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Abstract 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 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.

Polymer nanocomposite coatings of solar photovoltaic cells that absorb solar ultraviolet (UV) radiation and convert it into visible and near-infrared (NIR) light can increase the operational lifetime and the energy efficiency of the cells. We report a polymer nanocomposite spectrum converting layer (SCL) made of colorless polyimide CORIN impregnated with the nanoparticles (NPs) of fluoride NaYF4 doped with three-valent ions of Europium at a molar concentration of 60%. The NPs were the nanocrystals (179 ± 35 nm in size) in thermally stable hexagonal beta-phase. The visible-NIR photoluminescence quantum yield of the nano-powder was ∼69%. The SCLs were applied using the open-air multi-beam multi-target pulsed laser deposition method to silicon heterojunction (SHJ), copper-indium-gallium-selenide (CIGS), and inverted metamorphic multijunction (IMM) solar cells. The cells were exposed to UV radiation from a 365 nm light emitting diode. The I–V characteristics of the cells were measured with a solar simulator using AM0 filter. The proposed SCLs improved the UV stability of all three types of the cells: the power degradation of SHJs and IMMs cells was stopped or slightly reversed and the degradation rate of CIGSs decreased by ∼25%. The proposed SCLs have great commercial potential, especially for applications to space power.

Abstract 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.

A rise in the need for the integration of renewable energy sources, such as wind and solar power, has been attributed to the search for sustainable energy solutions. To strengthen community grids and improve access to electricity, this article investigates the potential of combining solar and wind hybrid systems. This is viable approach to address energy-related issues, like grid dependability, energy accessibility, and greenhouse gas reduction. This research focuses on the examination of the environmental, technological, financial, and operational effects, and features of hybrid solar and wind systems for grid support. To further demonstrate the practical uses and advantages of such hybrid systems; case studies are presented. This study attempts to shed light on how solar and wind systems can affect grid resilience, increase electricity accessibility, and shape the direction for sustainable energy in the future. The obstacles preventing the broad use of a hybrid system for grid support were investigated, and the intricacies required in implementing such systems at a large scale are comprehensively presented. It was observed that developments of demand-side management, energy storage technology, and hybrid system optimization algorithms would improve the dependability, effectiveness, and cost of the hybrid system. In summary, the motivation of this study was to provide an effective tool for the interaction of hybrid solar and wind systems in the changing the energy landscape, in order to provide communities with dependable access to electricity, and fostering a more sustainable future. This work offers stakeholders insights into utilizing the hybrid system for electricity access and shift towards cleaner and renewable energy.

Renewable energy stands as a cornerstone for a sustainable future, yet it grapples with a significant obstacle: curtailment—deliberate energy generation reduction to avert grid overload. Curtailment not only squanders clean energy but also poses economic and environmental challenges. This study seeks to enhance the value of renewable energy by proposing a comprehensive methodology to evaluate and mitigate curtailment, with a particular focus on repurposing curtailed energy for ammonia production in Jordan, a pioneering nation in renewable energy adoption. The study unfolds several pivotal contributions to the field: (1) Incorporating curtailment in the capacity factor equation for the first time. (2) Exploring green ammonia potential in Jordan for the first time. (3) Investigating, for the first time, the effects of by-products (specifically oxygen and rare gases) on the investment in green hydrogen and ammonia, utilizing system dynamics (4) Outlining hydrogen applications and highlighting the breakthrough hydrogen energy release optimizer for combustion-free hydrogen extraction. In a scenario where curtailment is restrained (514.1 GWh/year), our findings unveil promising prospects: hydrogen and ammonia can be produced at levelized costs of 2.4 USD/kg and 600 USD/ton, respectively. More significantly, the integration of free energy (curtailed energy) and the synergistic utilization of oxygen and rare gases, notably argon, precipitate a remarkable cost reduction to 1.5 USD/kg and 401 USD/ton for hydrogen and ammonia production, respectively. This remarkable cost reduction underscores a substantial improvement, with the cost of ammonia being USD 219 lower than the average market price (USD 620). We advocate for the adoption of the hydrogen energy release optimizer and strategic utilization of by-products as pivotal strategies to significantly mitigate the costs associated with hydrogen and ammonia production, thereby amplifying the value and viability of renewable energy initiatives. • Mitigation methodology for renewable energy curtailment in green ammonia production. • Integration of curtailment into the capacity factor equation for accurate assessment. • Pioneering exploration of green ammonia potential in Jordan's renewable energy landscape. • System dynamics analysis of by-products (oxygen, rare gases) impact on investment. • Utilizing by-products and deploying HERO for cost-effective hydrogen and ammonia production. [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]

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]

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]

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]

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]

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]

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]

With the substantial increase in the amount of energy demanded and consumed today, there is also an increase in energy generation from renewable energy sources. Including renewable energy sources in an electrical grid and using them introduces the problem of planning the network in the most economical and efficient way. This thesis addresses the optimal power flow problem, which is one of the power system problems integrated with wind and solar power, both renewable energy sources. The optimal power flow problem is an optimization problem with a nonlinear structure and various constraints, where the best values of control parameters are determined. Additionally, combining the nature of solar and wind energy increases the complexity of the problem. Heuristic search algorithms, which are a type of artificial intelligence technique, are preferred in solving such problems. In this paper, the Triangulation Topology Aggregation Optimizer (TTAO) algorithm was first developed based on distance adequacy balance for the solution of the optimal power flow problem. The developed algorithm was applied to the optimal power flow problem including wind and solar energy sources and compared with the results of different algorithms in the literature. The obtained results clearly show that the proposed algorithm is effective in this power system problem. [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]

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]

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]

The Barefoot College (India) is an NGO working in the fields of education, skills development, health, drinking water, and solar power mainly to train older, rural women who are determined to challenge restrictive gender roles in their respective communities. Since its inception, the NGO has trained over 2,000 rural women as solar engineers across 93 countries worldwide and has brought electricity to over 18,000 homes. Barefoot trainers employ nonnormative methods of sharing knowledge such as color coding, sign language, and practical experience. This paper conducts a critical assessment of the Barefoot College Solar Electrification Programme to explore how it empowers illiterate and semi-literate women from remote rural areas around the world to become solar engineers (or "Solar Mamas"). It utilizes qualitative research methods to analyze this women's empowerment project as a landmark practical application of decolonial feminist theory. The paper contends that the Barefoot approach both challenges and conforms to the Women in Development and Gender and Development approaches of the past. The research is grounded methodologically in feminist praxis and also borrows from the conceptual frameworks of Feminist Political Ecology and Women and the Politics of Place. Stories and personal experiences from Solar Mamas have been highlighted to understand the real world impact of the program. The main findings indicate that the Barefoot College's innovative approach to empower marginalized communities and educate older women is achieved through decentralizing control and demystifying technology. [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]

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]

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]

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]

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]

Asia, the world's highest emitting region with surging electricity demand, must transition to clean energy sources for sustainable development. Despite significant progress in solar power deployment, its penetration in the electricity mix remains relatively low. This study employs process tracing to compare solar power development in China and Thailand from 2000 to 2023, representing state-driven and society-centered governance approaches. By analyzing data from secondary sources and conducting online stakeholder interviews, we assess the role of energy governance in fostering resilience during the low-carbon transition. We find state-driven governance exhibits resilience in addressing immediate crises, while society-centered governance drives societal transformation towards a low-carbon future. While state-centric governance suits large-scale centralized renewables, society-centered governance is crucial for distributed renewable projects. Path-dependent governance determines how the energy sector takes in niche innovation, and the reinforced governance structure constrains energy resilience given the interaction within and of the sector with other societal actors. The findings provide insights to inform evidence-based policies and interventions for renewable deployment.

Photovoltaic-powered drip irrigation is a vital approach to address the irrigation requirements in regions with limited water resources and energy deficiencies, thereby ensuring the provision of sustenance and horticultural produce for local inhabitants. However, the susceptibility of the drip irrigation system to clogging as well as the fluctuations in photovoltaic output can significantly impact irrigation quality. Moreover, conventional storage methods commonly employed in photovoltaic-powered drip irrigation systems, such as elevated water tanks and batteries, exhibit notable technological, economic, and environmental limitations. The present study introduces a novel photovoltaic drip irrigation technology (CAES-PVDI) that utilizes solar energy as the exclusive source of power, enabling stable and cost-effective high-quality drip irrigation. This technology actively regulates solar energy through compressed air energy storage, employing a cyclic pulse discharge method to ensure uniformity in irrigation outflow and significantly enhance the anti-clogging performance of the drip irrigation system. The proposed technology was implemented in a solar greenhouse for drip irrigation, and subsequent tests were conducted to assess its hydraulic performance and anti-clogging properties The results demonstrated that the system achieved a discharge uniformity of no less than 91.76 %. Furthermore, there was no blocked emitter in CAES-PVDI system, and the sedimentation inside the capillary tube decreased by 78.95 %-93.36 % compared to traditional drip irrigation system. In comparison to existing photovoltaic-powered drip irrigation technology, the CAES-PVDI system exhibited exceptional technical indicators and offered significant economic and environmental benefits, thereby presenting a novel approach to promote environmentally friendly and efficient operation of drip irrigation systems.

Summary: Transitioning to renewable energy is crucial for meeting net-zero targets to tackle the climate crisis. Although wind and solar are the main generation methods being pursued, they risk exacerbating future land-use challenges. Here, we explore the UK’s land capacity to accommodate two levels of increased future onshore wind and solar energy capacity. We quantify the impact on breeding bird habitat, food production, and net greenhouse gas flux in the presence and absence of a range of nature-based climate change mitigation measures. We find that even the highest onshore renewable energy ambitions require only a small fraction of the suitable land area, with little cost to food production, greenhouse gas sequestration, or breeding birds, even under pessimistic modeling assumptions. Therefore, the UK could boost onshore renewables without contributing substantially to future land competition, especially if potential synergies between multiple land uses, such as cropping and solar, are realized. Science for society: A transition to renewable energy can help mitigate global climate change. However, the widespread deployment of wind and solar technologies raises concerns about their impacts, from visual amenity to food production. Further, the renewable transition alone is insufficient to reach net-zero greenhouse gas emissions. We need active removal of greenhouse gases from the atmosphere as well as emissions reductions. Such removals can be enabled by nature-based solutions, such as habitat creation and restoration, which concurrently help biodiversity. In a UK case study, we show that ambitious renewable deployment targets require little land, in addition to what is needed for nature-based solutions, and can avoid the most productive farmland to minimize the impact on food production. Even in such a densely populated country with many competing land-use interests, integrated land-use decisions could help to simultaneously achieve net-zero targets, nature recovery, and maintenance of food production.

Technology for “wireless power transfer” (WPT) has advanced greatly in recent years, especially when used for renewable power sources. Environmental issues and the growing need for renewable energy sources have prompted a great deal of study in this area. This report provided an overview of the evolution of WPT technologies for renewable energy systems, including their advantages, disadvantages, and future possibilities. An introduction to WTP’s foundational ideas is provided, including techniques such as resonant pairing, capacitive connection, and microwave-based approaches. It delves into how these systems have been modified and improved to facilitate the long-distance transmission of renewable energy sources like wind and solar power. The study emphasizes the benefits of WPT in renewable energy systems, such as improved convenience, lower maintenance costs, and the opportunity to lessen negative effects on the environment. Problems with WPT technology in renewable energy applications are also analyzed critically. Power loss in transmission, misalignment, and electromagnetic interference are all examples of technical difficulties, and these are on top of the financial and legal obstacles. WPT’s effects on humans and animals are also explored since they must be taken into account before broad use of the technology.

Abstract 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 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.

The limitations of conventional non-renewable energy resources raise concerns about sustainability and energy supply security in the future. One of the worrying factors is the carbon dioxide emissions that can affect living organisms' health. Renewable energy sources, such as solar, wind, and hydropower, emerge as solutions capable of minimizing environmental impacts and reducing dependence on limited resources. Therefore, this research will simulate and design a small-scale renewable energy power generation system, particularly solar energy, in the Cilacap region, Central Java. The main components involved in this research include PV arrays, IGBT diodes, and universal bridges, supported by supporting elements such as displays, scopes, resistors, capacitors, inductors, and bus selectors. The technical data used as input are specific to the area. The simulation results show that the direct power generated by one modelled PV array is 221.7 W per hour. This research contributes to optimising small-scale solar power generation systems in the designated area, considering relevant components and parameters to enhance efficiency and sustainability.

Abstract 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.

Electric vehicle Charging stations are essential for the growing number of EVs, requiring efficient integration of renewable sources, battery storage, and grid connections to enhance sustainability. However, connecting these components to a DC bus can cause DC bus voltage instability due to fluctuating power sources and varying load demands. Starting with the design of a second-order disturbance observer (DOB) aimed at estimating and counteract matched and mismatched disturbances. An adaptive sliding mode control (SMC) is then introduced. This enables the creation of a DOB-based adaptive SMC scheme, which manages the overall disturbances of the system. To address the chattering issue, a barrier function based SMC (BFSMC) approach is proposed. The distinctive aspects of this BFSM control approach is its capability to stabilize the designed sliding variable to a predefined neighbourhood around the origin within a limited duration, eliminating the need for prior understanding of the maximum limit of the lumped disturbance. The stability of the proposed control system is determined using the Lyapunov candidate function. The robustness of the controller is validated by comparing it to traditional control strategies like PID, Lyapunov and sliding mode controllers. The proposed method demonstrates superior performance in regulating DC bus voltage.

The unprecedented growth of Renewable Energy Sources (RES) positions solar power as a leading contender in the global energy mix. Solar energy offers a sustainable alternative to fossil fuels, mitigating carbon emissions and promoting environmental sustainability. This study explores the crucial role of forecasting algorithms within photovoltaic (PV) systems. We aim to provide a comprehensive understanding of methodologies, datasets, and recent advancements for enhancing predictive accuracy in solar power generation forecasting. While machine learning has dominated previous research, recent studies highlight challenges in achieving optimal efficiency and accuracy. A significant obstacle lies in the deficiency of real-world application for large-scale specifically for solar power generation forecasting. To address this gap, this study defines prevalent forecasting methodologies and illuminates datasets with diverse characteristics and their relevance. This study meticulously provides and explore recent advanced methods and datasets, emphasizing their impact on forecasting performance. This study not only deepens our understanding of existing methodologies but also provides valuable insights for future advancements in solar power generation forecasting.

This paper develops a probabilistic earthquake risk assessment for the electric power transmission system in the City of Los Angeles. Via a dc load flow analysis of a suite of damage scenarios that reflect the seismic risk in Los Angeles, we develop a probabilistic representation for load shed during the restoration process. This suite of damage scenarios and their associated annual probabilities of occurrence are developed from 351 risk-adjusted earthquake scenarios using ground motion that collectively represent the seismic risk in Los Angeles at the census tract level. For each of these 351 earthquake scenarios, 12 damage scenarios are developed that form a probabilistic representation of the consequences of the earthquake scenario on the components of the transmission system. This analysis reveals that substation damage is the key driver of load shed. Damage to generators has a substantial but still secondary impact, and damage to transmission lines has significantly less impact. We identify the census tracts that are substantially more vulnerable to power transmission outages during the restoration process. Further, we explore the impact of forecasted increases in penetration of residential storage paired with rooftop solar. The deployment of storage paired with rooftop solar is represented at the census tract level and is assumed to be able to generate and store power for residential demand during the restoration process. The deployment of storage paired with rooftop solar reduces the load shed during the restoration process, but the distribution of this benefit is correlated with household income and whether the dwelling is owned or rented.

The successful integration of renewable energy resources into the power grid hinges on the development of energy storage technologies that are both cost-effective and reliable. These storage technologies, capable of storing energy for durations longer than 10 hours, play a crucial role in mitigating the variability inherent in wind and solar-dominant power systems. To shed light on this matter, a transparent, least-cost macro energy model with user-defined constraints has been utilized for a case study of California. The model addresses all included technologies, solving for both hourly dispatch and installed capacities. Real-world historical demand and hourly weather data have been utilized to do this analysis. A novel approach has been introduced to assess the significance of long-duration energy storage technologies (LDS) in terms of their energy and power capacity. This method explores the contributions of pumped hydropower storage (PHS), compressed air energy storage (CAES), and power-to-gas-to-power (PGP) storage toward minimizing the overall balance of system cost. Historical electricity demand, hourly weather data, and current technology costs are used to investigate high-level implications for California’s power system options. Increasing the storage capacity of each technology from 1 to 10 hours results in 29.6%, 14.4%, and 7.5% cost reduction for PHS, CAES, and PGP cases respectively. However, in studied simulations, maximum availability (maximum) of pumped hydropower storage reduces the balance of system costs by 72.3% followed by CAES (60.6%) and PGP (48.6%) and suggests that pumped hydropower storage in combination with CAES/PGP could play an important role in California’s electricity system, provided that suitable sites can be identified and constructed at reasonable costs.

The global expansion of solar-powered within distribution networks with Low Voltage (LV) is experiencing substantial expansion. Despite the various advantages offered by solar photovoltaic generation, surpassing the constraints on Hosting Capacity (HC) within these networks persist a significant technical problem in system operation, especially in relation to voltage operation. This research delves into the effectiveness of improving the Hosting Capacity (HC) of a photovoltaic (PV) system within an LV distribution system. It utilizes a Model Predictive Controller (MPC) to achieve this enhancement and contrasts its performance with reactive power control. The study examines scenarios encompassing both linear and non-linear loads to assess the impact of these control strategies on the PV system’s harmonic current in the LV distribution network. Through detailed analysis, the MPC controller demonstrates superior adaptability and responsiveness, maintaining stable active power at 95.5 kW before accommodating a 100% PV system penetration and experiencing a substantial increase to 192 kW. The hosting capacity, thereby, sees a notable 101.05% improvement under MPC control. Additionally, the study reveals that MPC optimizes reactive power utilization, resulting in a 17.9% reduction in reactive power and an 18.3% enhancement in bus voltage compared to reactive power control. Notably, MPC exhibits superior adaptability to both linear and non-linear loads, emphasizing its potential as an effective solution for optimizing the performance of PV systems within LV distribution grids. This research underscores the significance of advanced control strategies in facilitating the integration of renewable energy systems while ensuring grid stability and reliability.

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.