Search

Purpose: This research seeks to delve into the potential of solar power as a sustainable and renewable energy solution, specifically examining its effectiveness in addressing energy poverty within the complex framework of the Indian energy landscape. Methods: Employing a mixed-methods approach, the research conducts an extensive literature review to establish the current knowledge landscape, identifying gaps and contextualizing the global and Indian scenarios. Quantitative analysis utilizes statistical data from sources such as the International Energy Agency, governmental reports, and research organizations to track the historical evolution of India's energy supply and solar power capacity. Case studies from China, India, and Bangladesh are presented to draw insights from successful solar projects. Additionally, policy analysis evaluates the effectiveness of current and past energy policies in India in promoting solar adoption and mitigating energy poverty. Results: The study unveils trends, and the impact of policy interventions in India's energy landscape. Comparative analyses position India within the global solar PV market. Case studies illustrate successful solar projects' impact on alleviating energy poverty. Policy analysis provides insights into the strengths and weaknesses of existing energy policies. Conclusion: Solar power emerges as a promising solution to end energy poverty, demonstrating significant cost competitiveness and sustainable attributes. The research underscores the need for targeted policies, financial incentives, and technological innovations to overcome challenges. The findings contribute to the discourse on renewable energy's role in sustainable development, emphasizing the potential for solar power to address energy poverty in India and beyond. [ABSTRACT FROM AUTHOR]

Featured Application: This article developed an improved InfoGAN wind–solar–load extreme scenario generation approach that can inform power system evaluation in extreme scenarios. In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system. [ABSTRACT FROM AUTHOR]

This review paper examines the burgeoning field of energy harvesting on highways, consolidating insights from various technologies: piezoelectric, thermoelectric, pyroelectric, electromagnetic, and solar. By summarising and comparing multiple papers, this review provides a valuable resource for researchers, engineers, and policymakers. The surveyed papers encompass theoretical modelling and practical experiments, showcasing the feasibility of highway energy harvesting. Some works introduce innovative ideas, expanding the field's boundaries. This review presents concise overviews of each technology's principles, workings, challenges, as well as their respective pros and cons. By offering a balanced assessment, it informs future research, investment, and policy decisions. The convergence of these technologies on highways offers a pathway to a more sustainable and efficient transportation system, minimising environmental impact while generating valuable energy resources. This review contributes to the dialogue on energy sustainability and infrastructure development. [ABSTRACT FROM AUTHOR]

Reducing emissions from energy production and enhancing the capacity of land use systems to store carbon are both important pathways towards greenhouse gas neutrality. Expanding photovoltaics (PV) contributes to the former, while the rewetting of drained peatlands preserves the peat soil as long-term carbon store, thus contributing to the latter. However, both options are usually considered separately. This study analyses Peatland PV, defined as the combination of open-space PV with the rewetting of peatlands on the same site, and has an explorative and field-defining character. Due to a lack of empirical data, we used expert interviews to identify the strengths and weaknesses, opportunities, and threats of Peatland PV in the sparsely populated and peatland-rich state of Mecklenburg-Western Pomerania in North-East Germany. The material was analysed using a qualitative content analysis and compiled into SWOT and TOWS matrices. Besides the ecological and technological dimensions, this study focuses on the economic and legal framework in Germany. We found that Peatland PV may mitigate land use conflicts by contributing to climate and restoration targets, energy self-sufficiency, and security. Continued value creation can incentivize landowners to agree to peatland rewetting. Technical feasibility has, however, a significant influence on the profitability and thus the prospects of Peatland PV. Although Peatland PV has recently been included in the Renewable Energy Sources Act (EEG), several specialised legal regulations still need to be adapted to ensure legal certainty for all stakeholders. Pilot implementation projects are required to study effects on vegetation cover, soil, peatland ecosystem services, biodiversity, hydrology, and water management, as well as to analyse the feasibility and profitability of Peatland PV. [ABSTRACT FROM AUTHOR]

To reduce energy consumption and secondary contamination of soil, using natural biosurfactants-tea saponins as enhancement agent was studied on electrokinetic remediation (EKR) of cadmium (Cd)-spiked soil powered by solar energy. The current during EKR, the pH of soil before and after remediation, the initial and the residual Cd in the soil, Cd collected in the electrolyte, the mass balance of Cd, and cost analysis and energy consumption were investigated. Analytical methods such as Fourier Transform Infrared Spectrometer (FTIR), Scanning Electron Microscope (SEM), and Energy Dispersive Spectroscopy (EDS) were applied. Experimental results showed that tea saponins could increase the removal of Cd from the soil by promoting Cd desorption and its complexation that increased its mobility. There was a significant positive correlation between the concentration of tea saponins and Cd removal efficiency, but this positive correlation gradually decreased and the most suitable concentration of tea saponins was 2 g/L under the conditions of this experiment. EKR driven by solar power (Treatment time: 180 h) can achieve the Cd removal efficiency of approximately 90% which was a promising technology for soil remediation, especially in regions with abundant sunshine year-round. FTIR analysis indicates that tea saponins can be quickly and naturally degraded without resulting in secondary pollution to the soil. According to SEM and EDS analysis, most of the Cd pollutants in the soil were removed by EKR, but some pore clogging still existed. [ABSTRACT FROM AUTHOR]

Accurate parameter identification plays a crucial role in realizing precise modelling, design optimization, condition monitoring, and fault diagnosis of photovoltaic systems. However, due to the nonlinear, multivariate, and multistate characteristics of PV models, it is difficult to identify perfect model parameters using traditional analytical and numerical methods. Besides, some existing methods may stick in local optimum and have slow convergence speed. To address these challenges, this paper proposes an enhanced nature‐inspired OLARO algorithm for PV parameter identification under different conditions. OLARO is improved from ARO incorporating existing opposition‐based learning, Lévy flight and roulette fitness‐distance balance to improve global search capability and avoid local optima. Firstly, a novel data smoothing measure is taken to reduce the noises of I–V curves. Then, OLARO is compared with several common algorithms on different solar cells and PV modules using robustness analysis and statistical tests. The results indicate that OLARO has better ability than others to extract parameters from PV models such as single diode, double diode, and PV module models. Moreover, the convergence performance of OLARO is more excellent than the other algorithms. Additionally, the I–V curves of two PV modules under different irradiance and temperature conditions are applied to verify the robustness of the proposed parameter extraction algorithm. Besides, OLARO is successfully applied to two real operating PV modules, and it is compared with two recent well‐known methods improved by FDB. Finally, sensitivity analysis, stability analysis, and discussion of practical challenges are provided. [ABSTRACT FROM AUTHOR]

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems. [ABSTRACT FROM AUTHOR]

Combined hydrogen, heat, and power (CHHP) is the cogeneration unit simultaneously meeting hydrogen, heat, and power demands. Due to the low efficiency of the electrolyser, the main grid or other generation units supply its input power. Herein, the photovoltaic farm's (PV farm) output power is used as the input power of the electrolyser. Optimal economic and emission scheduling of CHHP‐based microgrid (CHHP_MG) is defined as an objective function. To improve system efficiency, the scheduling problem is done in two cases, reconfigured SP‐PV farm and conventional SP‐PV farm. To optimal management of both demand and generation sides, a time of use (TOU) based demand response program is employed in the proposed short‐term study. To have a comprehensive study, the optimum solar panel tilt angle is calculated, and its impacts on the main function are analysed. Multi‐objective genetic algorithm is used to solve optimization problems. By solving the objective function, the obtained values for profit and pollution amount are 620$ and 69,245.78 gr, respectively. By applying the load response program, the mentioned values are changed to 715.132$ and 6872.5 gr. Since the input power is supplied through solar panels, the placement angle of the panels is very important and with the optimal setting of the placement angle, the values change to 780$ and 6825.3 gr. The main contributions of this work are optimal reconfigurable PV farm, evaluating the impacts of reconfiguring the efficiency of CHHP, employing the DR program on the scheduling of RSF‐CHHP, and analysing the role of tilt angle in the scheduling of RSF‐CHHP. [ABSTRACT FROM AUTHOR]

This study provides a comparative analysis of feasible architectures of Power Pulsating Buffer (PPB) as an actively controlled energy storage solution alternative to the electrolytic capacitors in a single‐stage isolated, single‐phase 120 V AC 60Hz grid‐connected photovoltaic (PV) microinverter for a 400 W rated system with 20–40 V input range. The comparison encompasses key factors including energy density, volume reduction, efficiency, frequency operation, and system stability. The findings shed light on the primary advantages and disadvantages of the buck‐ and boost‐derived PPB under study. The results indicate that the boost PPB exhibits enhanced stability, a more compact design, and higher power efficiency compared to the buck PPB. However, for low switching frequency operations, the buck PPB proves to be more effective in mitigating DC port voltage ripple and ensuring controller over microinverter load variation. [ABSTRACT FROM AUTHOR]

Generating hydrogen by electrolysis in an alkaline system with a green power source consisting of wind turbines (WTs) and photovoltaic (PV) power is a promising and sustainable way to produce clean hydrogen to reduce greenhouse gas emissions. This study utilized TRNSYS 16 software to perform a dynamic simulation of a hydrogen system. TRNSYS, which stands for Transient System Simulation Program, is a software package designed for simulating the dynamic behaviour of thermal and electrical energy systems. It is widely used to analyze and optimize the performance of various energy systems. This system incorporated a PV power source and a WT for electricity generation, along with an electrolyzer for hydrogen production. The analysis was carried out to evaluate variable weather conditions, specifically wind speed, solar radiation, and temperature. These factors have a direct impact on the system's performance, influencing the available power as a consequential outcome. The results reveal that, given the specific climate conditions in the Markham zone, Toronto, the integrated renewable system is capable of consistently providing electricity and meeting the load demand throughout the entire year. However, it is noteworthy that on cold days when solar radiation is limited, the WT emerges as the most effective and efficient power source. The analysis also indicates that the system reliably supplies enough energy to meet the laboratory's load demand. Moreover, the system's performance is particularly impressive with the WT as the power source, as it can generate a maximum of 9.03 kg of hydrogen per month. In contrast, the PV power source yields only 0.58 kg H2. Additionally, the cost per kilogram of hydrogen (kg H2) is considerably lower when the WT is used, at USD 0.55/kg H2, while it rises to USD 1.5/kg H2 when PV is the power source. These findings underscore the significance of using the most suitable power source, such as a WT, in specific climatic conditions and regions in terms of both performance and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Sensitivity to ambient air temperatures, consuming a large amount of fuel, and wasting a significant amount of heat dumped into the ambient atmosphere are three major challenges facing gas turbine power plants. This study was conducted to simultaneously solve all three aforementioned GT problems using solar energy and introducing a new configuration that consists of solar preheating and inlet-air-cooling systems. In this study, air was preheated at a combustion chamber inlet using parabolic trough collectors. Then, inlet air to the compressor was cooled by these collectors by operating an absorption cooling cycle. At the design point conditions, this novel proposed integration resulted in a 6.87% relative increase in generated power and a 10.53% relative decrement in fuel consumption, achieving a 19.45% relative increment in the plant's thermal efficiency. This was accompanied by a reduction of 0.026 kg/s, 4.2 kg/s, and 0.278 kg/s in CO2, CO, and NOx emissions, respectively. Finally, spider diagrams were employed to assess the impact of the operating parameters on the overall system's performance and its associated environmental implications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This abstract depicts a novel kind of solar-powered grass mower that is capable of being controlled remotely and can avoid obstacles thanks to the Internet of Things technology. This design offers an eco-friendly and effective solution that aims at redefining lawn care. By lowering reliance on fossil fuels, green energy usage promotes sustainability. During operation, an integrated collector accumulates grass clippings, streamlining trash management. This discovery unlocks the door for intelligent, user-friendly robotic grass cutters that can navigate obstacles by utilizing cutting-edge technologies, ushering in a new era of automated and sustainable lawn care. [ABSTRACT FROM AUTHOR]

We quantify the effect of dust accumulation at Jezero crater by means of a Dust Correction Factor (DCF) for the solar radiation measured by the photodiodes of the Radiation and Dust Sensor of the Mars 2020 mission. After one Mars Year, dust on the photodiode surface attenuated 25%–30% of the incoming solar radiation. The DCF did not decrease monotonically; we use a model to reproduce its evolution and to derive dust deposition and lifting rates, showing that dust removal is 9 times larger at Jezero crater than at InSight's location in western Elysium Planitia. The model fit obtained using observed opacities is further improved when fed with dust sedimentation rates simulated by a GCM that considers a particle size distrtibution. Projections show seasonal net dust removal, being encouraging for the long‐term survival of solar‐powered missions to Jezero or similarly active dust lifting regions. Plain Language Summary: Dust is ubiquitous in the Martian atmosphere, accumulating on both natural and artificial surfaces. Dust particularly affects the performance and lifetime of missions: the termination of InSight and MER‐B operations are recent examples. Dust accumulation shows a seasonal behavior, and attenuated 25%–30% of the incoming solar radiation on Perseverance after the first Mars Year of the mission. Dust removal is almost 10 times larger than at InSight's location: projections indicate that surfaces at Jezero will be periodically partially cleaned. The estimations of the effect of the accumulated dust as a function of time are encouraging for solar‐powered missions to regions with similar amounts of dust lifting, which might be determined from orbital data on where dust storms originate, dust devils or their tracks are found, or seasonal albedo changes are noted. In addition, the quantification of the effect of accumulated enables future studies requiring more accurate knowledge of incoming solar radiation at the surface. Key Points: We present the evolution of dust accumulation at Jezero crater for more than one Mars YearWe derive dust deposition and removal rates: removal is 9 times more efficient than at the InSight location in western Elysium PlanitiaProjections show that surfaces at Jezero will experience seasonal net dust removal, encouraging solar‐powered missions [ABSTRACT FROM AUTHOR]

Debates on technologies for harnessing renewable energies tend to generate a polarized arena in which critical voices are automatically denounced as defenders of fossil energy. This has created a difficult situation for activists and scholars voicing concerns about the comparatively low power density, low net energy, and environmental justice concerns of such technologies. In this paper, we highlight the contradictions and ambivalences underlying Promethean arguments for solar power, which are currently dividing the political left. Through a critical reading of three proponents of classical Marxism, we address the structural coherence and paradoxes of the discourses within which the faith in such "green" technology is mobilized. We illustrate how Promethean visions of solar power tend to suffer from a pervasive ontological separation of human ingenuity and global social metabolism. This raises important questions about the ambiguous and theoretically underdeveloped role of technology in historical materialism, and about how capital, once converted into the material form of technology, becomes exempt from political critique. Rather than accepting such an immaterial and ultimately depoliticized position on technology, we argue that Marxist scholarship should concede that Promethean approaches must be abandoned if we are to effectively address climate change and other challenges of the Anthropocene. [ABSTRACT FROM AUTHOR]

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor. [ABSTRACT FROM AUTHOR]

We evaluate the conditions and consequences of union strategies in three industrial transitions in California, all driven by its globally influential climate policies: in construction (solar power plants), electricity (nuclear power retirement), and manufacturing (electric vehicles). Building on recent, global frameworks in environmental labor studies, we grow the field's attention towards unions with workers transitioning between 'sunset' industries like fossil energy and 'sunrise' sectors like clean energy. Using original ethnographic and archival data, we analyze the conditions that shaped union strategies in transitions, and how these in turn impacted union power and coalitions. We argue that union strategies embraced climate transitions when they organized leverage to define a shift on their members' terms, independently from employers, often by turning environmental regulation to the advantage of labor and its allies. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

In renewable energy-sourced power systems, asynchronous motors serve as elements that both produce and consume electricity. The stability and efficiency of the asynchronous motor in renewable energy system should be considered. Although performance optimization studies have been carried out using power circuits that control the asynchronous motor fed with solar energy and control units such as proportional, integral, derivation (PID), and Fuzzy logic; They may be insufficient to eliminate and reduce vibrations in the output values of an induction motor fed with a renewable energy source. Losses occur as the system performance decreases constantly due to oscillations. In this study, an induction motor power system is optimized with the extra control coefficient (ECCA)-PID, which provides semi-linear control for the first time, unlike the power systems controlled by other linear controllers used so far to reduce oscillations, and the vibration losses are eliminated. The speed oscillations, torque oscillations, and oscillation losses of the induction motor in the system are controlled by conventional PID, and Fuzzy logic controllers in order to demonstrate the effectiveness of the proposed method in simulation and experimentation. Then, the results are compared after the proposed method are examined. As a result of the comparisons, while the motor oscillations and losses in the power system continue throughout the control period in traditional control methods, the proposed method reduces the losses to zero in a large part of the control period. [ABSTRACT FROM AUTHOR]

Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting. [ABSTRACT FROM AUTHOR]

High‐gain converters play a vital role in all industrial applications such as automobiles, motor drives, renewable energy systems, and railway transportation sectors. The conventional converters have low gain owing to high‐voltage stress on active MOSFET, diode and low efficiency related with high operating pulse ratio. To overcome the limitations associated with the conventional converters, this study proposes a non‐isolated configuration of multiport quasi‐resonant converter (MP‐QRC) with high gain. The proposed MP‐QRC has the merits of high‐voltage gain, reduced number of components, minimum conduction losses, realization of soft switching in the devices and improved reliability. Moreover, the proposed converter is scalable and can serve as a good candidate in microgrid environment where the integration of more than one input is essential. In this paper, first a comprehensive analysis of various operation modes and design constraints are presented. Further, the study is supported with a reliability evaluation of proposed converter based on component failure. Also, the futuristic behaviour of MP‐QRC under continuous conduction mode as a function of operational and environmental variables is investigated to ascertain the reliability. The steady‐state operation of the converter is demonstrated for off‐board electric vehilce (EV) charging using MATLAB/SIMULINK and experiments performed on a 300‐W test rig. [ABSTRACT FROM AUTHOR]

Precise forecasting of solar power is crucial for the development of sustainable energy systems. Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic (PV) power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data. To overcome these challenges, this research presents a cutting-edge, multi-stage forecasting method called D-Informer. This method skillfully merges the differential transformation algorithm with the Informer model, leveraging a detailed array of meteorological variables and historical PV power generation records. The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics, achieving on average a 67.64% reduction in mean squared error (MSE), a 49.58% decrease in mean absolute error (MAE), and a 43.43% reduction in root mean square error (RMSE). Moreover, it attained an R² value as high as 0.9917 during the winter season, highlighting its precision and dependability. This significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism, which greatly enhances the model's ability to identify complex interactions among diverse input variables, and the inclusion of weather variables, enriching the model's input data and strengthening its predictive accuracy in time series analysis. Additionally, the experimental results confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

As both the population and economic output of India continue to grow, so does its demand for electricity. Coupled with an increasing determination to transition to net zero, India has responded to this rising demand by rapidly expanding its installed renewable capacity: an increase of 60% in the last 5 years has been driven largely by a quintupling of installed solar capacity. In this study, we use broad variety of data sources to quantify potential and realized capacity over India from 1979 to 2022. For potential capacity, we identify spatiotemporal patterns in solar, wind, hydro and wave power. We show that solar capacity factor is relatively homogeneous across India, except over the western Himalaya, and is highest during the pre‐monsoon. Wind capacity factor is highest during the summer monsoon, and has high values off the southern coast, along the Western Ghats, and in Gujarat. We argue that wave power could be a useful source of renewable energy for the Andaman and Nicobar Islands, which are not connected to the main Indian power grid. Using gridded estimates of existing installed capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model. We use this model to identify weaknesses in the existing grid—particularly a lack of complementarity between wind and solar production in north India, and vulnerability to high‐deficit generation in the winter. We discuss potential avenues for future renewable investment to counter existing seasonality problems, principally offshore wind and high‐altitude solar. [ABSTRACT FROM AUTHOR]

Off-grid solar technologies have emerged as one of the remedies to Africa's severe lack of access to electricity. Evidence from Ghana, Malawi, Tanzania, and Kenya suggests that these systems positively affect income generation, education, health, and safety and security. Despite its potential, off-grid solar power's technical limitations, combined with concerns about durability and environmental impact, underscore the need for more comprehensive solutions. Addressing Africa's electrification crisis requires a multifaceted approach involving collaboration among various stakeholders to ensure sustainable, reliable, and inclusive energy access. [ABSTRACT FROM AUTHOR]

Modern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies. [ABSTRACT FROM AUTHOR]

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

The article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results. [ABSTRACT FROM AUTHOR]

In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN. [ABSTRACT FROM AUTHOR]

Energy sources are crucial for the development and growth of economies and civilizations. Solar energy is an alternative energy to generate electrical power. The challenges of solar photovoltaic panels (PV) are the low output power and efficiency and the huge installation area beside PVs need a tracking system for better efficiency. The motivation of this paper is to design an innovative solar sphere system, which is a new concentrated photovoltaic technology that has better performance (efficiency and output power) than the normal conventional solar panel (PV) with a smaller installation area and without any tracking system. This design consists of an acrylic solar sphere entirely filled with cooking oil (sunflower or corn oil) that captures solar radiation and concentrates it on a focal point. The focal point is adjusted over a multi-junction cell that acts as a collector device (concentrator solar cell). This focused solar energy can generate a massive amount of power, which is used to produce more electricity than normal photovoltaic panels. The experiments were carried out in order to discover the best acrylic models or shape designs, which is the sphere, the best materials or media in the sphere, that is oil, the best sphere's size and volume, and that is larger, the best sphere thickness, which at first is lower, the best fluid oil type, which is cooking oil, and finally the best fluid amount or volume inside the sphere, and this is the entire volume. Then, these factors mentioned above are compared with normal photovoltaics (PV) that have the same section area as these shapes. The results revealed that these factors have significant effects on the output power value and efficiency. It has been demonstrated that our innovative concentrated solar sphere system can produce nearly four times the output power or electricity greater than that of a conventional solar panel PV with the same cross-sectional area. This specific sort of compression is crucial because it shows that less space is required to establish this system than it would to install conventional solar panels. The performance of the system per unit of the square area it occupies was compared to the latest generation of flat panel PV available at the market performance; hence, the installation space will be decreased by 40% to 60%. Our system has about twice as much efficiency as solar PV and does not require a tracking system and maintenance. Our technology also has the benefit of not being impacted by extreme temperatures, clouds, dust, and humidity. [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]

Climate concerns require immediate actions to reduce the global average temperature increase. Renewable electricity and renewable energy-based fuels and chemicals are crucial for progressive de-fossilization. Hydrogen will be part of the solution. The main issues to be considered are the growing market for H2 and the "green" feedstock and energy that should be used to produce H2. The electrolysis of water using surplus renewable energy is considered an important development. Alternative H2 production routes should be using "green" feedstock to replace fossil fuels. We firstly investigated these alternative routes through using bio-based methanol or ethanol or ammonia from digesting agro-industrial or domestic waste. The catalytic conversion of CH4 to C and H2 was examined as a possible option for decarbonizing the natural gas grid. Secondly, water splitting by reversible redox reactions was examined, but using a renewable energy supply was deemed necessary. The application of renewable heat or power was therefore investigated, with a special focus on using concentrated solar tower (CST) technology. We finally assessed valorization data to provide a tentative view of the scale-up potential and economic aspects of the systems and determine the needs for future research and developments. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become an attractive alternative to IC engine cars due to the increased interest in lowering the consumption of fossil fuels and pollution. This paper presents the design and simulation of a 4 kW solar power-based hybrid EV charging station. With the increasing demand for electric vehicles and the strain they pose on the electrical grid, particularly at fast and superfast charging stations, the development of sustainable and efficient charging infrastructure is crucial. The proposed hybrid charging station integrates solar power and battery energy storage to provide uninterrupted power for EVs, reducing reliance on fossil fuels and minimizing grid overload. The system operates using a three-stage charging strategy, with the PV array, battery bank, and grid electricity ensuring continuous power supply for EVs. Additionally, the system can export surplus solar energy to the grid, reducing the load demand. The paper also discusses the use of MPPT techniques, PV cell modeling, and charge controller algorithms to optimize the performance of the hybrid charging station. [ABSTRACT FROM AUTHOR]

Solar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup. [ABSTRACT FROM AUTHOR]

Utilization of marine photovoltaic energy is primarily focused on surface harvesting with limited photovoltaic cell implementations in submarine environments. Potential applications include marine wildlife telemetry devices, autonomous underwater vehicles, or remote sensing assets. In these applications, understanding the power at depth is critical, but there has yet to be a long‐term study of cell performance in a realistic marine environment that spans time, geographic location, and depth. In this paper, we present photovoltaic assessments carried out by devices mounted to adult female northern elephant seals (Mirounga angustirostris) during their spring migrations in the Pacific Ocean with deployment times between 76 and 107 days. Encompassing a large geographic area between Santa Cruz, California, and the Aleutian Islands of Alaska during their migrations, elephant seal behavior allows for repeated depth profiles each day, making them an ideal host for subsurface power assessments. This paper presents the first longitudinal study of photovoltaic cell performance in the marine environment that spans location, time, and depth. This work discusses the calibration, data time alignment, and power calculations of these oceanic deployments. Deployment results, including power results and energy predictions from the data record, are presented up to 22 m in depth. We highlight how the recorded power data of these cells compares to previously published results and how depth impacts subsurface power and energy harvesting. [ABSTRACT FROM AUTHOR]

The photogalvanic cell, which works on the photogalvanic effect, has the potential for both solar energy conversation and storage. The dye sensitizer is the main material required for fabrication of these cells as it is a light absorbing agent and also an electroactive‐species at both the electrodes. The dye aggregation and dye toxicity are some of the main hurdles in the fabrication of the good and safe photogalvanic cells. The dye materials so far exploited in the photogalvanics have disadvantages like low‐water solubility and/or toxicity. The Sunset Yellow FCF dye is a food dye having very high solubility in the water (19 g/100 mL). This dye material has not attracted the attention of the photogalvanicists. So, in the present study, the use of very safe and highly water‐soluble Sunset Yellow FCF dye has been planned to fabricate safe and efficient photo galvanic cells. In present study, the observed optimum cell performance is as potential 806 mV, current 5400 μA, power 552.0 μW, and conversion efficiency 11.19%. The spectral study shows that the Sunset Yellow FCF dye material is responsible for absorption of the light by the electrolyte. This dye material shows very high photo‐stability as substantiated from the fact that the height of absorption band at wavelength maxima of electrolyte under pre‐illuminated and post‐illuminated conditions is comparable. Further, the power storage capacity is sufficiently robust as the cell is capable of supplying power at its ~80% capacity after very long time of 24 h. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model's predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server. [ABSTRACT FROM AUTHOR]

With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting. [ABSTRACT FROM AUTHOR]

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners' net income by 1.25 times compared to merely installing charging infrastructure. [ABSTRACT FROM AUTHOR]

In Vietnam, comprehensive measures are required to accommodate and encourage the potential development of electric two-wheelers in urban traffic and the rooftop solar power potential. In the case of PV-integrated electric two-wheeler charging stations, numerous vehicles charging simultaneously may trigger very high peak loads which adversely impacts other loads and the distribution grid. In this study, an optimal valley-filling algorithm for electric two-wheeler charging stations is proposed. The proposed algorithm can update the variation of available vehicles as well as the dynamic changes in the energy level of individual E2Ws at each time slot. The simulation results proved that the proposed method can effectively perform valley filling, significantly improving the total load profile compared to uncontrolled charging and average charging schemes. [ABSTRACT FROM AUTHOR]

In the contemporary world, energy stands as an essential driving force behind socio-economic development. However, with the alarming levels of environmental pollution, there has been a growing focus on renewable energy sources. These sustainable alternatives are gaining increasing attention, particularly as the depletion of fossil fuels becomes more ap-parent. Natural components such as sunlight, wind, and rainfall are used to generate renewable energy. Among these non-traditional renewable sources, solar energy emerges as a prominent contender for power generation. Solar power generation has gained recognition as a promising and environmentally sustain-able renewable energy source to meet growing global energy demands while minimizing cli-mate change consequences. This paper extensively examines solar power generation techniques, encompassing Photovoltaic (PV) Systems and Solar Thermal Technologies. The paper explores the present state of solar power generation technology, outlines its advantages, and researches the various challenges obstructing its widespread adoption. Furthermore, the study offers insights into potential solutions and outlines future directions for advancing solar power generation techniques. [ABSTRACT FROM AUTHOR]

In this review, the most recent revelations in the possibilities of integrating various solar collectors with thermoelectric generators (TEGs) and their main promising results are presented. These combined structures produce the normal (thermal, electrical) energy generated by the solar panel with an additional electrical power resulting from the combination with TEG modules, implying a better exploitation of solar radiation. At the beginning, the basic principles of combined solar thermal and electrical conversion systems, including the photovoltaic (PV) panel combined with the thermal collector (Th) constituting the PV/Th design, are summarized. Second, a detailed discussion on the existence and material varieties of thermoelectric generators, recent industrial applications, and parameters affecting the efficiency of thermal-electric conversion are reported. Then, recent feasibility analyses, experimental applications, types, and performance now of photovoltaic-thermoelectric (PV/TE) are reviewed, while TEG convert heat and thermal energy. Subsequently, considered and discussed is contemporary research on the utilization of thermoelectric generators in various stationary and concentrating solar thermal collectors and processes. An extensive examination of the key technical, practical, and experimental aspects of tri-generation solar hybrid systems integration is also summarized. This paper is therefore a very helpful reference for future research in the discipline of solar (PV, Th, PV/Th)-TE and its applications. [ABSTRACT FROM AUTHOR]

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa's ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kW pk /taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country's dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a forecasting system of sunshine radiation for planners to quickly and accurately predict the output of solar power. The field data, including observation time, temperature, relational humidity, wind speed and global radiation, were collected, and the data clusters were embedded in the Excel Database. To improve the computational performance, the data selection technique was used in the stage of data cleaning, data integration and data reduction. Using the Integration of the Radial Basis Function Network (RBFN) and Sliding Mode Control (SMC), a Sliding Mode Radial Basis Function Network (SMRBFN) was proposed to solve this forecasting problem. Since the Sliding Mode Control has the design's sense of optimal parameters, three parameters in the SMRBFN were dynamically adjusted to promote the accurate and reliability of forecasting system. Linking the SMRBFN and Excel database, the learning stage and testing stage of SMRBFN retrieved the input data from Excel Database to perform and analyze the forecasting system. The proposed algorithm was tested on Kaohsiung district in summer and winter. The average prediction error of MAPE and RMSE obtained from the forecasting results are about 9% and 0.223, respectively. It can be proved that SMRBFN can efficiently forecast the sunshine radiation and accurately provide the output of solar power in an uncertainty environment. [ABSTRACT FROM AUTHOR]

Due to the surge in oil prices, alternative energy sources, like solar power, are being explored to meet energy demands. Solar power is utilized in various industries, including agriculture. In Iraq and other developed countries, solar power is actively being developed due to the abundance of solar radiation. In agriculture, solar standalone pump systems with variable-speed drives are used. Electric motors operate at different speeds depending on the availability of sunlight. Inverters convert this solar energy from direct current to alternating current, enabling the powering of motors with a fixed voltage per frequency ratio and regulating motor current consumption. The variation in motor speed affects hydraulic pump efficiency and water productivity, making it crucial to optimize solar energy utilization in agriculture. The angle of inclination greatly affects the effectiveness of solar panels. The optimal tilt angle depends on location, latitude, season, and time of day. Adjusting this angle based on these factors maximizes power output. Innovative installation methods are being employed to enhance the benefits of solar power, reduce costs, and optimize sunlight capture. These methods drive sustainable development in various industries, including agriculture. [ABSTRACT FROM AUTHOR]

The spectral characteristics of outdoor structures, such as automobiles, buildings, and clothing, determine their energy interaction with the environment, from broad-spectrum absorption of light energy to high-efficiency thermal emission. Recently developed spectrally selective absorption (SSA) materials permit the reduction of energy loss from human habitat eco-system in the sustainable way and further reduce the utilization of fossil energy to achieve carbon neutrality. Here we review recent advances in SSA materials that enable rational and efficient management of thermal energy and provide new solutions for the resource base that supports human life like comfortable heat management, electricity production, and water supply. The basic principles of thermal photonic management, the regulation of SSA materials, and functional properties are summarized. An outlook discussing challenges and opportunities in SSA material energy management for comfortable living environments is finally presented, which expects the enormous potential of this interdisciplinary research in solving growing resource-shortage of human society. [ABSTRACT FROM AUTHOR]

A comparative analysis of a combined system comprising organic Rankine cycles (ORC) and supercritical CO2 (sCO2) cycles for concentrated solar power (CSP) applications was performed in the article. Low global warming potential (GWP) fluids were used to investigate strategies for reducing the effects on global warming. In the current study, four different combinations of the sCO2 cycles and ORC, including basic recuperated, pre‐compression, recompression with main compressor intercooling, and partial cooling, were taken into consideration, and their performances with regard to CSP application were compared. The integrated recompression with main compressor intercooling and ORC arrangement was seen to operate thermodynamically more efficient than the other considered configurations. This configuration's highest thermal and exergy efficiencies were discovered to be 51.9% and 55.84%, respectively, and R1243zf was found to be the best‐performing fluid among other considered low GWP fluids to reduce the environmental effects. [ABSTRACT FROM AUTHOR]

In this research, we propose IoT-Based Automatic Hydro-Organic Smart Farming System (AHOSFS) in Greenhouse with Solar Panels for Khok Nong Na, design and development with Internet of Things and solar energy. Data is collected from sensors for the following factors that affect plant growth: humidity, temperature, light, pH, EC, air quality, water temperature, and water level. The Firebase cloud-based application was used to collect all the data, and it enables farmers to monitor and control the system using their smartphones. The results showed AHOSFS can mix water and nutrient solution, which measure pH of 6.61 and EC of 1.23, control water level and mix nutrient solution in appropriate quantities. Green oak can thrive at pH levels between 6.0 and 7.0, EC levels between 1.1 and 1.7, and humidity level between 75 and 85. The sensor measured temperature value between 18 and 25 °C, which was adequate for the growth of green oak. Sensor also measured water temperature between 25 and 28 °C. The comparative value of cultivation in a hydro-organic system deployed between AB solution, organic fertilizer Formula 1 and Formula 2 found that organic fertilizer formula 1 has an effective and approximate AB solution at 81.64%. The fertilizer formula 2 has AB solution at 89.72%, which was more secure at a comparable level. [ABSTRACT FROM AUTHOR]

Water desalination plants powered by solar energy represent a viable solution for addressing a part of the water needs in areas without reliable water supply. In this case extending the electric grid and water delivery to remote communities remain challenging for some countries. Using Namibia as a case study, this work carried out a feasibility study on the extent of which, solar energy could be used to power the entire desalination process thereby aiding groundwater usable for human consumption. This process includes but not limited to desalination of underground wells and boreholes as solution to the water shortage problem in remote areas. In this study, a solar-powered desalination technique that could enhance the process of clean water provision to villagers is proposed with the aim of implementation. It further compares and analyzed various desalination technologies to determine the best fit based on the dryness of areas. Ohawuwanga Village in Namibia was used for evaluation purpose with a sampling instrument-questionnaire which determine the village's daily water consumption and corresponding power required. Modelling, simulation, and analysis of the system was carried out using MATLAB Simulink in terms of energy consumption. Other parameters considered in this research are cost, water recovery ratio, salt removal factor, durability, and market analysis. The research revealed that electrodialysis is the best technology for desalination in terms of efficient power consumption amidst other factors compared. [ABSTRACT FROM AUTHOR]

The paper presents a solution methodology for a dynamic electricity generation scheduling model to meet hourly load demand by combining power from large-wind farms, solar power using photovoltaic (PV) systems, and thermal generating units. Renewable energy sources reduce the coal consumption and hence reduce the pollutants' emissions. Because of uncertain wind velocity and solar radiation, the share of solar and wind units is restricted to certain limits of demand, considering ramping requirements. The solar and wind units start quickly, while the thermal units start slowly. The commitment to renewable energy units is made through a proposed optimistic optimizer to restrict the power generation share. Economic and environmental objectives of thermal units are unified using a price penalty factor, and the hybrid sine–cosine algorithm (HSCA) optimization method solves the problem. A forward approach is applied to handle the dynamic electricity generation scheduling optimization problem. HSCA incorporates local mutation and hill-climbing heuristics to reduce global operating costs and emission pollutants' while allocating generation to the devoted units. To handle the constraints, replacement and heuristic techniques are applied. The proposed method is verified on a system having solar PV units and wind power plants connected to 6, 13, and 40 thermal generating units, respectively. [ABSTRACT FROM AUTHOR]