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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]

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 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.

Abstract 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 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.

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.