fuel cells (SMFCs) are attractive for low-cost bioelectricity generation. In this study, a multiwalled carbon nanotube paper (MCNTP) was fabricated by a simple method for use as the anode and cathode electrodes in a stab-type membrane-less SMFC (SSMFC). Activated carbon powder, carbon fibers, and cellulose fibers were bound by multiwalled carbon nanotube paint to form the MCNTP. As a result, the MCNTP exhibited high conductivity, flexibility, and durability. In addition, the SSMFC was designed as a module mounted with three anodes in series and a floating air cathode. The SSMFC could be activated on demand by simply stabbing in the wet soil. Experimental results showed that the floating cathode outperformed the non-floating one. The SSMFC generated the output shortly after being plugged into the wet soil. The maximum power density was at the level of 60–70 mW/m2 for three separate experiment cycles. Additionally, a single SSMFC was used to power a clock as an example to demonstrate its practical use. [ABSTRACT FROM AUTHOR]

Sphere shape nanocellulose with cellulose II type crystal structure was prepared from waste paper by the swelling on aqueous system NaOH/thiourea/urea, a efficient, inexpensive, and less toxic strategy. The crystalline structure of bleached pulp and nanocellulose was performed using XRD. The nanocellulose exhibits the cellulose II type crystal structure. The crystallinity index of the nanocellulose is calculated to be 48.85%, lower than those of bleached pulp (64.42%). The FTIR of the bleached pulp and the nanocellulose were collected. It indicated that lignin and hemicellulose were removed during the dissolution of cellulose. Sphere shape nanocellulose particles were observed by scanning electron microscopy and transmission electron microscopy. The particle size distribution of nanocellulose was determined using dynamic light scattering. It showed an average size of 50 nm. The thermal behaviors of the bleached pulp and nanocellulose were studied by thermo gravimetric. The obtained nanocellulose had better thermal stability than bleached pulp, which have potential applications in transistors and batteries with high thermal stability. [ABSTRACT FROM AUTHOR]

Although a significant amount of brewery wastewater (BW) is generated during beer production, the nutrients in the BW could be reused as a potential bio-resource for biohydrogen production. Therefore, improvements in photofermentative biohydrogen production due to a combination of BW and pulp and paper mill effluent (PPME) as a mixed production medium were investigated comprehensively in this study. The experimental results showed that both the biohydrogen yield and the chemical oxygen demand removal were improved through the combination of BW and PPME. The best biohydrogen yield of 0.69 mol H/L medium was obtained using the combination of 10 % BW + 90 % PPME (10B90P), while the reuse of the wastewater alone (100 % BW and 100 % PPME) resulted in 42.3 and 44.0 % less biohydrogen yields than the highest yield, respectively. The greatest light efficiency was 1.97 % and was also achieved using the combination of both wastewaters at 10B90P. This study revealed the potential of reusing and combining two different effluents together, in which the combination of BW and PPME improved the nutrients and light penetration into the mixed production medium. [ABSTRACT FROM AUTHOR]

Background: One common renewable energy source for substituting fossil sources is photovoltaic (PV) systems. However, installing PV systems in agricultural areas can lead to competition with other land uses. These projects, therefore, often encounter problems with social acceptance in affected communities. Especially from the perspective of nature conservation targets, conflicts can arise. These potential differences are still under-researched but represent important knowledge for the societally broadly accepted design of such facilities and their contribution to energy transformation. In this paper, we investigate the perspectives of nature conservationists on PV in the region of Brandenburg, Germany. We comparatively analyse attitudes towards ground-mounted photovoltaics (GM-PV) and agrophotovoltaics (APV). APV combines energy supply and agricultural production on the same land and could thus be a possible solution for mitigating land-use conflicts. Results: We investigated the degree of local acceptability and positive and negative influencing factors through a qualitative text analysis of ten interviews with local representatives and position papers by Nature Conservation Associations. Our findings show a growing consensus around basic assumptions of the need for renewable energies, the prioritised support for PV systems on rooftops over the installation on agricultural land (GM-PV and APV), and the necessity for PV systems to be compatible with nature conservation objectives. Regarding specific site decisions on agricultural land, we find diverging attitudes when comparing the content of position papers and the responses of interviewees. The interviewees advocate taking into account local interests and specific regional conditions, the effects of PV systems on the local environment, and the need for distributional justice. Large-scale plants are locally less accepted than smaller ones and there is a more open attitude towards the expansion of APV than of GM-PV on agricultural land. However, a range of concerns regarding consequences for landscape and biodiversity persists, and further research and clarification is required to address these issues. Conclusions: We conclude that basic ecological standards and the demands of local stakeholders and interest groups should be taken into account when planning, implementing and reviewing PV projects in the future. [ABSTRACT FROM AUTHOR]

Increasing the price of fossil fuels, unreliability of fossil fuels for a secure supply of demand in future and their relevant environmental concerns provide an attitude toward substituting renewable energies with fossil fuels for reaching sustainable development in societies. Biofuels as a type of renewable energies can be easily transferred between supply chain's centers and do not have limitation for transportation after their production. Among various types of biofuels, biodiesel, which can be mainly produced from the non-edible feedstocks, such as Jatropha Curcas L. (JCL), is preferred to other biofuels because biodiesel production from JCL which can be cultivated in marginal lands, improves three pillars of sustainability. Since biofuel supply chain's costs can mainly be affected by its feedstock location optimization, this paper used a common weight data envelopment analysis (CWDEA) method for location optimization of feedstock cultivation for a biodiesel supply chain by considering a comprehensive set of sustainability criteria for investigating locations. A case study of Iran is provided for assessing the model's application, and its results in ranking potential locations for JCL cultivation are validated by a numerical taxonomy (NT) approach. In fact, this paper not only specifies the optimum locations for the feedstock cultivation of a biofuel supply chain regarding to sustainability criteria, but also discuss the balanced socioeconomic development and environmental benefits which can be attained by JCL cultivation in marginal and mostly underdeveloped lands. Provided results imply that vast area of Iran's marginal lands has suitable climate for JCL cultivation and policy makers can address all aspect of sustainability simultaneously by investment on those specified lands as well as supplying countries' demand for biodiesel which will be produced from this feedstock. [ABSTRACT FROM AUTHOR]

Wind power is a rapidly developing energy source. Many nations use wind power to meet a considerable amount of their energy needs. Moreover, the technology of wind power has evolved over the period of time. As a result, the wind farm-incorporated power system has received more attention for its outstanding contributions. The purpose of this study is to review the research works published on four key topics within the theme of wind farm-incorporated power systems. We survey the research papers that are featured in the Web of Science database. We employ an approach called Methodi Ordinatio to filter the papers. The publication of papers related to wind farm-incorporated power system has increased significantly, especially between 2018 and 2022. Therefore, we conduct a database search during this period and select important papers. Then we review and describe the technical challenges and solutions of these papers. Furthermore, a bibliographic coupling analysis is presented. The analysis shows that the journals such as Energy, Energies, and Renewable Energy are the leading journals publishing papers on all four key topics. The analysis further demonstrates that the focus of the researchers is on wind power forecasting, followed by energy storage systems, and wind farm layout optimization. The least focus is on optimal power flow. [ABSTRACT FROM AUTHOR]

With the proposed generalized, hitless, and simple control strategy, the delay tunability and delay bandwidth product of the delay line increases with the increasing order of the Mach-Zehnder-based stage. To support end-to-end wireless-wired deterministic communication in these private, professional environments, it is necessary to introduce TSN features to the wireless network segments. The article demonstrates the multi-stage Mach-Zehnder-based integrated continuously tunable optical delay line with an extensive tunability range increases transmission bandwidth and the vast delay-bandwidth product. [Extracted from the article]

The binding mechanisms of two reactive sizing agents, alkenyl succinic anhydride (ASA) and maleated sunflower oil high-oleic (MSOHO), with cellulose were studied. While ASA is produced from olefins out of fossil resources, MSOHO is a green sizing agent based on renewable plant materials. In contrast to common assumptions, that ASA is mostly covalently bound to cellulose, this study showed the largest part of ASA to be bound only by physisorption and only a rather small fraction, typically about 0.5 %, to be covalently attached by ester bonds. In the case of MSOHO, the covalent binding was only slightly higher, with about 3 % of the total amount being covalently linked. In both cases, covalently bound sizing agents were found to be almost uniformly distributed over the whole DP range of the cellulose as seen by carboxyl-selective fluorescence labeling in combination with size exclusion chromatography ('FDAM method'). [ABSTRACT FROM AUTHOR]

An alternative energy source such as solar is one of the most important renewable resources. A reliable solar radiation prediction is essential for various applications in agriculture, industry, transport, and the environment because they reduce greenhouse gases and are environmentally friendly. Solar radiation data series have embedded fluctuations and noise signals due to complexity, stochasticity, non-stationarity, and nonlinearity with uncertain and time-varying nature. Aside from being highly nonlinear, solar radiation is highly influenced by the environment and environmental parameters such as air temperature, cloud cover, surface reflectivity, and aerosols. In addition, the spatial measurements of these variables are not readily available. To tackle these challenges, it is necessary to consider data preprocessing techniques and to develop and test precise solar radiation predicting models at different forecast horizons. There is, however, controversy regarding the performance of such models in various studies. Comparisons are not conducted systematically among the different studies. Using a critical literature review, the authors hope to answer these questions and believe that further investigation of solar radiation can benefit researchers and practitioners alike. This study presents a comprehensive evaluation of solar radiation modeling using artificial intelligence in the last 15 years and provides a novel detailed analysis of the available models. The studies conducted in different climates of the world that were published in distinguished journals were considered (i.e., 90 papers in total) for this purpose. Newly discovered procedures for optimizing forecasts, data cleaning, feature selection, classification methods, and stand-alone or hybrid data-driven models for solar radiation prediction and modeling were evaluated. The results strikingly showed that the most used artificial intelligence methods were artificial neural network, adaptive neuro-fuzzy inference system, and decision tree family of models. In addition, the extreme learning machine, support vector machine, and particle swarm optimization were the most used optimization techniques in solar radiation modeling. In terms of forecast horizons, the most common forecast horizon found in papers was on the daily scale (51% of studies), followed by the hourly scale (26%), and the least common was the monthly scale (18%). Based on the regional studies, the highest number of solar radiation papers originated from Asia, with Europe in second place and African countries in third place. An increasing trend in the number of papers from 2011 to 2015 was noted, and the second peak started from 2018 till the present. Under each section, a summary of findings is provided. The paper concludes with future thoughts and directions on solar radiation modeling. [ABSTRACT FROM AUTHOR]

Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources. [ABSTRACT FROM AUTHOR]

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact. [ABSTRACT FROM AUTHOR]

Datacenters are the primary driving force behind the energy shift to renewable sources. This shift limits the environmental impact of non-renewable sources and decarbonizes datacenters. However, renewable energy generation fluctuates with time and location, which introduces uncertainty in fulfilling user requests (URs). Thus, non-renewable energy generation continues to power the datacenters to make stability. Recent works direct to the use of renewable energy generation followed by non-renewable generation while assigning the URs to the resources of the datacenters. These works do not model the uncertainty of renewable and non-renewable energy resources and the level of uncertainty. This paper extends the three benchmark algorithms, namely future-aware best-fit (FABEF), highest available renewable energy first (HAREF) and round-robin (RR), by incorporating uncertainty and its level (UNL), and we call them UNL-FABEF, UNL-HAREF and UNL-RR, respectively. The goal of UNL-FABEF is to minimize the overall cost, whereas UNL-HAREF is to maximize the available renewable energy usage. On the contrary, UNL-RR assigns the datacenter to the URs in a roundabout fashion. This paper also introduces the UNL multi-objective scheduling algorithm (UNL-MOSA) to make a trade-off between UNL-FABEF and UNL-HAREF. UNL-MOSA creates a balance between the overall cost and the available renewable energy usage. All four algorithms consider three UNLs of URs, namely low, medium and high, for renewable energy resources. These algorithms are tested using fifty instances of ten datasets with 200 to 2000 URs and 20 to 200 datacenters and compared using five performance metrics: the overall cost, number of used renewable and non-renewable energy resource slots, and uncertainty cost and time. The performance of four algorithms in these performance metrics is extensively examined to know their applicability. [ABSTRACT FROM AUTHOR]

Abstract-Achieving high solution efficiency on conventional sequential computation architecture is a challenging task due to penetration of multiple renewable energy sources (RESs). This challenge has become the bottleneck for the application in real-time grid operation, grid planning and analysis of the large-scale and complicated modern power system. Therefore, this paper proposes a parallel multi-GPU and multi-process Fast Decoupled (FD) method to accelerate the power flow calculation, reducing the system responsive time and guaranteeing real-time performance on a large-scale modern power system. In this paper, two hierarchy architecture, task parallelism and data parallelism, are designed to optimize the FD solver parallelization. Moreover, the GPUDirect technology is employed to enhance efficiency of data transmission and drastically reduce copy overhead. The proposed method in this paper achieves a speedup of 9 × ∼ 33 × , compared to the single GPU on a sample large-scale power system. [ABSTRACT FROM AUTHOR]

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO's superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness. [ABSTRACT FROM AUTHOR]

The African continent is confronted with interconnected challenges of water, energy, and food insecurity, particularly affecting marginalized urban residents. Decentralized Water-Energy-Food (WEF) systems have emerged as potential solutions to alleviate these issues exacerbated by climate change. Despite scholarly acknowledgment of constraints related to WEF, there is a growing global trend towards trials of innovative decentralized WEF systems. This paper contributes to the body of research aimed at demonstrating the applicability of decentralised WEF systems at local level, by showcasing the potential of such systems to enhance water, energy and food security in informal settlements. Focusing on Diepsloot slums in Johannesburg, South Africa, the paper applied space analysis, least-cost modeling of sack farming, and renewable energy technologies in providing WEF solutions that demonstrate feasibility, sustainability and decentralised solutions for informal settlements, compared to the popular broad-based solutions that often neglect these marginalised communities. Utilizing Geographic Information Systems (GIS), AquaCrop crop modeling, financial analysis, and linear optimization modelling urbs, the study provides a comprehensive analysis of past climate trends, water sources, spatial plant arrangements, and a proposed community centre. The proposed Phezulu 'Up' growers' model and the positive return on investment further demonstrate the feasibility of decentralized WEF systems. Policymakers are encouraged to promote these WEF systems to bolster urban livelihoods and achieve Sustainable Development Goals (SDGs), notably SDG 2, 6, and 7, in marginalized African urban areas. Recommendations and areas of further research include looking into issues of community engagement, partnerships, risk mitigation, strategies for equity and inclusivity and local knowledge integration during WEF implementation, and robust critique of WEF practical interventions and comparative analysis with other WEF models. Overall, the study provides a blueprint for inclusive and sustainable urban development, emphasizing the importance of localized WEF solutions and community empowerment. [ABSTRACT FROM AUTHOR]

Background: The Association of Southeast Asian Nations (ASEAN) relies mainly on fossil fuels in their energy supply, leading to higher CO2 emissions, pollution, and further environmental degradation. This paper uses the panel vector autoregressive and the Granger non-causality test in the heterogeneous panels, together with long-run estimation techniques, to examine the dynamic link among energy consumption, economic growth, and carbon emissions with the focus on renewable energy for the ASEAN countries in the past three decades. Results: The findings from this paper indicate that carbon emissions are associated with energy consumption. In contrast, renewable energy usage reduces CO2 emissions, improving environmental quality. Economic growth is associated with increased energy consumption and carbon emissions in the ASEAN countries. The findings also indicate that the effects of energy consumption on economic growth are more significant than those of renewable energy in ASEAN. When considered together, these findings form a vicious circle regarding the energy–growth–emission nexus for the ASEAN economies. In addition, a bidirectional Granger causality among energy consumption, economic growth, CO2 emissions and renewable energy usage is confirmed. Conclusions: Renewable energy has emerged as an important viable option for the ASEAN nations to achieve their dual objectives of enhanced economic growth, reduced CO2 emission, leading to improved environmental quality. [ABSTRACT FROM AUTHOR]

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions. [ABSTRACT FROM AUTHOR]

This paper investigates the persistence of shocks on non-renewable and renewable energy consumption for 15 leading countries by renewable energy consumption, over the period 1980–2018. For this aim, we apply Christopoulos and Leon-Ledesma (J Int Money Finance 29(6):1076–1093, 2010) Fourier ADF unit root test which structural breaks are included by using a trigonometric function to allow for smooth temporary mean changes rather than jump functions. In application, we do not need to specify the numbers, locations and the forms of the structural breaks a priori. The results give that shocks on non-renewable energy consumption per capita are persistent for 13 countries except Sweden and the United States; shocks on renewable energy consumption per capita are persistent for 12 countries except Canada, Sweden and the United Kingdom. Overall, we conclude that persistent policy implications on non-renewable energy consumption are more effective tool than transitory policy stances for 13 countries except Sweden and the United States whereas persistent policy implications on renewable energy consumption are more effective for 12 countries except Canada, Sweden and the United Kingdom. In other words, since the energy consumption returns to its trend path quickly, any policy will not be effective on non-renewable energy consumption for Sweden and the United States, and also on renewable energy consumption for Canada, Sweden and the United Kingdom. [ABSTRACT FROM AUTHOR]

Traction power systems (TPSs) play a vital role in the operation of electrified railways. The transformation of conventional railway TPSs to novel structures is not only a trend to promote the development of electrified railways toward high-efficiency and resilience but also an inevitable requirement to achieve carbon neutrality target. On the basis of sorting out the power supply structures of conventional AC and DC modes, this paper first reviews the characteristics of the existing TPSs, such as weak power supply flexibility and low-energy efficiency. Furthermore, the power supply structures of various TPSs for future electrified railways are described in detail, which satisfy longer distance, low-carbon, high-efficiency, high-reliability and high-quality power supply requirements. Meanwhile, the application prospects of different traction modes are discussed from both technical and economic aspects. Eventually, this paper introduces the research progress of mixed-system electrified railways and traction power supply technologies without catenary system, speculates on the future development trends and challenges of TPSs and predicts that TPSs will be based on the continuous power supply mode, employing power electronic equipment and intelligent information technology to construct a railway comprehensive energy system with renewable energy. [ABSTRACT FROM AUTHOR]

The pollution haven hypo thesis (PHH) postulates that foreign direct investment (FDI) inflows can increase environmental deterioration in developing countries as multinational firms tend to transfer their dirty industries to these countries. Turkey, as a developing economy, has witnessed intense FDI inflows over the last decades. Within this scope, the goal of this paper is to examine whether the pollution haven hypothesis (PHH) prevails in Turkey within the scope of the environmental Kuznets curve (EKC) hypothesis over the period 1970–2016. To that end, the paper employs unit root and cointegration methods based on the nonlinear smooth transition models. The empirical findings of the paper indicate that both hypotheses are valid in Turkey. The findings also imply that environmental quality in Turkey is negatively related to electricity production from renewable energy sources. [ABSTRACT FROM AUTHOR]

Renewable energy resources are the only solution to the energy crisis over the world. Production of energy by the solar panel cells are identified as the main renewable energy resources. The generation of energy by the solar panels is affected by the cracks on it. Hence, the detection of cracks is important to increase the energy levels produced by the solar cells. In this paper, the solar panel images are classified into either cracked image or non-cracked image using deep learning algorithm. The proposed method is designed with the following modules preprocessing, enhancement, feature computations, classification and crack segmentation. The source solar panel image are denoised using adaptive median filter as the preprocessing process and then the pixels in denoised solar panel image are enhanced using cumulative enhancement (CE) method. The external features are computed from the CE enhanced solar panel image and these features are classified by Improved AlexNet (IAN)-deep learning classifier to produce the classification results as either cracked or non-cracked solar panel image. Finally, the cracks in classified cracked solar panel image are segmented using morphological algorithm. The main significance of this paper is that the proposed methods stated here detect single and multiple level of minor cracks, which enhances the energy levels production. [ABSTRACT FROM AUTHOR]

This paper presents an overview for the Special Issue (SI) of Clean Technology and Environmental Policy journal (CTEP), and it includes accepted papers from 16th Conferences on Sustainable Development of Energy, Water and Environment Systems (SDEWES) held from October 10–15, 2021, in Dubrovnik, Croatia. Considering CTEPs policy of high-quality research papers, guest editors have invited 35 research articles, presented at the SDEWES 2021 conference. After a vigorous review process, 12 papers have been accepted for publication in this special issue. All 12 accepted papers are briefly presented in this overview together with a wider view that presents research efforts within the SDEWES community published through previous SDEWES special issues. [ABSTRACT FROM AUTHOR]

It can be observed from the existing energy literature the previous papers investigating the influence of renewables consumption on GDP for the USA commonly ignore structural breaks in the US economy. Hence, the purpose of this paper is to examine the impact of renewable energy consumption on economic growth for the USA using quarterly data over the period 1977Q1-2019Q3 through unit root and cointegration methods based on different approaches in modelling structural breaks. Our empirical evidence confirms that all unit root tests give similar outputs and show all the variables become stationary at 1st differences. Besides, cointegration tests show highly different results in terms of the statistical significance of the coefficients. For instance, the cointegration test without structural breaks indicates that renewable energy consumption has no impact on economic growth. With sharp structural breaks in the cointegration approach, there is no cointegration between the variables. The empirical findings of the cointegration test with sharp and gradual breaks imply that renewable energy consumption has positive effects on economic growth. This paper discusses the implications of the empirical findings. [ABSTRACT FROM AUTHOR]

The transition toward renewables is central to climate action. The paper empirically tests whether renewables also enhance international peace, a hypothesis discussed in the International Political Economy (IPE) of renewables literature. It develops and tests hypotheses about the pacifying effects of renewables, with a view to establishing the foundations for analyzing more detailed causal mechanisms. These mechanisms rest on the 'energy democracy' debate, suggesting that a low carbon world sees less interstate tension thanks to more states being democratic; the 'capitalist peace' theorem, establishing that the deployment of renewables brings about economic development, reducing conflict; and the human security literature, positing that renewables reduce local-level reduce vulnerabilities, thus enhancing social stability and reducing violence. Using a longitudinal dataset on global renewable energy investment, econometric tests suggest that distributed renewable energy systems do not seem to foster democratic rule, nor do they have a significant influence on human development. Countering the energy democracy literature, it is a higher concentration of renewable investment that tends to increase stability/ absence of violence and human development, instead of decentralized investment patterns. We find no evidence for the 'peace through prosperity' argument. Overall, there is no support for the assumption that renewables bring about peace and reduce conflict. The paper critically discusses the limitations of these findings and suggests further avenues for empirical research. [ABSTRACT FROM AUTHOR]

In the case of China, there is a need of green energy system to boost growth and environment. However, the current growth of urbanization is putting high pressure on energy system through financial capital. Hence, it is a need to develop such path through renewable energy consumption, capital development, and urbanization to enhance the performance of development and environment. Therefore, this paper contributes to the literature by adding the asymmetries between renewable energy, urbanization, economic growth, and capital investment by covering the period from 1970 to 2021. For this purpose, we apply the nonlinear autoregressive distributed lag model to find the nonlinearities between the variables under study. The findings confirm the asymmetric short- and long-term relationships between the variables. In this sense, capitalization shows the short- and long-term asymmetric impacts on renewable energy consumption. In addition, urbanization and economic growth have long-term asymmetric and positive impacts on renewable energy consumption. At last, this paper provides applicable and practical policy implications for China. [ABSTRACT FROM AUTHOR]

Based on the background of carbon neutrality targets, panel data of 30 Chinese provinces during 2000–2019 were selected in this paper to explore the nonlinear correlation mechanism among transportation, tourism development, renewable and non-renewable energy consumption and economic growth. Firstly, by employing a fixed effects model as the base model, we confirmed the existence of correlations among these variables. And the nonlinear moderating effects were also verified by setting interaction terms in the model. Secondly, we further discussed the characteristics of the nonlinear relationship among the variables by employing a threshold effects model. Our results demonstrated that the effect of transportation on economic growth is positive and significant if and only if renewable energy consumption surpasses a certain threshold. Meanwhile, we also found that the effect of tourism development on economic growth is positive when non-renewable energy consumption is under a certain threshold. Finally, our study indicated that transportation maintains a positive and significant effect on economic growth within any threshold of tourism development, and the positive effect gradually increases as the threshold rises. The findings of this paper suggested that for China, the contribution of transportation and tourism to economic growth can be expanded by further expanding the substitution of renewable energy for non-renewable energy. [ABSTRACT FROM AUTHOR]

The renewable energy+energy storage model has an important role to play in achieving China's proposal of the carbon peaking and carbon neutrality goal. In order to study the development mechanism of renewable energy+storage cooperation with government participation, this paper constructs a three-party evolutionary game model among government, renewable energy generators, and energy storage service suppliers, based on the data of a renewable energy+storage project in a Chinese province. In this paper, we analyze the game process and the factors affecting the behavioral strategies of the three parties through numerical simulation. The study shows that the government regulation has a positive effect on the cooperative development of renewable energy+energy storage, and it can effectively restrain the wasteful phenomenon of renewable energy generation by means of punishment and improve the operating income of cooperative projects by means of subsidies, which can help the enterprises to broaden the application scenarios of energy storage. The government can effectively promote the improvement of renewable energy+energy storage cooperation mechanism by formulating regulatory mechanism, controlling the cost of supervision, and dynamically adjusting the intensity of supervision. Therefore, the research in this paper not only enriches the research related to renewable energy+energy storage mode but also provides valuable reference for the government to formulate regulatory policies related to renewable energy+energy storage. [ABSTRACT FROM AUTHOR]

Renewable energy has become increasingly popular worldwide, and solar energy is one of the most sustainable and widely used forms of renewable energy. However, the lack of precision in system sizing often leads to oversizing and significant financial waste. To address this issue, this paper presents the development, testing, validation, and evaluation of an online educational/industrial standard-based tool for optimizing photovoltaic water pumping systems (PVWPSs). The proposed tool considers statistics on solar irradiance, average temperature, and necessary solar panel specifications to aid in calculating the peak power required to meet demand. Using MATLAB software, the tool enables the sizing of a PVWPS for either centrifugal or submersible pumps, including complete PV sizing for on-grid or off-grid systems. The tool also demonstrates the impact of ignoring temperature, sun irradiation, and pipeline losses on the sizing technique. Results indicate that significant temperature changes can negatively impact sizing, requiring further adjustments. Finally, a questionnaire was administered to various user segments to evaluate the tool's efficiency, yielding satisfactory results. This paper presents a valuable tool for optimizing PVWPSs, enabling more precise and efficient system sizing, and reducing financial waste. [ABSTRACT FROM AUTHOR]

This paper is an attempt to provide a dual-source inverter, an intelligent inverter topology that links two isolated DC sources to a single three-phase output through single-stage conversion. The converter is designed to be utilized in hybrid photovoltaic fuel cell systems, among other renewable energy applications. The proposed dual-source inverter employs a single DC-AC converter, as opposed to conventional dual-source hybrid inverters which make use of several input DC-DC modules to obtain the voltage formed across the inverter's input DC-link. In the proposed topology, the semiconductor count is low, which leads to improved efficiency, cost, complexity, and reliability. The proposed topology makes use of two impedance networks connected by transformers, diodes, and capacitors. The regulation of the electrical power generated by primary sources and the independence of the converter on key factors like voltage and frequency are essential parameters in multi-input converters. This feature becomes highly prominent when the control algorithm is implemented by conventional processors. Viewed from this perspective, the control method described in this paper is worthy of consideration. The research work describes a 220-W/50 Hz prototype that employs Simple Boost-SPWM. Experimental results verify the analyses and corroborate the satisfactory performance of the suggested converter. [ABSTRACT FROM AUTHOR]

As countries decarbonise, the competition for land between energy generation, nature conservation and food production will likely increase. To counter this, modelling, and sometimes energy policies, use exclusion zones to restrict energy deployment from land deemed as important to society. This paper applies the spatially-explicit ADVENT-NEV model to Great Britain to determine the cost imposed on the energy system when either environmental or food production exclusion zones are applied. Results show that exclusion zones impose a cost of up to £0.63 billion (B), £19.17 B and £1.33 B for the solar, wind, and bioenergy pathways. These costs give an indication of the value being placed on protecting these areas of land. When multiple exclusions are imposed on bioenergy, the high pathway is infeasible indicating a more flexible approach may be needed to meet net zero ambitions. The model also shows how the value of ecosystem services changes when exclusion zones are applied, highlighting how some exclusions increase non-market costs whereas others decrease them. In several cases exclusion zones are shown to increase social costs, the opposite of their intended use. For these exclusions to be justifiable, the unobserved values missing from the model must be as large as these increases. [ABSTRACT FROM AUTHOR]

In this paper, an economic life cycle assessment of hydrogen-fueled gas turbine power plant is developed, where hydrogen is produced via electrolysis with electricity generated from renewable resources (green hydrogen). By this way, the continuous green electricity without fluctuation can be generated. With a great potential in solar irradiation, Iran can be a major country for producing green hydrogen. For this purpose, first, we simulate a 100 MW solar PV plant in Fars province and calculate annual electricity generation. Considering capital cost (Capex) and operations and maintenance costs (Opex) of plants including PV, electrolyzer, hydrogen storage and distribution facilities, and gas turbine, we estimate levelized cost of electricity from green H2-fueled gas turbine power plant. Because hydrogen technology has not matured yet, the analysis for both 2021 and 2040 will be conducted. Due to lower marginal production costs, the increase in renewable energy sources decreases electricity wholesale prices. In this paper, we also estimate long-term merit order effect (MOE) of electricity generated by green hydrogen on electricity wholesale price. The results show that in renewable/hydrogen-based forecast for electricity mix of 2040, electricity wholesale price is estimated around 6.38 c$/kWh, nearly 38% less than present wholesale price. [ABSTRACT FROM AUTHOR]

Since the future grid needs to provide sufficient electrical energy with high quality, it is envisaged that most of the used energy will be supplied by grid-connected voltage source converters (VSCs). The VSCs are used for connecting renewable energy source to the grid, e.g., wind and solar power. The grid-connected VSC considered in this paper is a three-phase three-wire full-bridge converter in which a capacitor is connected to its dc side, while its ac-side is connected to the grid with current control. When operating under unbalanced voltages at the point of common coupling, either due to the presence of unbalanced loads or due to a grid fault condition, a voltage ripple appears at the dc side. The interaction between the VSC dc- side voltage ripple and the VSC ac-side currents is the main focus of this study. In this paper, we analyze the interaction between the VSC dc-side voltage ripple and the VSC ac-side currents under unbalanced voltages. Furthermore, the proposed model is used to determine in which conditions the stability of the VSC ac-side currents can be guaranteed even in the presence of the dc-side voltage ripple. Experimental results confirm that the system remains stable, although with distorted currents due to the dc voltage ripple. Furthermore, it is also shown that the injection of negative sequence currents can decrease the current distortion. [ABSTRACT FROM AUTHOR]

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. [ABSTRACT FROM AUTHOR]

This paper's main objective is to investigate the effect of stock market growth and green energy on GHG emissions. Further, this research empirically analyzes the effects of renewable energy consumption on CO2 emissions in the EKC hypothesis using data from BRICS countries (China, India, Russia, Brazil, and South Africa) from 1990 to 2019. The empirical analysis was carried out on the second-generation unit root test. The bootstrap panel integration method is used to verify long-term equilibrium connections between elements of all four models. The empirical results confirm a significant long-run flexibility study's findings that green energy improves environmental circumstances by lowering carbon pollution levels across the panel regions. Additionally, renewable energy consumption significantly decreases carbon dioxide emissions and increases financial output through the panels. Instead, the stock market expansion index fell carbon pollution in BRICS countries while increasing carbon pollution in India member country. In general, fixed capital formation and economic expansion degrade environmental quality by rising carbon pollution concentrations. This paper's results have significant and effective policy implications for promoting renewable energy consumption sources to help reduce CO2 emissions and ensure sustainable economic growth in the BRICS nations. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

Green technology innovation is a critical factor in ensuring the long-term stable development of renewable energy enterprises. Based on the super network theory, this paper constructs a network model of green technology innovation influencing factors of renewable energy enterprises, which includes the knowledge sub-network of green technology innovation of renewable energy enterprises, the research and development member sub-network of green technology innovation team of renewable energy enterprises and the policy sub-network of green technology innovation of renewable energy enterprises. It explores the mechanism of its influence on innovation in the preparation stage. Simulation analysis by Netlogo software concludes that innovation knowledge sharing, R&D membership, and innovation policy all have a significant positive impact on green technology innovation in renewable energy companies. [ABSTRACT FROM AUTHOR]

This paper focuses to identify and validate a more appropriate algorithm to solve the proposed problem. The economic load dispatch (ELD) with the emission parameters becomes more complex and diversified on the involvement of renewable energy sources (RES), and this increases the number of constraints incorporation in the distributed system of classical power system. The integrated economic emission dispatch (IEED) and ELD are a measure of minimizing the fuel cost of generators, minimizing the emitted gaseous pollutants of the conventional generators (CGs) due combustion, and fulfilling the load demand while satisfying the given system constraints. This paper develops and proposes the hybrid moth-flame and mayfly optimization algorithm (MFMFOA), the hybridization of moth-flame optimization (MFO), and mayfly algorithm (MFA) to solve the integrated problem of economic load and emission dispatch, while removing the constraints of the existing optimization algorithms. The overall cost saving of 10.03% and 23.95% was obtained in ELD and IEED, respectively, using MFMFOA. The MFMFOA when correlated with reduced gradient method (RGM), ant colony optimization (ACO), cuckoo search algorithm (CSA), interior search algorithm (ISA), bat algorithm (BA), directional bat algorithm (DBA), and MFO for different cases of ELD and IEED have obtained the comparatively minimum operational cost, a closer global optimum point, and better convergence characteristics. [ABSTRACT FROM AUTHOR]

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model's training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

Global warming is a vital problem that many researchers tried to solve with so many solutions such as: reducing electricity production with conventional generators by using renewable energy resources and using hydrogen as an alternative to fossil fuels. The universal Economic crisis came across to cut off certain quantities at scheduled times. This, directly, affects renewable energy sources connected to the grid due to voltage and frequency variations. To solve this dilemma, in this paper, the grid-connected solid oxide fuel cell (SOFC) model which is fed by green hydrogen to produce AC power, is developed by an Adaptive neuro-fuzzy inference system (ANFIS). It is one of the artificial intelligence applications to improve grid-connected SOFC dynamic response. ANFIS is mathematically presented and simulated using MATLAB/SIMULINK. The results show that the ANFIS controller has succeeded in enhancing most of the desired control and operation signals. [ABSTRACT FROM AUTHOR]

Global climate changes evoked by carbon emissions have stimulated genuine interest in sustainable technology apropos the marine transportation sector. Offshore charging infrastructure for electric vessels is one of the focal areas for sustainable marine transportation. Pervasive deployment of electric vehicles is restrained by travel range and battery energy capacity. This paper brings to bear the exigency of offshore floating charging stations (FCSs) that charge/recharge electric vessels at sea. This work underscores the feasibility of implementation and energy management of reliable offshore recharging stations with renewable energy sources, energy storage systems, and backup energy sources. A realistic study was performed at a North Sea offshore location for an appraisal of the viability and operational profile of an FCS. A reconfigurable energy management strategy was developed to fulfill real-time energy demands with balanced energy flows, ensuring minimal dependency on backup energy sources. The validation results revealed that the time duration of the FCS's need for a backup energy source was reduced by 96%, and the energy consumed from the backup energy source showed a 97% decrease. This investigation laid the groundwork for systematic follow-on studies that ratify sustainable electrified transportation in the maritime sector. The authors recommend further research in the following areas: vessel traffic analysis at dispersed offshore locations, FCS siting, and marine spatial planning for FCS implementation. [ABSTRACT FROM AUTHOR]

The unflinching upsurge in global warming has put the world into a state of disarray, and policymakers are not sure of what the future holds for the ecosystem. To get the global environment back on track of sustainability, efforts are relentlessly emanating from both developed and developing economies to identify the hindering and enhancing factors. Given the preceding narratives, this paper investigates the tripartite impacts of renewable and nonrenewable energy, trade openness, and institutions on sustainable environment in 54 African countries from 1996 to 2019. The study employs the two-step system generalized methods of moment (SYS-GMM) in estimating the effects of the regressors on four indicators of sustainable environment. Six findings are prominent from the study. First, nonrenewable energy (NRE) and trade openness (TO) hinder sustainable environment, while renewable energy enhances it. Second, the unconditional impacts of institutions drive the path to a sustainable environment. Third, the interaction of institutions with NRE and TO hinders sustainable environment. However, after conducting institutional enhancement estimations, the interactive terms became supportive of sustainable environment. Fourth, the thresholds are policy-relevant across the specifications. Specifically, a 31.11% utilization of nonrenewable energy and 43.75% openness to trade contribute to a sustainable environment. Sixth, the impacts of economic growth and its square empirically support the inverted U-shaped EKC. This study suggests the adoption of carbon-taxing on nonrenewable energy products, price-subsidization for renewable energy goods and services, and institutional quality reforms as pathways towards achieving a sustainable environment in Africa. [ABSTRACT FROM AUTHOR]

In today's world, the use of renewable resources is increasing due to cost-effective and eco-friendly solutions. Predominantly, using renewable sources in power generation leads to a decrease in cost value. Renewable sources such as wind and solar have power variations in their output because of seasonal changes. Therefore accurate forecasting is required for solar and wind energy. In this paper, a hybrid storage unit is developed to store the generated energy from solar, wind, and diesel generator. Because of large power storage purposes, the paper combines battery and super magnetic storage systems. The power from the hybrid storage unit is decided using the cat swarm optimization algorithm. Moreover, the paper proposes black widow optimization algorithm to solve problems such as contingency-constrained economic load dispatch (CCELD) in the transmission line. The CCELD problem solves the minimum fuel cost and minimum severity index. A standard IEEE 57 bus system is used to study the efficacy of a BWO algorithm for the CCELD problem. The final fuel cost value of line outages L1-L2, L1-L3, L3-L4, and L2-L5 are 2069.84Rs/hr, 2484.27Rs/hr, 1750.95Rs/hr, and 1381.94Rs/hr, respectively. The proposed method reduces overall cost to 19.33% with branch and rescue navigator method and 23.39% with enhanced mixed particle swarm optimization. [ABSTRACT FROM AUTHOR]

The global energy sector is currently undergoing a transformative shift mainly driven by the ongoing and increasing demand for clean, sustainable, and reliable energy solutions. However, integrating renewable energy sources (RES), such as wind, solar, and hydropower, introduces major challenges due to the intermittent and variable nature of RES, affecting grid stability and reliability. Hybrid energy storage systems (HESS), which combine multiple energy storage devices (ESDs), present a promising solution by leveraging the complementary strengths of each technology involved. This comprehensive review examines recent advancements in grid-connected HESS, focusing on their components, design considerations, control strategies, and applications. It provides a detailed analysis of technological progress in various ESDs and the critical role of power conversion, control, energy management, and cooling systems in optimizing HESS performance. Highlighting case studies of some notable and successful HESS implementations across the globe, we illustrate practical applications and identify the benefits and challenges encountered. By addressing these challenges, HESS can significantly enhance the efficiency and reliability of RES, supporting the shift towards a sustainable and resilient energy infrastructure. The paper concludes by identifying future research directions, highlighting the development of intelligent control systems, sustainable materials, and efficient recycling processes to ensure the widespread adoption and long-term viability of HESS. [ABSTRACT FROM AUTHOR]

In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller's robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller's robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics. [ABSTRACT FROM AUTHOR]

The energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together. [ABSTRACT FROM AUTHOR]

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid. [ABSTRACT FROM AUTHOR]

In this paper, an interleaved DC–DC step-up converter with improved characteristics based on a voltage multiplier rectifier is presented. The proposed converter is presented and analyzed for two different operating duty regions including operating region 1 (0 < D ≤ 0.5), and operating region 2 (0.5 ≤ D < 1). This converter can be used in various applications such as energy storage, electric vehicles, and renewable energy systems. This converter is composed of two stages: an interleaved boost stage and a voltage multiplier rectifier stage, which collectively forms its general structure. The interleaved boost stage is a type of two-phase boost converter that transforms the input DC voltage into a high-frequency AC square waveform. This waveform can be readily filtered using smaller capacitors. The square-shaped voltage waveform from the interleaved boost stage is rectified and converted to a high DC voltage by the Voltage Multiplier Rectifier (VMR) stage. The operating regions, the evaluation of the steady-state condition, the voltage gain of the proposed converter's parasitic and ideal models as well as its losses and efficiency analysis have been evaluated. The proposed converter has an efficiency of 97% at the output power of 150 W. The proposed converter is simulated to convert a voltage of 25–159.5 V and to validate the mathematical relationships and simulation results, a laboratory prototype has been developed. The simulation and experimental results show the precision of the performance of the proposed interleaved boost converter. [ABSTRACT FROM AUTHOR]