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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]

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]

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]

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]

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]

This paper presents a new structure for switched-capacitor multilevel inverter with octuple voltage gain capability. The proposed inverter utilizes three capacitors, 13 semiconductor switches, three diodes, and an input voltage source to achieve a 17-level output voltage. The switched capacitors naturally achieve voltage balancing without the need for sensors or additional circuits, indicating the ease of control of the proposed structure. To control the inrush current of the switched capacitors, a charge limiting inductor has been utilized in the charging path of the capacitors. This not only reduces the inrush current of the capacitors and the input source current but also enables faster capacitor charging and extends their lifetime. The switches used in the proposed structure can withstand a maximum of 4 times the input voltage value or the half of the maximum output voltage, which is a significant advantage for the proposed structure. A detailed comparison with similar structures is provided to examine the advantages and disadvantages of the suggested inverter. The procedure of self-voltage balancing of the capacitors and the functional modes of the proposed topology has been explained in detail. The proposed structure is suitable for applications such as renewable energy sources transfer to load or grid. The performance of the proposed topology under different conditions is confirmed through simulation in the Matlab\Simulink software and the implementation of the laboratory sample. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of recent literature on European energy system modeling and analysis with special focus on grid development. Spanning the years from 2013 to 2023, we analyze 59 selected articles, organizing them by geographical scope, grid expansion strategies, research focus, and methodology. Additionally, we provide an overview of established and recurring frameworks, including ELMOD, EMPIRE, AnyMOD, LIMES, TIMES, FlexPlan, PyPSA, REMix, and Balmorel. Further, we elaborate on the recent trends in research and modeling. Based on our observations, we propose avenues for future research. For instance, considering recent changes in the geopolitical environment, we suggest shifting the geographical research focus from the North Sea region to the Central and Eastern European regions. Other suggestions include investigating grid development under imperfect market competition, merging the study of grid development with sector coupling, and increasing the focus on blue hydrogen, which appear to not receive much focus, as opposed to green hydrogen. Overall, this work may serve as a useful resource for newcomers to grid-related research and a practical guide for seasoned researchers in the field. [ABSTRACT FROM AUTHOR]

Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative—Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller's parameters are meticulously optimized using the Coati Optimization Algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search Algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalances. Metaheuristic algorithms in MATLAB-SIMULINK platforms are employed to identify the controller's gains across all case studies, ensuring the maintenance of IUMG system frequency within predefined limits. Simulation results convincingly establish the superiority of the proposed controller over other counterparts. Furthermore, the controller's robustness is rigorously tested under ± 25% variations in specific IUMG parameters, affirming its resilience. Statistical analyses reinforce the robust performance of the COA-based 1PD-3DOF-PID control method. This work highlights the potential of the COA Technique-optimized 1PD-3DOF-PID controller for IUMG control, marking its debut application in the LFC domain for IUMGs. This comprehensive study contributes valuable insights into enhancing the reliability and stability of Islanded Urban Microgrids while integrating Mobile EV Energy Storage, marking a significant advancement in the field of Load-Frequency Control. [ABSTRACT FROM AUTHOR]

Background: Renewable energies are key to reduce CO2 emissions and other environmental impacts of fossil-fueled electricity generation. However, renewable energy systems can also cause negative environmental effects. In this paper, we analyze the potential environmental trade-offs associated with different spatio-technical (de)centralization options for a renewable electricity system. For this purpose, we first review the potential environmental life cycle impacts of key technologies for renewable electricity systems. Subsequently, we develop a framework identifying which factors determine actual environmental effects of renewable electricity systems. We apply the framework to four basic spatio-technical (de)centralization options for the future Germany electricity system. Results: Our analysis shows that all (de)centralization options are associated with potential environmental trade-offs. We find that the (de)centralization of the system is a relevant factor determining these trade-offs. For instance, the two more centralized options considered have lower environmental impacts related to PV, whereas the two more decentralized options have lower environmental impacts related to grid infrastructure. However, we also find that the trade-offs depend on the specific way (de)centralization is pursued. For instance, only in one of the two considered more decentralized development options, there is a potential environmental trade-off between higher impacts related to battery storage and lower impacts related to offshore wind power. Conclusions: Our analysis reveals that the spatio-technical (de)centralization of a renewable electricity system plays a role for its environmental trade-offs while further factors like the institutional and stakeholder management in place also shape the environmental trade-offs. Policy makers should acknowledge the identified potential environmental trade-offs and their influencing factors when making policies favoring certain spatio-technical (de)centralization options. Highlights: Review of potential environmental impacts of key renewable technologies Framework on determinants of actual environmental effects of electricity systems Review of electricity system (de)centralization scenarios for Germany Application of framework to four spatio-technical (de)centralization options Environmental trade-off analysis for the four (de)centralization options [ABSTRACT FROM AUTHOR]

Due to increasing load demand and the energy crisis, microgrids (MGs) have attracted more attention. The idea and technology of microgrids (MGs) have undergone significant advancements largely aimed at enabling the automation of distribution systems and enhance the integration of renewable energy sources (RESs). In this paper, the MG is a combined form of various distributed generations (DGs), battery energy storage system (BESS), and plug-in hybrid electric vehicles (PHEVs). A novel approach is introduced to minimize the operational expenses of the grid-connected low-voltage microgrid by leveraging a probabilistic expert optimization technique. This methodology takes into account various factors such as power supply costs, power dissipation, PHEV charging requirements, and the optimal capacity of BESS. Three charging techniques are proposed in this paper to control the PHEVs' charging demand at homes and public charging stations. This study employs a comprehensive model to simulate a whole day, accounting for the stochastic charging requirements of PHEVs, load profiles, RESs outputs, and variations in energy price. To address the inherent nonlinearity of the optimization problem, the energy management within the MG is tackled using the slime mould algorithm, a population-based stochastic optimizer. The primary goal is to minimize operational costs in the microgrid by optimizing the efficient utilization of available resources. [ABSTRACT FROM AUTHOR]

The pursuit of economic growth has implications for carbon emissions and climate change. Achieving low carbon development is important for attaining the targets of the sustainable development goals. Africa is often described as a largely import-dependent continent. The continent also requires significant investment in information and communication technologies (ICT) and renewable energy to achieve low-carbon economic growth. However, empirical evidence on the joint impacts of imports of goods and services, clean energy use, ICT, and economic growth on carbon emissions in Africa is scanty and mixed. This paper investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions by using rich data on total per capita carbon dioxide (CO2) emissions, economic growth, import of goods and services, renewable energy consumption, fixed telephone subscriptions, mobile cellular subscriptions, and individuals using the internet in Africa (2001 ─ 2020) obtained from the World development indicators (WDI) database. Using the Panel autoregressive distributed lag model (PARDL), we found that mobile cellular subscriptions, and level of economic growth significantly increased per capita CO2 emissions in Africa in the long run while renewable energy consumption and technologies and import of goods and services significantly decreased per capita CO2 emissions in the long run. We conclude that information and communication technologies, level of economic growth, import of goods and services, and renewable energy consumption exert impacts on carbon emissions in Africa. Highlights: Achieving low-carbon development in Africa is possible. This research investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions in Africa. This study utilized the Panel autoregressive distributed lag model and panel data from 31 African countries. The results shed new light on the emission reduction potential of renewable energy consumption and import of goods and services. The study also shows that ICTs increase carbon emissions in Africa. This article offers recommendations concerning carbon emissions reduction. [ABSTRACT FROM AUTHOR]

This paper evaluates and discusses the proposed design for a low carbon environmental village in Al-Baha region. It considers its viability in view of local renewable energy sources, climatic conditions and local vernacular architectural identity. Five areas were targeted when designing the village: (a) low carbon architectural design strategies; (b) on-site renewable energy strategies; (c) selection of local construction materials; (d) exploitation of rain water harvesting and greywater recycling; and (e) waste disposal recycling and management. The used approach is to design a sustainable village with its facilities in prospective of environmental requirements social needs. The design follow application five roles' strategies from the literature and depending on local site challenges, available of natural in a location of Al-Baha gate from the airport. The site selected is located along the main highway that connects Al-Baha city to its nearest airport. The chosen area is 4,945,000 m2 and has a flat topography with mountains located relatively close by. The findings present in the study plans for comprehensive sustainable low carbon energy village, which meet the climatic requirements and exploiting natural resources operated by natural renewable energy. This village is designed for typical Saudi families, and will be built from local construction materials. The design includes more than 6150 residential units powered by photovoltaic (PV) panels, and local services using wind energy. The energy requirements for the whole village were calculated based on macro and micro energy generation. Local regulatory standards and sustainability criteria have also been met for the proposed village. Five experts also evaluated the five-purpose design proposition. The impact of designing sustainable villages will lead in protection of environment improved the life and health quality as well as economic benefits. The benefits of addressing five aims are highlighted and the study concluded with some recommendations. [ABSTRACT FROM AUTHOR]

The rising global energy demand necessitates innovative solutions for harnessing renewable energy sources. Solar ponds have received attention as they present a viable means to address this challenge by absorbing and storing solar radiation. This article provides a comprehensive review of solar pond technology, including its principles, applications, heat extraction mechanisms, and approaches to optimize performance, with special attention to the salt-gradient solar pond. Additionally, the article identifies challenges that currently hinder the large-scale adoption of solar pond technology and offers recommendations for future research. By providing a detailed analysis of the current trends and future research directions, this paper seeks to contribute to the ongoing efforts to improve these systems, exploring various approaches to increase their efficiency and make them more economical and environmentally sustainable. [ABSTRACT FROM AUTHOR]

Hydrogen emerges as a promising alternative energy source, particularly in fuel cell applications, necessitating efficient and safe charging and storage systems. This paper presents the design and development of a specialized regulator tailored for high-pressure hydrogen environments. Focusing on precision control, the regulator ensures optimal performance and safety during charging and storage phases. In order to improve the unstable response characteristics of the existing commercial hydrogen regulator, which has the same differential pressure generation site and pressure control site, a regulator operating at an ultra-high pressure of 105 MPa was designed by separating the differential pressure site and the control site. And structural safety and performance of the regulator were predicted through structural and flow analysis. The results of structural and flow analysis of a 105 MPa high-pressure regulator that can be used for high-pressure hydrogen charging and storage systems were compared with the experimentally measured values. [ABSTRACT FROM AUTHOR]

Background: The transition to energy-sustainable systems is a globally accepted concept, but it is implemented with various degrees of success around the world. The aim of this paper is to determine the status of green transition in five Central Asian countries (Tajikistan, Turkmenistan, Uzbekistan, Kazakhstan, and the Kyrgyz Republic) that are among the highest energy consumers globally and the most vulnerable to climate change. Despite a notable improvement in living conditions across all countries over the past two decades, the region remains underdeveloped. Economic growth is based on the export of natural resources and remittance inflows, but the structure of the economy is monolithic. Main findings: Upon analysing transition indicators for the period 2000–2020, no evidence of significant changes towards a sustainable energy transition is found. All countries in the region (except Uzbekistan) have recorded an increase in carbon emissions, while the carbon intensity of the energy mix is almost the same. While there has been visible progress in reducing energy intensity, the countries in the region remain among the most energy-intensive economies. Despite the region's substantial potential for renewable energy production, it remains underutilized. Conclusions: A multitude of varied problems accompanies the green transition in Central Asian countries. The energy market that has not yet been liberalised, along with significant subsidies and low energy tariffs, discourages investments in renewable energy sources and energy efficiency initiatives. A high level of corruption, rigid legislative and institutional infrastructure, and insufficient capital are additional factors that determine the speed, scope, and effectiveness of the green transition. Thus far, the primary focus in these countries has been on ensuring energy security. However, there has been a discernible surge in investments (particularly from China) in renewable energy projects. Although energy efficiency and climate change issues are formally defined, the energy transition goals are often declarative without an action plan that provides concrete steps in transition process. Strategic planing of economic development that includes all related sectors (not only the energy sector) and paying attention to socio-economic and environmental indicators of sustainable development are prerequisites for successfully implementating the energy transition in these countries. [ABSTRACT FROM AUTHOR]

The present energy demand of the world is increasing but the fossil fuels are gradually depleting. As a result, the need for alternative fuels and energy sources is growing. Fuel cells could be one alternative to address the challenge. The fuel cell can convert CO2 to value-added chemicals. The potential of bio-fuel cells, specifically enzymatic fuel cells and microbial fuel cells, and the importance of immobilization technology in bio-fuel cells are highlighted. The review paper also includes a detailed explanation of the microbial electrosynthesis system to reduce CO2 and the value-added products during microbial electrosynthesis. Future research in bio-electrochemical synthesis for CO2 conversion is expected to prioritize enhancing biocatalyst efficiency, refining reactor design, exploring novel electrode materials, understanding microbial interactions, integrating renewable energy sources, and investigating electrochemical processes for carbon capture and selective CO2 reduction. The challenges and perspectives of bio-electrochemical systems in the application of CO2 conversion are also discussed. Overall, this review paper provides valuable insights into the latest developments and criteria for effective research and implementation in bio-fuel cells, immobilization technology, and microbial electro-synthesis systems. [ABSTRACT FROM AUTHOR]

This paper presents the MPPT algorithm for the proposed hybrid energy system, i.e., PV and wind systems, and is tested for grid-connected and standalone utilities. This study's major objective is to maximize the power output and enhance the power quality of the hybrid renewable energy technologies that have been created. In the literature, many research scholars have worked in the field of MPPT calculations for PV and wind systems, which increases the number of publications in the field of control calculations for hybrid power systems. This study pays attention to designing a controller based on MPPT for extracting maximum energy from wind and PV energy systems. The suggested algorithm is tested in grid and standalone modes, considering variable weather conditions. The proposed system is designed for 500 W solar and wind plants with the usual step-up converter and tested in MATLAB software to judge the efficacy of the suggested controller designed for MPPT. In this work, the suggested radial basis function network is implemented in wind and PV facilities via MPPT technique-based ANN control to obtain the maximum power by using fuzzy controllers. [ABSTRACT FROM AUTHOR]

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results. [ABSTRACT FROM AUTHOR]

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a modular multiport DC-DC converter for hybrid charging station. The system is supplied by renewable energy sources (RES) like solar photovoltaic system (SPV), wind energy system (WE) and fuel cell (FC). The proposed converter has several benefits like current sharing capability, providing lower current ripple and lower voltage ripple, lower current stress on the switches, lower current rating of switches and using a small-sized inductor. Due to the unavailability of continuous flow of power from the RES throughout the day, an effective energy management system using the adaptive neuro-fuzzy inference system (ANFIS) is implemented in the proposed system. ANFIS controller is implemented to identify and enable the available power from the renewable energy sources to feed the base load as well as charge the batteries in the hybrid charging station. The ANFIS controller continuously monitors the power availability for the charging station, thereby feeding minimum power to the base load. The proposed system is simulated and tested using the MATLAB/SIMULINK software, and a prototype of the same has been developed in the laboratory. From the simulation and experimental results, it is observed that the proposed converter suits best for integrating hybrid sources and used for charging the EVs. Improved life expectancy of the battery is obtained using the proposed converter. The proposed converter produces an efficiency of 97% and is superior to the other existing converters. [ABSTRACT FROM AUTHOR]

Decarbonization of energy systems has been a recent trend during the last two decades where large-scale renewable energy sources (RES) are integrated into the modern power systems. Various countries have developed new energy policy instruments, such as Renewable Energy Certificates (RECs), to promote the growth of RES. RECs are tradable, non-tangible assets, which have a monetary value. Tracking and certification of the origin of an energy resource regardless of its type (e.g., a conventional power plant or RES) are a critical operation. In addition to the certification of origin, trading transactions must be performed using a secure method. Energy industry participants need to secure the data and applications related to RECs. Digitalization technologies such as artificial intelligence (AI) and distributed ledger technology (DLT) are among the most popular and promising options. DLT is a perfect framework that can support such REC functionalities. This paper addresses the cybersecurity aspects in REC trading using blockchain and distributed ledger technology, considering detailed cybersecurity perspectives and aspects of adopting technology from an organizational perspective. [ABSTRACT FROM AUTHOR]

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031. [ABSTRACT FROM AUTHOR]

The overwhelming growth of cloud computing has introduced new challenges for the cloud infrastructure. Many cloud service providers (CSPs) have been adopting renewable energy (RE) sources to increase profitability and reduce carbon emissions. Therefore, recent literature focuses on managing cloud infrastructure with RE sources in addition to traditional non-renewable energy (NRE) sources whenever required. However, these works aim to maximize the usage of RE or minimize the cost without considering the uncertainty (UN). This paper presents three user request-based scheduling algorithms, namely UN-based future-aware best fit, UN-based round-robin, and UN-based highest available renewable first, by managing the UN of RE and NRE. These algorithms consider two types of UN, namely UN-RE and UN-NRE, concerning the user request. The proposed algorithms undergo a rigorous simulation process with 200 to 2000 user requests and 20 to 200 datacenters and are compared using overall cost, UN cost and time, and the number of used RE resources. [ABSTRACT FROM AUTHOR]