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This paper considers two pertinent research inquiries: 'Can an AI-based predictive framework be utilised for the optimisation of solar energy management?' and 'What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?' The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and $ R^2 $ R 2 . A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency. [ABSTRACT FROM AUTHOR]

This work reports a novel TiO2/g-C3N4 photoanode-based photocatalytic fuel cell (PFC) designed to convert chemical energy from simulated wastewater. The g-C3N4 modified TiO2 nanorod was successfully synthesized by a facile hydrothermal method. The results indicated that the maximum photocurrent density reached 2.44 mA cm-2 at 1.23 V vs. RHE by 1.167 g L-1 g-C3N4 loaded TiO2 composite. On the basis of analysis, the photoelectrochemical mechanism of the composite photoanode was proposed. This mainly demonstrated that the composite photoanode increases the electron donor density and boosts charge separation efficiency. In addition, the power density and hydrogen production of the proposed PFC were enhanced by 5.37 and 1.49 times compared to TiO2 photoanode-based PFC. To find the origins of the excellent performance of PFC, the influence of the organic compounds were investigated. The ESR measurement results indicated that the organic matter was captured by the photoexcited holes directly to facilitate the charge separation. The achieved power density and hydrogen production of 0.14 mW cm-2 and 21.60 μmol h-1 cm-2 were measured using RhB as the model pollutant, which was 2.42 and 1.23 times higher than the experiments with PBS electrolyte. This study proposed a novel PFC system converts the organic pollutant to the hydrogen and the electricity. [ABSTRACT FROM AUTHOR]

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production ( P p v ). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the P p v estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence P p v , the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in P p v between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m2. The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences. [ABSTRACT FROM AUTHOR]

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio. [ABSTRACT FROM AUTHOR]

For the application of renewable energy at the airport, the use of solar power requires certainty of the electricity produced. The certainty of electricity generated from solar power can be predicted using machine learning methods. Predictions made on PV electrical power output are based on historical data from direct measurements of solar PV parameters, including solar radiation and PV panel temperature. Various types of machine learning methods for predicting PV output power have been used in previous studies with different evaluation values of prediction results. In this study, the author conducted a hybrid K-NN method with LSTM to predict the PV electrical power of solar PV output with solar radiation parameters and PV panel temperature. After making predictions using this method, excellent RSME results were obtained with a value of 0.015424830635781967. The results of the PV output power value graph in this prediction are also very good, where the predicted value is close to the value of the testing data or actual data. [ABSTRACT FROM AUTHOR]

Using solar photovoltaic power sources has become a discussed topic in the construction and energy industry. The pressing need to reduce reliance on fossil fuels, increasing costs of traditional electricity generation, and affording photovoltaic modules has sparked a growing interest in solar photovoltaics. This study aims to optimis e the layout of solar photovoltaic systems to minimise environmental impact and building load, comparing the performing south-oriented panels with east-west-oriented panels. The comprehensive analysis performed hinges on a diverse array of determinative factors that demand thoughtful consideration before embarking upon implementing any photovoltaic installation. These salient factors, including but not limited to structural integrity, incorporating ballast for stability enhancement, integrating requisite electrical components, selecting solar panels, quantifying energy production capabilities, assessing carbon emissions, and discerning associated benefits, require thoughtful consideration before implementing any photovoltaic installation. The findings derived from this study underscore that, within the context of the given geographical location, the solar photovoltaic system configured with an East-West orientation represents the optimal choice to reduce both emissions and structural load. This study provides a scientific basis for the construction industry and the energy field and guides the future development of photovoltaic installation projects in a more economic and environmentally friendly direction. [ABSTRACT FROM AUTHOR]

Solar energy reaching a horizontal surface can possess fluctuations that impact electricity generation at a solar plant. Despite this, energy access remains inadequate, particularly in rural areas, with an estimated 82% deficiency. This drives us to assess the regressive and spatial-temporal accessibility of solar energy in the southern and mid regions of Mozambique. This evaluation aims to determine the actual availability of energy for electrification purposes. Data on global horizontal irradiation from approximately 8 stations across all provinces in the specified regions, collected between 2012 and 2014 at intervals of 1 and 10 min, were analyzed using regression and correlation methods along with a specialized algorithm for classifying days based on clear sky index terms. The statistical analysis identified days with significant potential for energy accessibility, exceeding 50% of the average. The findings suggest a correlation coefficient of approximately 0.30 for energy and non-linear regression with clear sky index coefficients around 0.80. The method employed demonstrated accuracy when compared to theoretical simulations of the clear sky index in the region, indicating its potential applicability in other regions of interest. [ABSTRACT FROM AUTHOR]

Forecasting future wind electricity generation requires diverse methods and tools to estimate potential output at specific locations. This study utilizes historical meteorological data and simple models at particular sites, along with past electricity production records from selected wind farms. The interannual and decadal oscillations of wind energy production at the grid level are computed. The large‐scale, long‐term energy storage needed to achieve dispatchable electricity, addressing generation variability is assessed. For the continental United States, the estimated storage requirement is approximately 1300 GWh per GW of installed capacity. The inclusion of solar power generation and the round‐trip efficiency of energy storage positively or negatively impact this estimation. [ABSTRACT FROM AUTHOR]

The modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind-based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30-year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects. [ABSTRACT FROM AUTHOR]

The shift towards renewable energies is driven by the shortage of fossil fuels for electricity generation and the associated harmful impacts. Grid-connected PV systems are a reliable and effective choice for power production across different uses, making them a key player in the global renewable energy landscape. Consequently, the careful selection of components for these systems is a crucial and widely studied aspect in this area of research. This paper introduced using gray wolf optimization algorithm GWO & whale optimization algorithm WOA for determining the optimal number of grid - connected bifacial photovoltaic PV systems in Babylon Hilla. The considered factors included available space, desired energy production, radiation, dihedral factor, budget constraints, and grid connectivity requirements. The mathematical formulation of the problem and implementation details of the algorithms are presented. In addition, two cases studied are performed one for a residential area, and the other for a single house. The results demonstrated the efficiency and effectiveness of both algorithms in identifying optimal solutions for determining the size of systems in the area under study. However, the WOA surpassed the GWO in meeting the optimization criteria. The proper selection of these systems resulted in higher power generation, lower costs, improved energy management, and the advancement of sustainable solar energy solutions. [ABSTRACT FROM AUTHOR]

This article addresses the contemporary challenges associated with the generation of electricity from solar panels, considering the diverse environmental conditions affecting efficiency. In response, various maximum power point tracking (MPPT) techniques are explored to optimize power generation. The study focuses on three MPPT techniques—perturb and observe, incremental conductance, and the beta method—in the context of solar water pump systems. Utilizing MATLAB software for analysis, this research compares the performance of these MPPT techniques to identify the most suitable approach for enhancing power generation in solar water pump applications. The findings highlight incremental conductance and the beta method as highly effective for operating solar water pump systems, providing valuable insights for enhancing their efficiency in real-world scenarios. This emphasizes the significance of utilizing optimized control strategies to improve the performance and reliability of solar water pumping technologies, thereby advancing sustainable water management practices across agricultural and rural environments. [ABSTRACT FROM AUTHOR]

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

Electricity demand increasing day by day with the passage of time, fossil fuels and traditional electricity sources are becoming obsolete. The possibility of dealing with a power deficit and importing oil and gas for electricity generation exists for developing nations like Pakistan. One of the most accessible and affordable sources of renewable energy in the world is solar power generating. Photovoltaic (PV) cell performance is influenced by the environment and the technology used to capture the available energy. The Islamabad region of Pakistan is blessed with an average of 300 days of sunshine a year due to its location in the solar band at 33.7215°N latitude and 73.0433°E longitude. The current study compares and contrasts how Pakistan and South Asian Association for Regional Cooperation (SAARC) countries generate electricity using solar systems. Choosing one geographical place from all SAARC nations, PVSyst software is used to develop and estimate the performance study for solar model cell Panasonic 320Wp, 48V and 1.5 kW Inverter Fronius for a 9.6 kWp load. Energy supplied into the grid is studied together with various losses that occur in the system, with the choice of PV arrays and other factors remaining the same for comparison study of all SAARC countries. The approach to minimize losses through the utilization of an adjustable tilt angle has been implemented. This study will be useful in estimating and planning the solar energy output in all SAARC nations for the same photovoltaic system, and it will be simple to trade all PV system components in the region. [ABSTRACT FROM AUTHOR]

The world depends heavily on electrical energy for accessing technologies. For the generation of electricity, technology can utilize renewable energy sources like solar energy and wind energy. Solar photovoltaic (PV) systems occupy space among consumers due to their feasibility, flexibility, cost, and simple implementation procedures. The solar PV system experiences many factors causing power loss like partial shading, hotspots, and diode failure. In this work, a new static PV array configuration, named Renzoku puzzle patternbased array configuration, is proposed. This proposed configuration technique was designed to overcome the drawbacks of the previously proposed array configurations in terms of power generation, fewer mismatch losses, a high shade-dispersion rate, and consistent performance under any level of partial shading. The proposed array configuration has been validated using both simulation and hardware. The simulation is carried out in a 9 x 9 PV array in MATLAB/Simulink®. The performance analysis, results, and corresponding characteristic curves are presented in this manuscript. [ABSTRACT FROM AUTHOR]

In recent years, Electric Vehicles (EVs) are contributing a major share in Thailand and benefit the environment. Most of the EV charging stations are sourced from solar energy as it becomes a carbon-free source of energy production. Secondly, Thailand is rich in solar irradiance, and higher irradiance leads to higher power production. On the other hand, in tropical conditions, solar Photovoltaic (PV) module temperature increases following the solar irradiance due to high ambient temperature, resulting negative impact on the efficiency and lifespan of photovoltaic (PV) modules. Further, to increase PV power production, in this study, different rates of cooling strategies are proposed. The study found that reducing the temperature by 5% to 25% resulted in increased average power outputs of 5947.94W, 6021.43W, 6094.92W, 6168.41W, and 6241W, respectively. Notably, 25% of the cooling rate achieved higher production. However, it is lower than the nominal power production. Following that, economic analysis and environmental impacts are analyzed for Thailand's EV charging station using a different cooling rate of PV module. Overall, it is concluded that, depending on the economic viability of the EV charging station, cooling technology can be applied, and it will benefit the EV charging station both economically and environmentally. To further enhance the solar PV power production approach for EV charging stations in Thailand, it is imperative to prioritize future endeavors towards optimizing cooling technology, integrating energy storage, and implementing supportive policies. [ABSTRACT FROM AUTHOR]

The paper presents a feasibility study for hybrid system operation of a hydroelectric power plant and a floating photovoltaic plant. Using the database of government agencies, it established the daily production of electrical energy and the corresponding water flow of the Ilha Solteira hydroelectric power plant in São Paulo, Brazil. The PVsyst software simulated the potential of 480 MWp of a floating photovoltaic plant in the hydroelectric power plant lake. The MATLAB software performed the mathematical modeling, analyzing 12 scenarios of weather conditions for hybrid electricity generation at the hydroelectric power plant and the floating photovoltaic plant. The data obtained in the analyzed scenarios show an average monthly reduction of 6% in hydroelectric power generation and 7% in the volume of water in the turbine, allowing the generation of electric energy from a floating photovoltaic plant, improving the reserves of waterenergy stock, reducing the production of greenhouse gases, and avoiding the emission of 55,000 tCO2/yr. The financial evaluation shows a cost of US$ 0.73/Wp, and 13 years for the floating photovoltaic plant system to start producing a profit. Yet the Floating Photovoltaic is advantageous because it shares the power transmission system of the hydroelectric plant, and it is not necessary to acquire large land areas. [ABSTRACT FROM AUTHOR]

As electricity systems become increasingly dominated by variable inverter-based generation (such as wind and solar photovoltaics (PV)), additional sources of variability appear, while the share of dispatchable thermal power plants decreases. Maintaining a stable grid frequency requires new sources of inertia to slow the frequency changes, as well as new sources of reserve power to counter the imbalances. This work uses a linear optimization modeling approach to analyze the reserve market of the electricity system during the transition to a carbon-free system with high shares of variable electricity generation. The modeling is performed for three regional contexts and with varying degrees of available flexibility technologies. As for the reserve supply, batteries, electrolyzers, electric boilers and heat pumps for district heating, curtailed wind and solar power, and hydropower and thermal power plants are all included. The results indicate that of these suppliers of reserves, the introduction of grid-scale batteries into the system drastically reduces the reserve market size. Only early in the transition is revenue from the reserve market greater than 5% of the total revenue for any technology. While the demand for reserves increases as the share of solar PV and wind power increases, the modeling reveals that access to flexible loads, storage units, and emulated inertia from wind power also increases. Depending on the choice of flexibility measures available, demand-side participation could play a major role in minimizing the cost of grid stability in the future. [ABSTRACT FROM AUTHOR]

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model. [ABSTRACT FROM AUTHOR]

This study investigates the potential for renewable energy-based electricity generation using existing wave, wind, and solar energies in Türkiye. A significant part of Türkiye's energy needs is still met using fossil fuels. Considering the country's resources, renewable energy sources appear as an alternative source to meet these needs. The objective of this study is to find an effective, efficient, economical, environmentally friendly, and sustainable way to produce electricity to reach net-zero targets and transition towards low-carbon and carbon-free energy systems. To be able to make a deep investigation about the relevant issue, six provinces from different regions of Türkiye (Antalya, Çanakkale, İstanbul, İzmir, Kırklareli, and Muğla) are assessed in terms of wave, wind, and solar energy potential, including wave data, wind speeds, sunshine duration, and global radiation values. Wind, wave, and solar energy data of the selected regions were taken from the ERA5 database, which is the weather forecast model of the European Center for Medium-Term Weather Forecasts (ECMWF), and the Ministry of Energy and Natural Resources of the Republic of Türkiye and the General Directorate of Meteorology. Calculations were made using monthly data for the last 5 years. Considering the coastal lengths in the determined regions, the annual total electrical power produced from wave, solar, and wind energies was calculated. In these calculations, the coastal length parameter was assumed to be uniform across all cities, and the electrical power potential from these energy sources was analyzed. Within the framework of these analyses, the number of houses in the selected regions whose electricity needs can be met was calculated. As a result, the potential electrical power and the amount of affordable housing units in the selected regions were compared. As an important result of the studies, it was determined that the characteristic features of the selected regions, such as wavelength, wave height, and wind speed, were directly related to the applicable coast length. The power obtained from wave energy was higher than that from other renewable energy sources, considering the determined coast lengths. Wave energy was followed by parabolic solar collector, wind, and photovoltaic solar energy systems. According to the model, the power obtained from renewable energy systems was at the highest level in the Kırklareli/Demirköy province compared to other locations. Kırklareli was followed by İstanbul, Antalya, İzmir, Muğla, and Çanakkale. It was also found that the electricity needs of 763,578 houses were met in the Kırklareli/Demirköy region, and the electricity needs of 470,590 houses were met in the Çanakkale/Ayvacık region. The statistically optimized factors using the Response Surface Methodology (RSM) for wind, photovoltaic, parabolic solar collector, and wave power were reported as 995.278, 4529.743, 2264.546, and 276,495.09, respectively. The optimal factors aim to achieve a total electricity generation rate of 2.491 × 109 (kWh/year), a total number of houses of 682,590.55 (number/year), and a total cost of USD 813,940,876. In line with the results obtained, the Kırklareli/Demirköy region becomes favorable when considering wave and wave-integrated wind and solar energies. The proposed system has the potential to meet the entire electricity demand of the Kırklareli province based on data from the Republic of Türkiye Energy Market Regulatory Authority (EMRA). [ABSTRACT FROM AUTHOR]

To achieve sustainable development, the energy transition from lignite burning to renewable energy resources for electric power generation is essential for Greece. Wind and solar energy have emerged as significant sources in this transition. Surprisingly, numerous studies have examined the potential for onshore wind based on land eligibility, while few studies on open-field photovoltaic (PV) installations have been conducted. Therefore, based on the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD-RES), along with insights from previous relevant studies, this work conducts a land eligibility analysis of onshore wind and open-field PV installations in Greece using the software Geospatial Land Availability for Energy Systems (GLAES 1.2.1) and ArcGIS 10.2. Additionally, through an in-depth exploration of wind and solar PV energy potential in decommissioned lignite mines integrated with wind power density (WPD) and global horizontal irradiation (GHI) maps, this study compares the suitability of wind versus solar as energy sources for the decarbonization of Greece. Overall, despite the greater spatial eligibility for onshore wind turbines compared to open-field PV power plants, the relatively lower wind energy potential and operational limitations of wind turbines lead to the study's conclusion that solar energy (PV) is more suitable for the decarbonization of Greece. [ABSTRACT FROM AUTHOR]

Combined concentrated solar power with photovoltaics can provide electricity and heat at the same system while maximizing the power output with reduced losses. Spectral splitting is required in such systems to separate the infrared part of the solar spectrum towards the thermal system, while the visible and near-infrared radiation can be converted by the photovoltaic solar cell. The performance of concentrated solar power plants comprising reflective beam splitters for combined generation of electricity and heat is presented in this work. A 50 MW power plant is considered in this work as a case of study in Southern Crete, Greece. The solar power plant consists of parabolic trough collectors and utilizes beam splitters with varying reflectivity. The dynamic performance of the power plant is modeled, and the annual energy yield can be calculated. Up to 350 MWt of thermal power can be delivered to the photovoltaic system utilizing a 50% reflecting splitter. The penalty to the high-reflectivity system is limited to 16.9% and the annual energy yield is calculated as 53.32 GWh. During summer months, a higher energy yield by up to 84.8 MWh/month is produced at 80% reflectivity compared to 90% as a result of the number of parabolic troughs. The reported energy yields with reflectivity by dynamic modeling can highlight discrete points for improvement of the performance in concentrated solar power photovoltaics. [ABSTRACT FROM AUTHOR]

The generation of electricity from renewable sources has made considerable strides in today's industrialized nations. The primary objective of this research is to examine the grid integration of solar systems within permissible total harmonic distortion (THD) limits, with the help of a proposed cascaded feedforward neural network (CFNN) controller fed advanced power conversion devices such as symmetrical hybrid (SH), switched inductor (SL), and switched capacitor (SC) based boost converter and reduced switch cascade multilevel inverter (CMLI). The SH–SL–SC converter for power conversion features a high voltage gain and reduced switch CMLI features minimum number of power electronic switches and low harmonic content in both the voltage and current profiles. When producing a control signal for the recommended SH–SL–SC boost converter, the proposed CFNN-MPPT controller performs better under different weather scenarios where it needs significant data to operate effectively. Consequently, the CFNN-MPPT controller fed SH–SC–SC boost converter-based PV system is integrated into the grid through reduced switch CMLI where it is controlled by proposed CFNN controller. The CMLI can be operated on asynchronous input voltage sources and making it ideal for PV systems when integrated to the grid with improved power quality by reducing total harmonic distortion (THD) to less than 5% at point of common coupling. With reference to the standards outlined in IEEE 1547 and 519, the simulation's outcomes were reviewed. Finally, a symmetric hybrid SL–SC boost converter and modified cascaded nine level inverter have been conceived and produced as a laboratory prototype model and its output waveforms are presented in this article. The proposed intelligent controller fed converter exhibits enhancements in the converter voltage gain and power quality by lowering the THD level at PCC in the grid integration with the PV power systems. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) farming is increasingly being used to power electric vehicles (EVs). Although many studies have developed dynamic EV charging prediction and scheduling models, few of them have coupled rooftop PV electricity generation with the spatiotemporal EV charging demands at an urban scale. Thus, this study develops a research framework containing three interconnected modules to investigate the feasibility of EV charging powered by rooftop PVs. The framework is constructed by the statistics of time serial EV charging demands at each station, the planning of rooftop PV installations associated with all charging stations, and the development of a dynamic dispatching algorithm to transmit surplus electricity from one station to another. The algorithm can maximize the overall balance between supply and demand, maximize the total PV electricity generation while minimising the total PV area, minimize the number of PV charging stations used as the suppliers for dynamic dispatch, and minimize the total electricity transmission distance between stations given the same power supply. The experiment utilizes a complete EV charging dataset containing 5574 charging piles with more than 9.7 million records in June and July in Guangzhou, China. The results show that rooftop PVs can supply more than 90% of the charging demand. The results encourage and inspire us to generalize and promote such a solution in other cities. Future work can refine the algorithm by adapting different PV sizes into various charging stations to further improve the electricity generation capability and the dynamic dispatching efficiency. [ABSTRACT FROM AUTHOR]

The usefulness and productivity of photovoltaic (PV) panels are significantly impacted by ambient and operating temperatures. However, the negative influence of hot climates on PV panel performance can be mitigated through innovative cooling techniques. This research work aims to investigate the implementation of phase change material (PCM) on the backside of solar modules to reduce panel temperature and enhance energy production. A hybrid system utilizing soy wax for cooling is applied to the rear of the panel. Comparative data have been collected on various days, and the outcomes have been analyzed. The outcomes reveal that the usage of phase change material reduced panel temperature by up to 18°C, causing a 10.89% rise in electricity generation compared to panels without cooling systems. [ABSTRACT FROM AUTHOR]

Energy gains from natural sources have become widespread around the world. Solar and biogas energy systems, which we hear frequently today, also attract the attention of investors. Obtaining large gains with appropriate investments in the appropriate region has made both energy more environmentally friendly and more profitable for the investor. In this study, the energy and environmental potentials for the installation of solar or biogas energy systems on a dairy farm in Karacabey district of Bursa were examined and a comparison was made between the two energy systems. Using the RETScreen program for the solar energy system, it was determined that the dairy farm would generate a total of 1.026.597 kWh/year of electricity and the system could reduce greenhouse gas emissions by 345 tCO2/year. For the biogas system, it was determined that the total electricity generation potential to be produced from the farm at the end of 2023 is 1.012.158 kWh/year and a total of 692,316 tons/year of CO2 emissions can be prevented. It was determined that the annual electricity consumption of the farm was 885.855 kWh/year. It is concluded that both energy systems will meet the electricity energy amount of the farm, but the biogas plant will prevent greenhouse gas emissions more. However, it was decided to install solar panels on the dairy cattle farm due to the high initial investment and operating costs of the biogas plant and the decrease in the amount of waste due to the decrease in the amount of animals over the years and this situation negatively affects the efficiency of the plant. [ABSTRACT FROM AUTHOR]

Copyright of Revista Técnica Energía is the property of Centro Nacional de Control de Energia CENACE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

Solar energy has received great interest in recent years, for electric power generation. Furthermore, photovoltaic (PV) systems have been widely spread over the world because of the technological advances in this field. However, these PV systems need accurate monitoring and periodic follow-up in order to achieve and optimize their performance. The PV systems are influenced by various types of faults, ranging from temporary to permanent failures. A PV system failure poses a significant challenge in determining the type and location of faults to quickly and cost-effectively maintain the required performance of the system without disturbing its normal operation. Therefore, a suitable fault detection system should be enabled to minimize the damage caused by the faulty PV module and protect the PV system from various losses. In this work, different classifications of PV faults and fault detection techniques are presented. Specifically, thermography methods and their benefits in classifying and localizing different types of faults are addressed. In addition, an overview of recent techniques using different artificial intelligence tools with thermography methods is also presented. [ABSTRACT FROM AUTHOR]

The integration of solar‐driven interfacial evaporation and electricity co‐generation is considered a promising approach to simultaneously alleviate freshwater scarcity and the energy crisis. However, affected by intermittent solar irradiation/uncontrollable weather, the overall performance of solar‐driven evaporation in the real world is greatly reduced. Herein, inspired by antifreeze proteins in beetles that survive in extreme climates, all‐weather solar‐driven interfacial evaporators with a sandwich structure are designed. The top and bottom layers composed of MnO2‐modified cotton cloth are used for photothermal conversion and water transport, meanwhile, the middle layer made of a phase change microcapsule/hydrogel composite serves for heat storage and release. Under 1 kW m−2 irradiation, the evaporator exhibits a high evaporation rate of 2.67 kg m−2 h−1 and an efficiency of 89.5%. In the dark, the heat released from the phase change layer supports an evaporation rate of 0.43 kg m−2 h−1, 3.6 times that of pure water. Additionally, assembled with a thermoelectric module, the hybrid device achieves a stable output electricity power of 0.42 W m−2 under 1‐sun illumination and a prolonged output for 30 min in the dark. This work provides a novel approach for full‐time solar‐powered steam‐electricity co‐generation and a proof of concept for biomimetic steam generation/heat management integration. [ABSTRACT FROM AUTHOR]

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

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Unlike conventional opaque solar cells, semi‐transparent solar cells enable simultaneous electricity generation and light transmission. Along with solar energy harvesting, the offered multiple functionalities of these technologies, such as aesthetic appearance, visual comfort and thermal management, open diverse integration opportunities into versatile technological applications. In this work, the first demonstration of a novel semi‐transparent solar cell based on ultrathin hydrogenated amorphous Si/Ge multiple quantum wells (MQW) is reported. Through optoelectronic modelling, the advantages of ultrathin MQW as photoactive material to overcome the intrinsic limitations of thin (20 nm) and ultrathin (2.5 nm) single quantum well (SQW) counterparts are explained. This allows extra degree of freedom for both optical design and bandgap engineering. Mainly, the multiplication of the QWs number in a periodic configuration, taking advantage of effective synergy between electronic and photonic confinements, leads to an improvement of photocurrent, while preserving high voltage and fill factor and ensuring significant transparency. The MQW new concept yields a boost in power conversion efficiency up to 3.4% and a considerable average visible transmission of about 33%. A light utilization efficiency above 1.1% is achieved, which can be considered as one of the highest among inorganic semi‐transparent solar cell technologies. The successful demonstration of ultrathin semi‐transparent Si/Ge MQW solar cells indicates the promising integration potential of this emerging photovoltaic technology for supplying systems in relevant applications such as in buildings, vehicles and greenhouses. [ABSTRACT FROM AUTHOR]

Background: The transition towards renewable energy sources has become an imperative step to mitigate climate change, reduce carbon emissions and improve energy security and economic prosperity in a sustainable manner. Maximizing the cost effectiveness of electric power generation is crucial to making renewable energy sources viable and attractive options for clean energy production. The strategic allocation of wind, hydro and solar power systems is essential to achieving this goal. This paper attempts to demonstrate how the cost effectiveness of electrical power system could be maximized through the integration of wind, solar and hydropower systems and comparison at different penetration levels of 0, 25, 50, 75 and 100% on cost effectiveness of electric power generation. The different generator technologies were designed based on their electrical output attributions. Results: The cost of electric generation for the integration of each generator at the various buses were calculated at different penetration level for fair comparison. The results indicate that the minimum money loss for the integration of solar power was $743.90 at bus 4 and at 50% penetration level, the minimum money loss for the integration of wind power was $999.00 at bus 4 and at 25% penetration level while the minimum amount loss for the integration of hydropower was $546.50 at bus 4 and at 75% penetration level. Conclusions: The magnitude to which the integration of the different generator affects the cost effectiveness of power production hinges on the type of generator, the penetration level and the location of the generator in the grid. [ABSTRACT FROM AUTHOR]

The Dutch climate agreement anticipates the large-scale implementation of solar and wind energy systems on land and water. Combining solar and wind farms has the benefit of multiple surface area use, and it also has the advantage of energy generation from both solar and wind energy systems, which is rather complementary in time; thus, a better balance can be found between electricity generation and demand and the load on the electricity grid. In combined solar and wind farms (CSWFs), the turbines will cast shadows on the solar panels. This concerns the static shadow from the construction tower of the turbine as well as the dynamic shadow caused by the rotating blades. This paper reports on the results of millisecond data monitoring of the PV farm of a CSWF in the Netherlands on land. Static and dynamic shadow effects are discussed, as well as their dependency on farm design. It is observed that the dynamic shade of the wind turbine blade causes serious disturbances of the DC inputs of the inverter, resulting in deviation of the maximum power point tracking monitored. The shadow of the wind turbine results in a total energy loss of about 6% for the given period, park configuration, PV modules, inverter type, and setting. [ABSTRACT FROM AUTHOR]

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU. [ABSTRACT FROM AUTHOR]

This study focused on the two important gaps in the literature. The first is solar energy- powered electricity generation in a more economical way via the integration of flat plate solar collector (FPSC), an Organic Rankine Cycle (ORC), and an absorptional heat transformer (AHT) system. Another gap is advanced exergy analysis of the AHT cycle/ORC process based on renewable energy integration to reveal clues for improving the system. To close these gaps, a novel system including a lithium bromide AHT cycle-ORC with a FPSC system application was proposed in this study. In this proposed system, the temperature of the heat source for the ORC system was upgraded via an integration of the AHT and FPSC cycles. The main components of the AHT cycle are the condenser (ABScon), refrigerant cycle pump (P1), evaporator (EV), absorber (ABS), solution heat exchanger (SHX), absorbent cycle pump (P2), expansion valve (V), generator (Gen), ORC turbine (ORCT) and ORC condenser (ORCcon). To demonstrate the electricity production from solar energy in a more economical way thanks to the proposed system, a comparison was made with similar-scaled existing solar power plants. The results supported the main purpose of this study. The annual electricity production with the proposed system was calculated as 2601 MWh, with initial investment cost and payback period values of US$3.924 million and 4.531 years, respectively. The conventional and advanced exergy, exergoeconomic, environmental impact, and sustainability analyzes were also performed. Based on these, the novel performance parameters and prioritization method were proposed to assess the improvement potential of the system. The results indicated that SHX and FPSC had the highest exergy destruction rates (EDRs) of 23.711% and 21.849% over 5853.89 kW due to the stronger thermal and chemical reactions. Similarly, Gen, FPSC, and SHX had the highest ED cost rates (CRs) of 67.59%, 59.09%, and, 47.98%, respectively. Gen, V, and ORCcon were higher contributors to the exergy destruction rates of almost all the components. However, these showed an adverse manner for irreversibility activities. So, the temperatures of Gen and ORCcon should be optimized carefully. ABScon, P2, P1, ABS, Gen, ORCT, and ORCcon had high development priority to improve the whole system. [Display omitted] [ABSTRACT FROM AUTHOR]

Various types of PV modules are commercially available. Of the many types, the one that is recommended for constructing a solar power generation system is the one that generates power in an optimal location with high efficiency and high yield. To plan the deployment of a system, knowledge of the solar irradiance of a given location is essential to predict the possible electricity generation (PEG) of a defined area. Predeterminations based on simulations of insolation and PEG are currently being carried out at specific locations, but it has not been possible to determine the actual technical thrust. This paper introduces a peripheral trio-block synthesis (PTBS) methodology to calculate solar irradiance based on measured physical irradiance data. A metric called Module Preferred Rank (MPR) is also introduced to compare different types of PV modules. Solar irradiance at the location of latitude 13.6214 ON and longitude 79.2903 OE is physically measured using a solar sensor on the day immediately adjacent to the vernal equinox. The developed PTBS methodology is applied to calculate solar irradiance based on measured physical irradiance data. The calculated solar irradiance is used to evaluate PEG via various PV modules that can be deployed in the open area of the site in question, and the MPR of each type is evaluated. The proposed PTBS methodology can potentially become a real-time solar irradiance calculation methodology. The MPR evaluated for various constraints may serve as a reference for the end user to select the appropriate PV module type. Choosing the right PV modules among several can increase the contribution to green power. The results of this paper were able to confirm the above statements. [ABSTRACT FROM AUTHOR]

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