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Renewable energy sources like wind and solar, need help in both short-term and long-term forecasts due to substantial seasonal fluctuation. The objective of this study is to demonstrate the unpredictability of renewable energy sources like solar and wind to calculate the amount of hydrogen energy storage (HES) that would be required to meet grid stability requirements while dealing with this volatility. Finding out how much HES is required to stabilize an Australian system that depends exclusively on wind and solar power is a novelty. The usual power of electrolysis facilities is only 46 GW, which allows for significant capacity savings when using high-efficiency storage methods like flow batteries among wind and solar generators and electrolyzers. The highest possible capacity of the electrolysis facilities is 269 GW. There is 169 GW of power coming from the hydrogen generators that produce electricity. The results are in perfect harmony with the Royal Society's prior assessment of the energy storage needs of the UK by 2050 to meet the net-zero pledge in a 570 TWh yearly stable grid, where hydrogen is also the main storage material. • Unpredictability of renewable energy sources like solar and wind. • Hydrogen energy storage quantity that would be required to meet grid stability. • Highest possible capacity of the electrolysis facilities is 269 GW. • Energy storage for net-zero, hydrogen as the key material in a 570 TWh stable grid. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of foldable solar panels used in agrivoltaics systems (AVS), focusing on the dual benefits of optimized land use for agriculture and solar power generation. Employing simulation techniques, the study investigates the impact of inter-panel shadow effects on power generation in systems using multiple foldable solar panels. Key findings indicate that foldable panels achieve optimal performance during periods with shorter daylight hours, demonstrating high seasonal variability in power generation. The study shows that foldable panels, which can adjust their angle relative to the sun's position, are particularly effective at reducing the loss of solar irradiance due to shading, compared to fixed panels. Additionally, this study highlights the potential of foldable solar panel systems in AVS to adapt to varying solar conditions, thereby enhancing land use optimization for sustainable agricultural and power generation practices. [ABSTRACT FROM AUTHOR]

In this article, the development of multi‐functional grid connected solar photovoltaic (PV) system using improved Fractional order Control theory‐based Pole fixed Second Order Generalized Integrator (FO‐PFSOGI) is proposed. The fractional order (FO) control technique offers an advantage to adjust the fixed structure of the integer order and provide additional degree of freedom to achieve accurate response during the system operation. The FO‐PFSOGI control technique is used to extricate fundamental constituent of the non‐sinusoidal load current. The PV system is competent of feeding the local load requisite and may also inject surplus power into the grid. The voltage source converter (VSC) utilized in the grid‐connected system can also be operated as power quality compensator, taking care of harmonics, unbalancing and excess reactive power demand of the local load. The feasibility of multifunctional operations of the converter is established in this article, thus achieving maximum utilization of the power electronics used in the system and reducing the overall cost. A performance comparison of the developed control technique is presented with the conventional techniques in terms of harmonic compensation, weight convergence and computational complexity. The implementation of the controller is modest and extracts fundamental quantities without any phase delay under different loading conditions. The developed system is validated using both simulation and experimental study. A scaled down experimental setup of grid‐connected PV system is implemented in the laboratory and real time performance of the FO‐PFSOGI is demonstrated using variations in system load and PV irradiance. [ABSTRACT FROM AUTHOR]

As the usage of solar power generation increases, it has become essential to predict power generation accurately. Among the various factors that affect solar power generation, soiling on the panel surface drastically reduces solar power generation. Therefore, accurately identifying the area of soiling on the panel surface helps predict solar power generation. However, most existing studies classify the presence or absence of soiling on the panel or the type of soiling. Additionally, current datasets used for training these models, such as the Solar Panel Soiling Image (SPSI) dataset, suffer from limitations, including a lack of diversity in panel types and a small number of unique soiling shapes. To address these issues, we propose three novel data augmentation techniques—Naïve, Realistic, and Translucent—that generate diverse solar panel images with various soiling patterns. Using Pix2Pix and Copy-Paste methods, we created three corresponding datasets to address the imbalances in the existing SPSI dataset. We trained the DeepLabV3+ model for soiling localization using both the original SPSI dataset and our augmented datasets. Experimental evaluations on real-world solar panels installed at Chungbuk National University demonstrated that models trained on our proposed datasets significantly outperform those trained on SPSI data, with improvements in the Jaccard Index of 3.3%, 2.4%, and 14.6% for the Naïve, Realistic, and Translucent datasets, respectively. These results highlight the effectiveness of our data augmentation techniques for improving soiling localization in solar panels. [ABSTRACT FROM AUTHOR]

The employment of behind‐the‐meter solar photovoltaic (PV) systems has gained increasing popularity in recent years as more individuals and organizations aim to reduce their reliance on conventional grid‐connected power sources and take advantage of the environmental and economic benefits of solar power. However, precisely estimating the potential output of PV systems is a challenging task, since most of the PV systems used in residential properties have been installed behind the meter. Consequently, electric power companies are limited to accessing only the recorded net electricity consumption. This article introduces an innovative approach to estimate behind‐the‐meter PV power generation within a large region, utilizing a limited representative subset. The proposed framework integrates Missforest, that is, a robust tool for missing data imputation, with a hybrid application of K‐Means, Pearson Correlation Coefficient, and Principal Component Analysis, for the precise selection of representative PV sites. Additionally, it leverages the Informer model, a cutting‐edge deep learning‐based time series model, to link the relationship between the PV power generation at representative sites and the total PV power output on the entire region. To conduct a case study, the power output of 367 PV sites and solar radiation measured at 105 weather stations in Taiwan were collected and analyzed. The application of this comprehensive methodology demonstrates a notable advancement in the estimation of "invisible" PV power generation in comparison to other established techniques. [ABSTRACT FROM AUTHOR]

Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integration of RERs into power networks is considered as a strenuous imposition due to their uncertainties. The objective is to determine the placement of four wind and four PV units into large‐scale 118‐bus network to reduce expected power losses. The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte‐Carlo simulation and reduction‐based approaches are utilized to generate the novel set of optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such as fitness‐distance balance selection, mutation, and gorilla troops‐based approaches are utilized to improve overall strength of SOA. Effectiveness of ESOA is proved via statistical and non‐parametric analysis using benchmark functions, the results are further compared with other optimization techniques. The proposed ESOA is also used to resolve the deterministic and stochastic ORPD frameworks to reduce power losses and expected power losses. By incorporation of RERs into the stochastic ORPD framework can saved the expected power losses around 24.01%. [ABSTRACT FROM AUTHOR]

The concept of agrivoltaic, combining agriculture and solar photovoltaic system, is ideal for populous countries like India as it provides access to eco‐friendly power and crop production from the same land. The main objective of the study was to evaluate the performance of agrivoltaic systems for different geometry of solar photovoltaic strings and their suitability for agricultural practices for the greengram (Vigna radiata L. Wilczek) crop under North‐Gujarat agro‐climatic conditions of India. Eight equal‐capacity strings with different geometry were designed to evaluate power generation and crop production beneath the strings. The experiment involved eight strings taken as eight treatments and the traditional system of greengram (V. radiata L. Wilczek) cultivation practices is taken as the ninth treatment. The greengram (V. radiata L. Wilczek) was shown and all other cultural practices was kept same upto grain yields. The results revealed that 10.5 feet height string with continuous solar panel pattern) provided the highest gross income (Rs. 24,364.00) from power generation and greengram (V. radiata L. Wilczek) yield. In terms of net realization, a 6.0‐feet string with continuous solar panel pattern provided the highest net return of Rs. 12,417.00, as the capital cost was less for the system. Treatment‐5, which involved transparent panels, was found to be better for the photosynthesis process of the greengram (V. radiata L. Wilczek) crop, as it provided the highest yield (12.99 kg) under the agrivoltaic system. [ABSTRACT FROM AUTHOR]

European power systems are facing a fast capacity expansion of wind and solar power that outpaces the capacity expansion of transmission lines, thus requiring additional solutions to balance the variability. We studied the profitability of demand side management (DSM) systems operating in the European power markets in 2025. The results show that the DSM system would be the most profitable (10+ % internal rate of return with up to 600 EUR/kW investment cost) in Germany, Poland, Denmark, and Baltic countries. This is because Germany and Poland still have a notable share of fossil fuels mixed with growing share of VRE creating a constant price variability. Sensitivity analysis shows that the profitability is very sensitive to specific unit parameters; especially larger storage-to-power ratio could increase the annual income more than 100%. The most notable systemic uncertainty is the total capacity of DSM units, as the value of further DSM investments decreases rapidly. [ABSTRACT FROM AUTHOR]

This paper adopts a media archaeological perspective to excavate the social, technical, and ecological protocols embedded within videogames with the goal of imagining engaged users and alternative futures centred around photovoltaic technologies. We begin by dissecting the media lineages and infrastructures that are entwined with the development of mobile media. Then, we turn to the regime of planned obsolescence and address how eco-critical modding practices have flourished as a response. Using these theories as a provocation, we summarize the workshops and game jam we facilitated in the summer of 2022 as part of Concordia University's Solar Media Collective. Through these theoretical and practical explorations, we arrive at the clash between the optimism of solarpunk and the apathy of technological ambivalence. In our concluding discussion, we reflect on the realities of modding and DIY practices, closing with proposals for new (imaginary) research avenues. [ABSTRACT FROM AUTHOR]

In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors' study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors' work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors' study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors. [ABSTRACT FROM AUTHOR]

The Green Deal strategic plan for the development of renewable energy until 2030 is of particular importance in the context of the restoration of Ukraine's post-war energy infrastructure. One of the key topics is the analysis of the possibilities of installing large solar power plants in Ukraine. In this article, a multi-criteria analysis of the suitability of the territory of Ukraine is carried out on the basis of climatic, topographic and land use criteria. To assess land suitability, criteria standardized using fuzzy logic with weights determined by experts through the method of pairwise comparisons were combined using a weighted sum model. Upon completing the study, a suitability map was generated, depicting zones with varying levels of suitability (ranging from 0 to 1) for solar power plant placement. It was found that more than 35.68% of the country has average values of the suitability index (0.65–0.7), and approximately 18.82% show high indicators (<0.7). Conditions are especially favorable in the south of Ukraine. [ABSTRACT FROM AUTHOR]

The objective of this research study is to enhance the performance of a flat plate collector by using various cooling fluids, as an increase in solar panel temperature can decrease its efficiency. The experiment utilized three different fluids: distilled water, zinc sulfide nanofluid, and copper zinc sulfide nanofluid. FTIR analysis revealed a pronounced peak at 1133 cm−1, indicating the presence of Cu2+ ions in ZnS. Three key parameters were systematically examined to optimize the solar panel's energy gradient and temperature variance. The flow rate of the cooling fluid varied from 0.5 to 2.0 L min−1. Notably, the use of copper zinc sulfide nanofluid resulted in improvement in the energy gradient, reaching a peak value of 1112 W m–2. The temperature difference showed a significant increase, peaking at 4.73 °C when using CuZnS nanofluid at a flow rate of 1.5 L min−1. The incorporation of copper particles in the nanofluid notably enhanced the thermal conductivity of the cooling fluid. This improvement significantly boosted the efficacy of heat transfer processes, thereby increasing the overall efficiency of the solar panel system. [ABSTRACT FROM AUTHOR]

Here, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ε$\epsilon $‐normalised LMS (ε$\epsilon $‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519. [ABSTRACT FROM AUTHOR]

The escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future. [ABSTRACT FROM AUTHOR]

The high performance of the photovoltaic cell requires proper and efficient cooling because the electrical efficiency of the photovoltaic cell is affected by the operating temperature. Providing a suitable operating environment for the photovoltaic cell at a low temperature is necessary, which can be achieved using devices with highly effective thermal performance in which heat is transferred from the cell to the external surroundings via pulsating heat pipes, and this process leads to significant energy generation and the best efficiency of the photovoltaic unit. In this work, we use pulsating heat pipes with copper fins to achieve adequate cooling of the cell and make it operate at the best temperature, leading to a higher photovoltaic cell efficiency. The metal used for the pulsating heat pipes is copper, which has high conductivity needed to increase performance. The study showed that pulsating heat pipes are the most suitable options for cooling the photovoltaic cell, which leads to increasing its efficiency and increasing electricity production. [ABSTRACT FROM AUTHOR]

Abstract In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors’ study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors.

Abstract Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integration of RERs into power networks is considered as a strenuous imposition due to their uncertainties. The objective is to determine the placement of four wind and four PV units into large‐scale 118‐bus network to reduce expected power losses. The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte‐Carlo simulation and reduction‐based approaches are utilized to generate the novel set of optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such as fitness‐distance balance selection, mutation, and gorilla troops‐based approaches are utilized to improve overall strength of SOA. Effectiveness of ESOA is proved via statistical and non‐parametric analysis using benchmark functions, the results are further compared with other optimization techniques. The proposed ESOA is also used to resolve the deterministic and stochastic ORPD frameworks to reduce power losses and expected power losses. By incorporation of RERs into the stochastic ORPD framework can saved the expected power losses around 24.01%.

Abstract The employment of behind‐the‐meter solar photovoltaic (PV) systems has gained increasing popularity in recent years as more individuals and organizations aim to reduce their reliance on conventional grid‐connected power sources and take advantage of the environmental and economic benefits of solar power. However, precisely estimating the potential output of PV systems is a challenging task, since most of the PV systems used in residential properties have been installed behind the meter. Consequently, electric power companies are limited to accessing only the recorded net electricity consumption. This article introduces an innovative approach to estimate behind‐the‐meter PV power generation within a large region, utilizing a limited representative subset. The proposed framework integrates Missforest, that is, a robust tool for missing data imputation, with a hybrid application of K‐Means, Pearson Correlation Coefficient, and Principal Component Analysis, for the precise selection of representative PV sites. Additionally, it leverages the Informer model, a cutting‐edge deep learning‐based time series model, to link the relationship between the PV power generation at representative sites and the total PV power output on the entire region. To conduct a case study, the power output of 367 PV sites and solar radiation measured at 105 weather stations in Taiwan were collected and analyzed. The application of this comprehensive methodology demonstrates a notable advancement in the estimation of “invisible” PV power generation in comparison to other established techniques.

Abstract The concept of agrivoltaic, combining agriculture and solar photovoltaic system, is ideal for populous countries like India as it provides access to eco‐friendly power and crop production from the same land. The main objective of the study was to evaluate the performance of agrivoltaic systems for different geometry of solar photovoltaic strings and their suitability for agricultural practices for the greengram (Vigna radiata L. Wilczek) crop under North‐Gujarat agro‐climatic conditions of India. Eight equal‐capacity strings with different geometry were designed to evaluate power generation and crop production beneath the strings. The experiment involved eight strings taken as eight treatments and the traditional system of greengram (V. radiata L. Wilczek) cultivation practices is taken as the ninth treatment. The greengram (V. radiata L. Wilczek) was shown and all other cultural practices was kept same upto grain yields. The results revealed that 10.5 feet height string with continuous solar panel pattern) provided the highest gross income (Rs. 24,364.00) from power generation and greengram (V. radiata L. Wilczek) yield. In terms of net realization, a 6.0‐feet string with continuous solar panel pattern provided the highest net return of Rs. 12,417.00, as the capital cost was less for the system. Treatment‐5, which involved transparent panels, was found to be better for the photosynthesis process of the greengram (V. radiata L. Wilczek) crop, as it provided the highest yield (12.99 kg) under the agrivoltaic system.

Because of the complimentary nature of solar and wind electricity, hybrid solar wind power systems are the most effective renewable energy sources. Weather conditions have a significant impact on the amount of power that can be generated by wind and solar photovoltaic systems. Their intermittent nature causes output fluctuations. The purpose of this research is to offer a technique for a hybrid wind solar power plant that ma kes the most efficient contribution of renewable energy resources and is backed by technology that allows batteries to store energy. The fact that solar and wind power display power profiles that are complimentary was the driving force for the construction of the hybrid solar wind power system.

Abstract Here, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ε‐normalised LMS (ε‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519.

Abstract The escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Romania's automotive sector is experiencing an evolution towards sustainable transport, with an increasing interest in incorporating solar power technology into vehicles. This article examines the present state of solar power use in Romania's automobile industry, including difficulties, possibilities, and prospects. The analysis looks at technology improvements, legislative applications, consumer preferences, and the carbon footprint of solar-powered cars in Romania.

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

Abstract 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.

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

Abstract 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.

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

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

The environmental hazard posed by global warming necessitates the development of sustainable, eco-friendly power production unit based on renewable energy principles. Solar Chimney Power Plants (SCPP) are the resource that fits this description. Here, the chimney is equipped with a larger roof at bottom, referred as collector, absorbs the sunlight to warm the air inside. This heat creates an upward draft, resulting a forward motion of air, which rotates the turbine. There is a better possibility of enhancing the performance of an SCPP with modification of factors such as chimney height, collector area, collector angular position. Hence, this research objective is to study the alteration in efficiency of an SCPP with collector angular modifications, such as completely slopped, intermediately sloped profiles, as well as the effects of various chimney designs with area ratios larger than one. An additional study of a semi divergent (SD) chimney with a completely slanted collector, positioned vertically. Initial analysis is performed using ANSYS-FLUENT, and a simulation environment is modeled to mimic the various chimney and collector configurations in preparation for the experimental work. The better model is chosen from these simulations and experimented in true environmental conditions. It was determined that the average increase in temperature within the SCPP was 17 K. The research found that the collector setup with a slope of 50% (case-2) resulted in a peak velocity 12% higher than that of the fully sloped configuration (case-1). Additionally, case-2 was 23% more productive than the Manzanares facility. On the other hand, case-3's semi divergent chimney with a complete slopped collector outperformed the other two by 23% and 12%, respectively. [ABSTRACT FROM AUTHOR]

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

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

In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation's unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable. [ABSTRACT FROM AUTHOR]

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

Renewable energy is an energy resource that can be used continuously. At present, international oil prices continue to rise, the problem of global climate change is becoming increasingly prominent, and renewable energy and clean energy have ushered in a new round of development opportunities. Based on the new gray prediction model, this paper forecasts the consumption of renewable energy and further analyzes the sustainable development of renewable energy. In this paper, the combinatorial optimization method of cumulative order, background value coefficient, and initial conditions, parameter optimization combination, parameter combinatorial optimization process of the gray prediction model, and parameter optimization mechanism based on the PSO algorithm are proposed, and the reduction error analysis is carried out. The consumption of wind power and photovoltaic renewable energy is forecasted, and three different forecasting methods as exponential smoothing method, time series analysis method, and new gray forecasting method are compared, and the wind speed, irradiation intensity, and load are forecasted by these three different forecasting methods. Compared with the time series analysis method and the exponential smoothing method, the RMSE of the new grey prediction method is reduced by 127.12% and 160.59%, and the error rate is reduced by 3.16% and 4%, respectively. Based on the consumption forecast of renewable energy, this paper analyzes its sustainability from three directions economy, resource supply, and environment, and finally gives energy policy recommendations. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions The aim of this study was to describe the evolution of a regional neonatal service in Sierra Leone and changes in mortality and service use as it transitioned from a non‐specialist service to a dedicated special care baby unit (SCBU).This was a retrospective observational study. Anonymised data were taken from the ward admissions books at Bo Government Hospital, and trends in admissions and mortality within the neonatal service were examined for each stage of the department's evolution.Four phases of the service's development were identified between November 2015 and October 2019. Records of 2377 admissions and 333 deaths were identified. The average number of admissions per month and deaths per month varied by service development phase. There was a trend towards reduced death rates and increased numbers of admissions as the unit evolved into a dedicated neonatal unit with a reliable electricity supply.The development of an adequately sized SCBU with a reliable electricity supply and specially trained staff was associated with a reduction in the death rate and an increase in admissions. [ABSTRACT FROM AUTHOR]

Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro. [ABSTRACT FROM AUTHOR]

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

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]