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Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature 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.)

The development of a dynamic model for a popular implemented solar power plant is a critical task for power engineers aiming to enhance the plant's performance and reliability. In this study, we utilized the prediction error method (PEM), a robust algorithm for system identification, to capture the plant's operational characteristics with precision. Additionally, we employed both recursive and hierarchical algorithms to identify the system parameters effectively. The results from each identification method are rigorously quantified and analyzed, allowing for a detailed comparison of their performance. This comprehensive evaluation not only highlights the strengths and weaknesses of each approach, but also provides valuable insights into their practical applications in the context of solar power systems. [ABSTRACT FROM AUTHOR]

Economic growth and the increasing demand for electrical energy require a breakthrough in the provision of electrical energy, one of which is the use of renewable and environmentally friendly energy. Solar energy is one of the renewable energies that can be used as a solar PV system. This solar PV system can also be combined with the PLN network (grid) and generators to meet electrical energy needs. This study aims to find out the amount of electrical energy efficiency value that can be produced by the plant every month, and whether it can affect the electricity bill paid by PT Pertamina Patra Niaga every month. Methods This research uses observation methods and collects various data related to the installation of Solar Power Plants on the Grid starting from April to September 2024. This research contains the results of the total electrical energy produced by the solar power plant, the efficiency of the solar power plant as well as the saving costs generated after the installation of the solar power plant at the Pertamina Labuhan Deli Fuel Depot. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter 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.)

In past years, concentrated solar power (CSP) with an energy backup system has been a unique renewable energy utilization system among intermittent renewable energy systems. It could allow a CSP plant to operate as a base load system in the future. This paper simulates a solar power plant for 1 MWe. Parabolic trough collector (PTC) array and linear Fresnel reflector (LFR) field attached consecutively to produce superheated steam at 40 MPa. The Rankine cycle has been used to run the steam turbine and an electricity generator is attached to a steam turbine to convert mechanical energy into electrical energy. Maximum temperature attained at the turbine inlet is 418.13 ˚C in 12:00–13:00 time slot in the month of January. Results show that solar power plant is feasible to produce 1 MWe. The minimum value of the power produced by the generator is 1.01 MWe in November in the 10:00–11:00 time slot whereas the maximum value of generated power is 1.57 MWe in December in the 11:00–12:00 time slot. The overall efficiency of power generated by the Rankine cycle is 21.25% in January in the 10:00–11:00 time slot. An energy storage system is attached to the system to work at night hours or in cloudy weather conditions. [ABSTRACT FROM AUTHOR]

Accurate forecasting of global horizontal irradiance (GHI) is critical for optimizing solar power plant (SPP) output, particularly in tropical locales where solar potential is high yet underutilized due to forecasting challenges. This research aims to enhance GHI prediction in one of the major cities of Indonesia, where existing models struggle with the area's natural climate unpredictability. Our analysis harnesses a decade of data 2011-2020, including GHI, temperature, and the Sky Insolation Clearness Index, to calibrate and compare these methodologies. We evaluate and contrast the exponential smoothing method versus the more complicated artificial neural network (ANN). Our findings reveal that the ANN method, notably its fourth iteration model with 12 input and hidden layers, substantially outperforms exponential smoothing with a low error rate of 1.12%. The use of these methodologies forecasts an average energy output of 252,405 Watt for a solar panel with specification 15.3% efficiency and 1.31 m2 surface area throughout the 2021 to 2025 timeframe. The work offers the ANN method as a strong prediction tool for SPP development and urges a further exploration into more advanced forecasting methodologies to better harness solar energy. [ABSTRACT FROM AUTHOR]

Photovoltaic energy system is one of the most popular and quickly expanding renewable energy sources because of its great availability, enormous potential, and environmental sustainability. In addition, photovoltaic systems produce clean energy and not pollutant thus contributing to the fight against global warming. Photovoltaic system requires less maintenance given the absence of mechanical components, and it can be also integrated to the electrical grid. The objective of this research is proposed an effective method for designing and Modeling of an 11 MWp photovoltaic plant capacity connected to the national grid of Niger by using software PVsyst. Method: choice of the type of system (system connected to the network), the geographic location of the installation site using the software database. Then comes the orientation of the photovoltaic system, the choice of the technology of the photovoltaic system (photovoltaic panels, inverters, transformers... etc) and their calibrations, Study of detailed losses (system losses, wiring losses and array losses) and execution of the command to simulate the system. Results: design and simulation model of an 11MWp photovoltaic power plant was developed composed of 29120 photovoltaic panels with a power of 385 Wp each, 133 MPPT inverters with a power of 66 KWac each and a Medium Voltage transformer with a capacity of 11 MW. The project has been simulated and results obtained has been analyzed. Practical significance of the research: Design and Simulation of photovoltaic system using the software allows the engineers, academics and researchers to acquire skills in the design and simulation of photovoltaic systems.

Abstract The development of a dynamic model for a popular implemented solar power plant is a critical task for power engineers aiming to enhance the plant’s performance and reliability. In this study, we utilized the prediction error method (PEM), a robust algorithm for system identification, to capture the plant’s operational characteristics with precision. Additionally, we employed both recursive and hierarchical algorithms to identify the system parameters effectively. The results from each identification method are rigorously quantified and analyzed, allowing for a detailed comparison of their performance. This comprehensive evaluation not only highlights the strengths and weaknesses of each approach, but also provides valuable insights into their practical applications in the context of solar power systems.

The automatic system model uses a Solar Charge Controller (SCC) to turn off the solar panel system at the minimum battery point so that the battery is safe and durable and an Automatic Transfer Switch (ATS) to automatically transfer the electricity network from State Electricity Company or Grid or solar panels. In this study, Solar Panel Priority Grid electricity is used if the solar panel is insufficient and hybrid system with solar panel priority with 2 cut out battery usage. The results of this study are 450WP Solar Panel, 100A SCC MPPT, 12V/100Ah Battery, and 3000 Watt Modified Sine Wave Inverter, Miniature Circuit Breaker, Contactor, Relay Switch, Time Delay Relay and Indicators. The results of the switching process test between the Solar Power Plant source and grid with ATS control can run automatically in Grid Priority Mode, meaning Solar Power Plant as a backup, or Solar Power Plant Priority Mode where the grid source is used as a power backup system. In the Battery Capacity Optimization System, Low Voltage Disconnect (LVD) Protection can work well, namely it can cut off the voltage from the inverter if the battery is in a low voltage state at a rating below 10.8 Volts, Auto Cut Charging Protection testing can charge the battery up to 13.8 Volts and Cycle Use, namely the process of this system can work to store energy while releasing energy to run the load. The results of this study can be used as a reference for the best choice of the most efficient Solar Power Plant system.

Abstract In past years, concentrated solar power (CSP) with an energy backup system has been a unique renewable energy utilization system among intermittent renewable energy systems. It could allow a CSP plant to operate as a base load system in the future. This paper simulates a solar power plant for 1 MWe. Parabolic trough collector (PTC) array and linear Fresnel reflector (LFR) field attached consecutively to produce superheated steam at 40 MPa. The Rankine cycle has been used to run the steam turbine and an electricity generator is attached to a steam turbine to convert mechanical energy into electrical energy. Maximum temperature attained at the turbine inlet is 418.13 ˚C in 12:00–13:00 time slot in the month of January. Results show that solar power plant is feasible to produce 1 MWe. The minimum value of the power produced by the generator is 1.01 MWe in November in the 10:00–11:00 time slot whereas the maximum value of generated power is 1.57 MWe in December in the 11:00–12:00 time slot. The overall efficiency of power generated by the Rankine cycle is 21.25% in January in the 10:00–11:00 time slot. An energy storage system is attached to the system to work at night hours or in cloudy weather conditions.

Solar is a promising renewable energy source for Indonesia's increasing electricity demand, which grows at the rate of 3.3% annually. However, high investment costs and unclear policies hinder Solar Power Plant (SPP) development. Considering the potential for growth in energy demand and the low long-term operational costs, it is imperative to foster SPP expansion in Indonesia. This study aims to identify location criteria and potential SPP development sites in Indonesia. We employ Multi-Criteria Decision-Making (MCDM) combining Fuzzy-AHP and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies to assign criteria weights and prioritize alternative SPP locations. Results show that, from all respondents using the geometric mean in the Fuzzy Analytic Hierarchy Process (AHP), it is found that the Economic criteria give the highest weight at 31%, and subcriteria of Land Availability, Peak Sun Hours, Geographic Location, Distance from Transportation Networks, Construction Costs, and Government Regulations contribute significantly. The ranking of alternative solutions indicates that South Sumatra Province holds the highest priority for SPP development with a 0.572 score, closely followed by North Sumatra at 0.571

Together with the growing interest towards renewable energy sources within the framework of different strategies of various countries, the number of solar power plants keeps growing. However, managing optimal power generation for solar power plants has its own challenges. First comes the problem of work interruption and reduction in power generation. As the system must be tolerant to the faults, the relevance and significance of short-term forecasting of solar power generation becomes crucial. Within the framework of this research, the applicability of different forecasting methods for short-time forecasting is explained. The main goal of the research is to show an approach regarding how to make the forecast more accurate and overcome the above-mentioned challenges using opensource data as features. The data clustering algorithm based on KMeans is proposed to train unique models for specific groups of data samples to improve the generation forecast accuracy. Based on practical calculations, machine learning models based on Random Forest algorithm are selected which have been proven to have higher efficiency in predicting the generation of solar power plants. The proposed algorithm was successfully tested in practice, with an achieved accuracy near to 90%. [ABSTRACT FROM AUTHOR]

High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods. [ABSTRACT FROM AUTHOR]

The article provides a description of the microgrid function optimization model for solving the problem of reducing the cost of electricity purchases by the owner of such a microgrid on the retail electricity market. The objective function of minimizing the cost of electricity purchase has been formalized. Approaches to determining the coefficients of penalty functions designed to cut off technically unacceptable modes of the microgrid have been determined. A mathematical model of an energy storage system (ESS) is proposed, in which the functions of calculating the technological costs of electricity are formalized. A system of constraints for the problem of minimization of the cost of electricity purchase is given. Approaches to solving the problem of evaluating the effect of SPP and ESS operation in microgrids using optimization results are determined. Examples of microgrid operation optimization for an enterprise with its own SPP and ESS are presented. The need for additional control of the economic expediency of involving ESS in the regulation of electricity balances at the enterprise was noted. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ilmu Manajemen Terapan (JIMT). is the property of Dinasti Publisher 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 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.)

Solar photovoltaic power plants typically consist of rows of solar panels, where the accurate estimation of solar irradiance on inclined surfaces significantly impacts energy generation. Existing practices often only account for the first row, neglecting shading from subsequent rows. In this work, ten transposition models were assessed against experimental data and a transposition model for inner rows was developed and validated. The developed model incorporates view factors and direct and circumsolar irradiances shading from adjacent rows, significantly improving global tilted irradiance (GTI) estimates. This model was validated against one-minute observations recorded between 14 April and 1 June 2022, at Évora, Portugal (38.5306, −8.0112) resulting in values of mean bias error (MBE) and root-mean-squared error (RMSE) of −12.9 W/m2 and 76.8 W/m2, respectively, which represent an improvement of 368.3 W/m2 in the MBE of GTI estimations compared to the best-performing transposition model for the first row. The proposed model was also evaluated in an operational forecast setting where corrected forecasts of direct and diffuse irradiance (0 to 72 h ahead) were used as inputs, resulting in an MBE and RMSE of −33.6 W/m2 and 169.7 W/m2, respectively. These findings underscore the potential of the developed model to enhance solar energy forecasting accuracy and operational algorithms' efficiency and robustness. [ABSTRACT FROM AUTHOR]

The global trend of reducing the "carbon footprint" has influenced the dynamic development of projects that use renewable energy sources, including the development of solar energy in large solar power plants. Consequently, there is an increasingly pronounced need in scientific circles to consider the impact these projects have on space and the environment. The fact that international financial institutions consider environmental effect to be a significant factor when funding solar energy projects is one of the main reasons this topic is so important in professional circles, particularly among solar energy investors. This paper highlights the fact that solar power plants can have both positive and negative impacts on space and the environment. Those impacts need to be defined in order to choose optimal spatial and territorial solutions that ensure preventive planning and active environmental protection. In the process, the application of strategic environmental assessment (SEA) in the planning and spatial organization of solar power plants becomes important. SEA is characterized by a holistic approach where complex interactions and correlations in the location of planned implementation of the solar power plant can be understood at the earliest stage of project development. By doing this, it is possible to prevent all potential risks that may emerge in the project's later stages of implementation, which is favorable both from the aspect of effective environmental protection and from the point of view of investors investing in solar power plant projects. Optimal solutions that bring about the basic role of SEA are sought primarily in the analysis of the spatial relations of the solar power plant with regard to land, biodiversity, landscape, and basic environmental factors, which is particularly highlighted in the paper. Also, the basic methodological concept applied in SEA is demonstrated, combining different methodological approaches and methods for impact assessment, as part of a unique semi-quantitative method of multi-criteria evaluation of planning solutions. [ABSTRACT FROM AUTHOR]

The widespread usage of distributed generation units based on renewable energy sources is leading to the transformation of traditional centralized power systems and the increasingly active implementation of sustainable energy. Efficient usage of renewable energy sources enables to unload local and distribution grids and significantly improve the reliability of electricity supply. At the same time, adverse energy overflows may occur, leading to additional power and voltage losses. In addition, distributed generation units connected to industrial power grids may also have a negative impact on the quality of electricity in them. Besides,the traditional methods of mitigating the negative impact of distributed generation sources cannot always ensure the required quality of electricity and electromagnetic compatibility of consumers with the grid. Local microsystems, which have a modular structure and can operate both with a connection to the general power grid and in an isolated mode, allow for more efficient usage of renewable energy sources in the power grids of individual consumers. The article proposes the structure of energy-efficient electrical grid of an industrial enterprise with sources of distributed generation. It is expedient to create three local interconnected grids: an alternative current grids to which AC wind turbines are connected without quality assurance units for the generated electricity and consumers who do not need high-quality electricity ; AC grids with high-quality electric supply, as well as direct current grids with the possibility of connecting photovoltaic installations, DC wind turbines and DC consumers. This approach greatly simplifies the integration of renewable energy sources into the grids of industrial enterprises, improves electromagnetic compatibility, and makes it unnecessary to ensure the quality of the entire volume of electricity consumed. In addition, the proposed power grid structure reduces unwanted power overflows and facilitates the creation of a unified control system based on Smart Grid communication technologies. [ABSTRACT FROM AUTHOR]

The main results of the reports at the "Solar Energy" section were considered and summarized, in particular, one of the areas of solar energy engineering - solar power generation. Specialists from Ukraine and the Republic of Uzbekistan took part in the work of the section in this field. A total of 32 reports were presented, focusing on topics such as the implementation of distributed generation facilities based on photovoltaic systems, improving the energy efficiency of solar power plants, etc. The reports demonstrated the primary trends to highlight the issues of building and operation of energy-efficient solar power plants in today's realities. [ABSTRACT FROM AUTHOR]

Relevance. А simple and up-to-date methodology is needed to assess the potential of electricity that can be obtained from solar power plants installed in different regions. The difficulty in assessing the potential of solar energy lies in the presence of some factors that affect electricity generation and depend on random events. Such factors include, first of all, the sky clarity, the direction of photovoltaic panels strictly to the Sun, dustiness, and ambient temperature. The literature uses different approaches to assess the potential of solar energy. This paper presents a methodology for evaluating the generation of electricity by a solar panel having a continuous solar tracking system, taking into account the dustiness and temperature of photovoltaic panels. The technique has been tested on an existing physical model. Aim. To study the existing methods for assessing the potential of solar energy and to select the most suitable ones for the central part of Russia. Object. Solar power plants. Methods. Empirical and analytical methods. Using data obtained from an operating solar station, the adequacy of the proposed methodology was assessed. Results. The selected methods take into account the hourly sky clarity index, which made it possible, with sufficient accuracy for engineering techniques, to determine the daily power generation of a solar station with different angles of inclination of solar panels. This will improve the accuracy of estimating the potential of electricity generated by solar power plants, both with stationary photovoltaic panels and with solar tracking systems. Based on the conducted computational experiments and the analysis of data obtained from an operating solar station, the authors concluded that the technique presented in this paper allows us to determine with high enough accuracy the potential of electricity that can be produced in Russia by solar stations.

Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner.

In this study, 11 provinces with more than 1 MW solar power plants (SPP) in Turkey have been examined in terms of global solar radiation and power produced. The main objective of this study is to underline the serious differences in technical feasibility analysis conducted by the academic circles and solar energy industrial environments in Turkey. The obtained results were compared with the values operated by the private installation companies in the industry, these were interpreted, and the disagreements were revealed. Moreover, the study reveals some gaps in technical feasibility analysis, which the sector professionals usually ignore in the field. The findings were evaluated in monthly, annual, and seasonal time zones. The present study showed that the analysis, to which private companies in the field refer, is insufficient and the parameters that would guide the investor in detail could not be obtained. More detailed prospection has been obtained within the scope of this study. Considering the whole of our findings, while the most advantageous location for investing in SPPs in Antalya with a total of 97.50 h monthly average sunshine duration and radiation of 59,599.6 W/m2 throughout the year, the most disadvantaged location is Kahramanmaraş with a total monthly average sunshine duration of 80.80 h and a radiation value of 44,810.8 W/m2-year. Antalya is followed by Mardin location (monthly average sunshine duration of 96.10 h and radiation of 59,487.9 W/m2-year) and Karaman (monthly average sunshine duration of 94.40 h and radiation of 58,847.1 W/m2-year). [ABSTRACT FROM AUTHOR]

As the global quest for sustainable energy solutions intensifies, the role of solar power plants in meeting energy demands while mitigating environmental impact becomes increasingly vital. This study focuses on the performance evaluation and economic analysis of Engreen Sarishabari Solar Plant Ltd., a 3.3 MW grid-connected photovoltaic (PV) power plant located in Sarishabari, Jamalpur, Bangladesh. A thorough evaluation of the solar power plant's performance ratio was carried out using the PVsyst software and data supplied by plant authorities. Moreover, a mathematical model was developed to analyze energy output variations, while grid stability post-integration of PV into the distribution feeder was examined under diverse conditions, including load and irradiance variations, as well as different short-circuit fault scenarios. The findings reveal that the annual performance ratio of the solar power plant stands at approximately 71%, with an average annual energy production of 3132 MWh. With a payback period of 10.1 years and an energy generating cost per kWh of 0.1132 USD, the installation cost came to 6,740,853 USD. This research not only provides valuable insights into the operational efficiency and economic viability of the Engreen Sarishabari Solar Plant Ltd. but also contributes to the broader discourse on the integration of solar energy into the grid infrastructure of Bangladesh, offering practical implications for policymakers, energy stakeholders, and investors striving towards a sustainable energy future. [ABSTRACT FROM AUTHOR]

Steam power generation is one of the most important energy productions in the world and needs to be improved to reduce the greenhouse effect while increasing electricity production. This paper deals with the energy, exergy, and exergoeconomic analysis of a steam-generating solar power plant. A general methodology is presented to define and calculate the exergy efficiency, exergy destruction, exergoeconomic factors, total costs, improvement potentials, and exergy costs in thermal systems. The methodology is based on a specific exergy cost approach and a sensitivity cost analysis. The thermodynamic properties of the working fluid are determined using THERMAX and MATLAB software packages. For the considered normal operating and economic conditions, the percentage of exergy destruction relative to the total exergy destruction and potential improvement of the boiler was found to be the highest at 86% and 85.3%, respectively. The exergoeconomic coefficient of the system is calculated with a value of 0.52. The total cost of exergy losses is $5939.6 per hour. Furthermore, the results of the solar direct evaporation analysis show that the behavior of the exergoeconomic coefficient in January and July was calculated with values of 0.64 and 0.34, respectively. The total costs are $3010.4 and $5480 per hour, respectively. Obtaining specific values and clear parameter influences is a valuable achievement and helps field engineers and operators effectively perform their individual tasks while taking into account the conflicts between energy consumption, exergy and costs. [ABSTRACT FROM AUTHOR]

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems. [ABSTRACT FROM AUTHOR]

Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner. [ABSTRACT FROM AUTHOR]

Today, the world is turning to use renewable energy to solve the problems of fuel shortage and pollution due to CO2 emissions from the use of fossil fuels. In this study, parabolic trough solar collectors (PTC) with two types of heat transfer fluids HTF are used to investigate the performance of a retrofitted steam power plant using solar energy. A thermo-economic analysis was performed for a 10 MW simple steam power plant with different boiler pressure from 10 to 100 bar and located in the city of Basra in Iraq which receives high levels of solar radiation. Basra's weather conditions are used to simulate the solar-assisted regenerative system using a parabolic trough collector (PTC). According to the system analysis, it was found that increasing the boiler pressure reduces the area required for the PTC heater for constant power output. For 10 bar operating pressure the required PTC area is 64233,562 m² while for 100 bar operating pressure the required PTC area is 42907.59 m². Also, it was estimated that the Levelized Cost of Energy (LCOE) decreased with increasing operating pressure. The decrease in LCOE for PV1 heating fluid is 43.25% and the decrease in LCOE is 43.16% for the pressure range from 10 to 100 bar. [ABSTRACT FROM AUTHOR]

This study is focused on the problem of contractor selection for the implementation of a solar power plant project to produce electricity from sustainable sources for the needs of the company Voćar. The goal of this research is to select a construction contractor to install a solar power plant using sustainability criteria. With this power plant, the company Voćar can reduce its electricity costs and contribute to the production of sustainable energy. A total of three main sustainability criteria were used, in which six auxiliary criteria were symmetrically distributed. With these criteria, six suppliers were analyzed, and expert decision making was carried out with the application of the fuzzy–rough approach. To define the weights of the criteria, the SWARA method was utilized in this study. Based on the findings of this method, the most important criteria are the ecological criteria. Using the CRADIS method, the contractors were ranked, and the results show that contractor C6 has the best results and is the first choice for implementing this project. Choosing the best supplier increases the sustainability of project implementation and the realization of the expected effects. [ABSTRACT FROM AUTHOR]

The purpose of the study is to determine the feasibility and conditions for the usage of hydrogen energy storage systems in the power grids of industrial enterprises with available renewable energy sources. The constant growth of renewable energy sources connected to the general power systems and power grids of industrial enterprises allows strengthening the energy security of the country and reducing the costs of electricity for enterprises. At the same time, the random nature of RES generation requires an increase in the number of highly maneuverable capacities needed for the steady operation of such an energy system. Today, the such capacities are provided mainly by hydroelectric power plants, which may not be enough with further growth in the generation of renewable energy sources. Thus, in order to reduce the impact of stochasticity on electricity generation, RES should be combined with energy storage systems. In this case, the capacity required for electricity storage depends on the hourly volumes of RES generation, the energy consumption profile of the enterprise, its base and maneuvering loads, operating mode, etc. The paper proposes a methodology for determining the appropriate parameters of the components of hydrogen energy storage systems, which take into account the random nature of the electricity generation by renewable energy sources and the actual consumption of an industrial enterprise. The application of this methodology is considered on the example of one of the industrial enterprises, on the territory of which it is possible to install solar panels and wind turbines. [ABSTRACT FROM AUTHOR]

Among the types of renewable energy, solar energy has received more attention due to its ability to be directly converted into electricity and heat, ease of use, possibility of storage, and its endless nature. Therefore, in recent decades, a lot of research has been done on solar energy systems in Iran and the world. Makran region, located in the southeast of Iran, has one of the greatest potentials of using solar energy among other regions of Iran. In this research, a one-megawatt power plant in Chabahar city, located in Sistan and Baluchistan province, on the southeast coast of Iran, has been modeled using RETScreen software and issues such as return on investment, internal rate of return (IRR), net present value (NPV) and carbon dioxide emissions. levels have been investigated. Finally, according to the considered conditions of the investment return period, according to the guaranteed purchase contract with a tender price of 73 dollars, the amount of carbon dioxide production in the region will be reduced for about 3 years and about 1009 tons per year. [ABSTRACT FROM AUTHOR]

In the present work, the absorber tube of the DISS (Direct solar steam) solar plant in Plataforma Solar de Almeria (Spain) is powered by fluid flows in a swirl motion. Numerical simulations are used to predict the performance of a parabolic trough solar collector with inlets that allow the working fluid to flow in a swirl motion inside the absorber tube. There are numerous sites for tangential inlets distributed along the absorber tube. There is one inlet placed up, one located down, two located up, and two located down. High Reynolds numbers between 2.2 × 105 and 6.2 × 105 are used for the simulation. When the collector with the tangential inlet is compared to a conventional collector, the results reveal that using one inlet down causes a 9.1K decrease in the average temperature of the lower surface of the absorber tube. Compared to axial flow, one inlet down increases useful heat and heat transfer by 12.7 % and 41.7 %, respectively. The average friction factor of axial flow is 16.4 % higher than that of one inlet down. The collector with a tangential inlet reduces surface area by 10.9 % over Reynolds ranges when compared to an axial flow collector. Therefore, the thermal strain on the absorber tube and collector surface area is reduced by the swirling flow in the parabolic trough solar collector. As a helpful design tool, thermal performance correlations of absorber tubes with tangential inlets are constructed.

ABSTRAK Paper ini menganalisis secara teknis dan ekonomi PLTS (pembangkit listrik tenaga surya) hibrid off-grid untuk rumahtangga di Yogyakarta, Indonesia. Sistem PLTS terdiri dari delapan panel surya polikristalin dengan daya 410Wp masing-masing, inverter 3kW, dan baterai LiFePO4 (lithium ferrophosphate) 48V 300Ah. PLTS telah dioperasikan sejak awal 2021, dan energi yang terkumpul dari panel surya dimulai November 2021 hingga akhir Agustus 2022, menunjukkan variasi nilai dari 3,2kWh hingga 17,4kWh. Dengan mempertimbangkan efisiensi perangkat 85%, hasil total energi diperkirakan 3.740,7 kWh per tahun. Penelitian ini mendapatkan nilai hasil rata-rata tahunan sebesar 1.140,5 kWh/kWp dan rasio kinerja 63,4%. NPV (net present value) digunakan sebagai indikator kelayakan investasi, dengan hasil masa investasi 24 tahun, tingkat diskonto 7%, laju degradasi PV (fotovoltaik) sebesar 0,8%, dan penurunan baterai sebesar 1,5% per tahun. Studi ini menyoroti potensi keuntungan investasi PLTS dengan kondisi tertentu dan mendorong intervensi regulasi pemerintah untuk meningkatkan aplikasi PLTS. Kata kunci: PLTS, off-grid, NPV, panel surya, rasio kinerja ABSTRACT This paper presents technical and economic analysis of hybrid off-grid SPP (Solar Power Plant) for a home in Yogyakarta City, Indonesia. The system consists of eight polycrystalline photovoltaic with 410Wp each, 3kW inverter and a 48V 300Ah LiFePO4 (lithium ferrophosphate) battery. The harvested energy was recorded starting from November 2021 to the end of August 2022. The recorded data shows the variation of values from 3.2kWh to 17.4kWh. The annual energy is 3,740.7 kWh by considering 85% devices efficiency. Furthermore, an annual average final yield is 1,140.5 kWh/kWp and performance ratio is 63.4%. NPV (net present value) was used as an indicator of feasibility investments by considering device lifespan, 7% discount rate, 0.8% of PV (photovoltaic) degradation rate and 1.5% battery fade per year. This study highlights the potential benefits of investing in hybrid off-grid SPP under specific conditions and advocates for government intervention through regulations to enhance the SPP applications. Keywords: Solar Power Plant, off-grid, NPV, photovoltaic, performance ratio

Searching for faulty, and therefore operating in abnormal mode, solar panels at a power plant is an urgent task in the context of the development and growth of the share of solar energy in electricity generation. The research is aimed at developing and evaluating the effectiveness of a new methodology and software algorithm for searching for anomalies in the operation of solar panels based on the results of a digital twin created and trained using telemetry data from a solar power plant. The methodology is based on studies of deviations in power values at the point of maximum efficient operation of the solar panel, calculated by the digital twin, from the average statistical values for the power plant. Using the proposed methodology, over six months of direct observations, 16 anomalies in the operation of the solar panels of the power plant were discovered and confirmed. It has been established that when analyzing deviations of normalized power values at the maximum power point PN, it is possible to detect solar panels that have defects or operate with loss of efficiency.

Abstract The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ‘very low’ suitability of site covering 4.866% of the study area, ‘low’ suitability of site 13.190%, ‘moderate’ suitability of site 31.640%, ‘good’ suitability of site 32.347%, and ‘very good’ suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions.

In the field of energy networks, for their effective functioning, it is necessary to distribute the required load between all online generating units in a proper way to cover the demand. The load schedule is obtained by solving the so-called Economic Dispatch Problem (EDP). The EDP can be solved in many ways, resulting in a power distribution plan between online generating units in the network so that the resulting price per unit of energy is minimal. This article focuses on designing a distributed gradient algorithm for solving EDP, supplemented by models of renewable sources, Battery Energy Storage System (BESS), variable fuel prices, and consideration of multiple uncertainties at once. Specifically, these are: time-variable transport delays, noisy gradient calculation, line losses, and drop-off packet representations. The algorithm can thus be denoted as robust, which can work even in unfavorable conditions commonly found in real applications. The capabilities of the presented algorithm will be demonstrated and evaluated on six examples. [ABSTRACT FROM AUTHOR]

Solar energy is one of the sources of renewable energy which is clean, affordable, and limitless. Despite Bangladesh having a significant potential for solar energy for its excellent geographic location, relatively low research work on location selection for the establishment of solar plants and little attempt has been taken to expand the solar energy industry. The optimal location for a solar plant to be built is the main objective of this research. Considering several aspects, five potential locations are selected utilizing the geographic data to find the optimal location and the Analytic Hierarchy Process (AHP) which is one of the multi‐models assessment techniques implemented to get the ideal site for solar power plants. While applying this method, some expert survey has been done via Google Form where some of the principal impact factors are set up like questionnaire and obtained the weight for the criteria for the AHP method. The results of the current analysis indicate that Rajshahi is obtained as best location for solar plant establishment followed by Ishwardi, Chattogram, Chuadanga, and Mymensingh. This work will enable to propose an effective, practical, and structured decision‐making framework that could aid policymakers in Bangladesh to select the best locations for solar farms. [ABSTRACT FROM AUTHOR]

Indonesia has abundant potential for solar energy. The decrease in the cost of solar power generation components can bolster the development of solar power plants. Due to its geographical characteristics, it is essential to analyze the feasibility of using solar power plants as a primary renewable energy source in Indonesia, especially on Sumatra Island. One of the critical aspects of developing solar power plants is determining the suitable location of the power plant and allocating the electricity generated to the regions. Therefore, this study considers the Capacitated Location Allocation Problem (CLAP) to determine the optimal placement of solar power plants on Sumatra Island to minimize investment and transmission costs. To address the problem, we explore three metaheuristics, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Large Neighborhood Search (LNS). The results obtained by these metaheuristic methods show significant differences in cost, with SA providing the best solution with the lowest cost. The investment and transmission cost can be minimized by solving the CLAP to obtain optimal solar power plant placement while enhancing the region's resilience in implementing distributed generation. [ABSTRACT FROM AUTHOR]

To support the energy transition and maintain the Green Port title, it is necessary to design a Solar Power Plant (PLTS) in the PT Pupuk Kalimantan Timur Port Office Building. In designing a PLTS system, a design analysis is needed that is appropriate to the roof capacity or roof area available on the building and the potential for solar radiation at that location. The stages carried out include selecting a PLTS system, selecting PLTS components, creating a PLTS design using the HelioScope simulator, and calculating investment feasibility. The results of this research show that there is potential for electricity production of 109.8 MWh per year with the construction of PLTS in the PT Pupuk Kalimantan Timur Port Office Building. The appropriate PLTS installation design is PLTS with an On-Grid system, consisting of 158 solar panels with a capacity of 540 Wp (Seraphim 540 BMA HV) and 1 inverter with a capacity of 100 kW (SUN 200-100KTL-M1). The installed capacity of PLTS is 85.3 kWp with a Performance Ratio of 79.6% and specific energy of 1,286.8 kWh/kWp. Investment in the construction of PLTS in the PT Pupuk Kalimantan Timur port office building is feasible with an investment value of IDR 872,515,500,-, IRR of 11.18% (IRR > WACC) and NPV of IDR 8,789,894.50 (NPV>0). The estimated return on capital can be carried out in the 7th year. It is hoped that this research can be a reference for the design and calculation of installing PLTS in the PT Pupuk Kalimantan Timur Port Office Building. [ABSTRACT FROM AUTHOR]

This paper examines the consideration of the design features of closed-cycle pumped-storage hydroelectric power stations, as well as other gravitational energy accumulators to ensure the accumulation of energy from renewable energy sources by means of a comparative analysis. The tasks of research are a rational way of accumulating electricity from wind and solar power plants require a comprehensive approach, taking into account all the features of renewable energy sources. The growing installed capacity of photo and wind power plants is associated with the need to solve the problem of instability of electric power generation over time. The analysis of various storage technologies, including gravity accumulators, hydroaccumulating power plants of underground and marine types, thermal storage, emphasizes that the closed-cycle pumpedstorage hydroelectric power stations is an effective energy accumulator for autonomous energy systems of renewable energy. A comparative analysis of closed-cycle pumped-storage hydroelectric power stations showed that stations of this type, in comparison with classic ones, require a smaller amount of working fluid to ensure the same power, which is achieved due to the creation of a higher head up to 600m and the fact that the external source of water needed only to cover system losses. The reduction of environmental impacts is also established, as the installation requires a smaller size of exclusion areas, and the possibility of using tunnels and underground stations minimizes the impact on the environment. It was established that the number of potential places for the installation of closed-cycle pumped-storage hydroelectric power stations is over 615,000 around the world with a total accumulation potential of 23.1 million GWh. [ABSTRACT FROM AUTHOR]