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Photovoltaic development has played a crucial role in mitigating the energy crisis and addressing global climate change. However, it has also had significant impacts on the ecological environment. To ensure the sustainable growth of the photovoltaic industry, it is essential to establish an indicator system to assess the ecological and environmental effects of photovoltaic development. This study utilizes the Driving-Pressure–Status–Impact-Response (DPSIR) framework to create an indicator system for evaluating the ecological and environmental effects of desert photovoltaic development. The study evaluates the ecological and environmental effects at the on-site (WPS), transitional zone (TPS), and off-site (OPS) areas of the Qinghai Gonghe Photovoltaic Park in China. The entropy weight method was utilized to calculate indicator weights, while the evaluation model and indicators were transformed uniformly to obtain standardized scores for ecological and environmental effects. and conducting a thorough analysis of the distribution characteristics and factors influencing the evaluation indicators' scores. Overall, the large-scale development of desert photovoltaics in Gonghe County has had a positive impact on the ecological environment. The WPS had better ecological and environmental conditions than did the TPS and OPS, and the ecological and environmental evaluation levels of the WPS were categorized as "general" (0.439), while the ecological and environmental effect evaluation levels of the TPS (0.286) and OPS (0.28) were both "poor", indicating significant room for improvement. Moreover, all indicators in the scheme layer, which are used to evaluate ecological and environmental quality, yielded higher scores for the WPS than for the TPS and OPS, demonstrating that photovoltaic development has a positive effect on desert area ecology and the environment. [ABSTRACT FROM AUTHOR]

Abstract Photovoltaic development has played a crucial role in mitigating the energy crisis and addressing global climate change. However, it has also had significant impacts on the ecological environment. To ensure the sustainable growth of the photovoltaic industry, it is essential to establish an indicator system to assess the ecological and environmental effects of photovoltaic development. This study utilizes the Driving-Pressure–Status–Impact-Response (DPSIR) framework to create an indicator system for evaluating the ecological and environmental effects of desert photovoltaic development. The study evaluates the ecological and environmental effects at the on-site (WPS), transitional zone (TPS), and off-site (OPS) areas of the Qinghai Gonghe Photovoltaic Park in China. The entropy weight method was utilized to calculate indicator weights, while the evaluation model and indicators were transformed uniformly to obtain standardized scores for ecological and environmental effects. and conducting a thorough analysis of the distribution characteristics and factors influencing the evaluation indicators’ scores. Overall, the large-scale development of desert photovoltaics in Gonghe County has had a positive impact on the ecological environment. The WPS had better ecological and environmental conditions than did the TPS and OPS, and the ecological and environmental evaluation levels of the WPS were categorized as “general” (0.439), while the ecological and environmental effect evaluation levels of the TPS (0.286) and OPS (0.28) were both “poor”, indicating significant room for improvement. Moreover, all indicators in the scheme layer, which are used to evaluate ecological and environmental quality, yielded higher scores for the WPS than for the TPS and OPS, demonstrating that photovoltaic development has a positive effect on desert area ecology and the environment.

As large-scale photovoltaic power plants gradually move towards high-altitude and cold regions, accurate evaluation of the impact of freeze-thaw erosion hazards on the safe operation of photovoltaic power plants is essential. This study, based on a large-scale photovoltaic power plant in a high-altitude and cold area, conducted an initial calculation of the freeze-thaw erosion intensity in the study area by selecting appropriate environmental factors. By coupling human engineering quantitative indicators, a targeted and reasonable evaluation model for the freeze-thaw erosion intensity of photovoltaic sites was proposed, and the freeze-thaw erosion intensity and its spatial distribution under multiple environmental factors and human engineering conditions were explored. The results showed that the freeze-thaw erosion intensity of the photovoltaic site was generally moderate, with a certain degree of concentration and spatial heterogeneity in its distribution, which was mainly affected by topography, climate, and vegetation factors. Furthermore, the construction of the photovoltaic power plant had a certain weakening effect on the freeze-thaw erosion intensity of the site. The research results can provide reference and guidance for the freeze-thaw erosion evaluation of similar large-scale projects. [ABSTRACT FROM AUTHOR]

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics. [ABSTRACT FROM AUTHOR]

Renewable energies are a major tool to reduce greenhouse gas emissions and combat climate change. Among renewables, solar parks are a key technology and their expansion will strongly increase in the future. Hence, there is a great need for research regarding their impact on biodiversity. In this study, we investigated the importance of three solar parks in northern Germany as habitat for carabid beetles. More specifically, we determined how habitat characteristics (temperature, vegetation structure), species richness, activity densities and carabid beetle assemblages vary between different locations within solar parks: areas under solar panels "UNDR", gaps between panel rows "BTW" and areas at the edge of solar parks "EDGE". We found that UNDR strongly differed from BTW/EDGE in terms of habitat conditions, species richness and activity densities of carabid beetles. UNDR mainly provided habitat for a species- and individual-poor subset of species already present in BTW/EDGE and made only a small contribution to the total species richness of the investigated solar parks. BTW and EDGE, on the other hand, differed only slightly. Thus, the narrow gaps between the panel rows were of similar importance as a habitat for carabid beetles as the edge areas. Overall, the investigated solar parks—and thus also the more species-rich areas EDGE and BTW—almost exclusively provided habitat for widespread and common non-threatened generalists, while species of conservation concern were extremely rare. Implications for insect conservation: The low importance of UNDR for carabid beetles should be considered when assessing the biodiversity value of solar parks, since a large proportion of these facilities is covered with solar panels. Between panel rows and at the edge of solar parks, larger areas should be kept free from construction with solar panels in order to provide habitats for carabid beetles and other species groups. More research should be conducted on how to further improve solar parks for carabid beetles and overall biodiversity. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office 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.)

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia's programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management. [ABSTRACT FROM AUTHOR]

The safety of workers and equipment in the power grid requires the shutdown of power plants in case of maintenance or malfunction. The shutdown relies on protective devices that must be properly coordinated to isolate only the part of the grid affected by the malfunction. Using the DIgSILENT PowerFactory software, an analysis was conducted on a model of an accurate real grid integrating a photovoltaic power plant with a capacity of 390 kW. Protection coordination testing was carried out for three-phase, two-phase, and single-phase short circuit currents at arbitrarily selected locations at medium and low voltage levels. Protection in the time domain is coordinated on lines and busbars to determine the speed and selectivity of protective devices. The analysis results indicate that adequately adjusting the three-phase short circuit at the main transformer output 110/10 kV with an impedance of 0 Ω and an allowed protection operation time of up to two seconds can be correctly addressed within 36.7 ms.

The aim of this study is to assess the economic potentials of power and hydrogen generation via solar and wind energy resources at locations in Northern Germany and California, both of these (geographical) regions being pioneers in terms of a sustainable energy transition. Based on extensive research and electrolyzer manufacturer interviews, green hydrogen generation plants are modeled in a MATLAB® environment. All relevant inputs and outputs of the systems studied (wind power plants, photovoltaic power plants, and polymer electrolyte membrane electrolyzers, among others) are considered for different locations and framework conditions. In contrast to the existing literature, special attention is paid to the part-load behavior of electrolysis systems, which becomes particularly relevant in their interplay with volatile renewables. Metrics such as the levelized costs of electricity (LCOE) of the wind and photovoltaic power plants and the resulting levelized costs of hydrogen (LCOHy) are determined. With the help of the developed model, dimensioning of a whole system's components can be determined for different locations. A case study is conducted for a Northern German site and another one for a site in the Californian Mojave Desert. Both the optimal dimensioning of the renewable energy plants and the ratio of installed wind and photovoltaic power plant capacity are strongly location-dependent. In Northern Germany, for example, lower LCOHy can be generated at higher wind power plant capacity shares and, in the Mojave Desert, be produced at higher photovoltaic power plant capacity shares. In general, with larger plants and correspondingly larger polymer electrolyte membrane electrolyzers, LCOHy are lower due to scaling effects. Following this dimensioning recommendation, the LCOHy vary between 4.5 €/kg and 5.2 €/kg in the Northern German case study and between 4.6 US$/kg and 5.3 US$/kg in the Californian one. With costs of 1–2 €/kg, gray hydrogen is still economically superior. • Hydrogen production is modeled based on electrolyzers, wind and PV power plants. • Due to the volatile power supply, a focus is on part-load behavior of electrolyzers. • The optimal sizing of the system components is determined for different locations. • Two case studies are conducted, for Northern Germany and the Mojave Desert, CA. • Levelized costs of hydrogen are found to vary between 4.5 and 5.2 €/kg (4.6–5.3 US$/kg). [ABSTRACT FROM AUTHOR]

With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×106. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field. [ABSTRACT FROM AUTHOR]

The safety of workers and equipment in the power grid requires the shutdown of power plants in case of maintenance or malfunction. The shutdown relies on protective devices that must be properly coordinated to isolate only the part of the grid affected by the malfunction. Using the DIgSILENT PowerFactory software, an analysis was conducted on a model of an accurate real grid integrating a photovoltaic power plant with a capacity of 390 kW. Protection coordination testing was carried out for three-phase, two-phase, and single-phase short circuit currents at arbitrarily selected locations at medium and low voltage levels. Protection in the time domain is coordinated on lines and busbars to determine the speed and selectivity of protective devices. The analysis results indicate that adequately adjusting the three-phase short circuit at the main transformer output 110/10 kV with an impedance of 0 O and an allowed protection operation time of up to two seconds can be correctly addressed within 36.7 ms. [ABSTRACT FROM AUTHOR]

This article is devoted to the problem of forecasting electricity generation by photovoltaic power plants based on meteorological data from open sources using machine learning methods. To solve the problem proposed in the article, an overview of existing meteorological data sources and possible methods of processing them is given, as well as a simplified LSTM algorithm based on the architecture of the machine learning methodology for predicting solar energy generation a day earlier. [ABSTRACT FROM AUTHOR]

As the construction of photovoltaic power plants continues to expand, investors have placed great importance on the suitability assessment of site selection. In this study, we have developed a multi-level evaluation system and proposed an AHP–XGBoost–GIS comprehensive evaluation model for assessing site suitability in the Beijing–Tianjin–Hebei region. The findings revealed that approximately 48,800 km2 (21.59% of the total area) constituted a suitable area in this region, surpassing previous research results. Generally suitable areas accounted for the largest proportion at 35.89%. Suitable areas in Beijing and Tianjin were relatively limited, with most of them concentrated in Baoding City, Zhangjiakou City, and Chengde City on the northwest side of the "Yanshan-Taihang Mountains", where there are ample wastelands with gentle slopes conducive to constructing photovoltaic power plants southeast of the "Yanshan-Taihang Mountains", and solar radiation conditions were better. However, ecological and economic factors restricted suitable areas resulting in smaller coverage including cities such as Handan, Xingtai, Qinhuangdao, and Tangshan, among others. This study successfully overcame limitations associated with traditional subjective evaluation methods by providing decision support for regional energy allocation planning and spatial planning efforts, as well as environmental protection endeavors. [ABSTRACT FROM AUTHOR]

PURPOSE. Determination of prospects for the development of renewable energy sources in the Kyrgyz Republic by assessing their potential, current state and development in the Kyrgyz Republic, as well as analyzing the regulatory framework, opportunities and barriers to their development. METHODS. Anonymous surveys of specialists in the field of energy and renewable energy, systematic and integrated approaches, analysis and synthesis, induction and deduction were used. A survey of the expert community was also conducted in order to identify "bottlenecks" in the state management of renewable energy projects and find ways to solve them. OUTCOMES. Ways to improve the state policy and mechanisms for the development of renewable energy in the Kyrgyz Republic through the introduction of auctions, the expansion of the practice of public-private partnership (PPP), the solution of the issue of guaranteed state purchase of electricity from small and mediumsized producers at mutually attractive tariffs and terms, the issue of land allocation, zoning for projects for the deployment of photovoltaic solar and wind generating plants are proposed. [ABSTRACT FROM AUTHOR]

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics.

Abstract In Latin America, distribution networks are mostly radial, with high energy losses, representing about 16% of the region's electricity consumption. In Cuba, one of the alternatives that has been proposed to reduce these losses is the distribution of photovoltaic (PV) power plants of up to 3 MWp in them. This paper presents a new hybrid optimization algorithm to localize and size PV power plants to minimize energy losses in radial distribution networks. The method localizes the power plants using Empirical Discrete Metaheuristic in conjunction with a novel strategy that reduces the execution time of the algorithm. The sizes are found using the Coordinate Search Method, which performs one‐dimensional searches in the directions of the variables of the objective function. The algorithm was run on a 33‐bus IEEE test system in which up to five power plants were optimally installed, taking into account load variations at the distribution level and changes in the weather conditions under which the power plants operate. It should be noted that the execution times for each scenario did not exceed 30 s. The algorithm was validated by comparing it with other methods found in the literature and proved to be more efficient than them.

In Latin America, distribution networks are mostly radial, with high energy losses, representing about 16% of the region's electricity consumption. In Cuba, one of the alternatives that has been proposed to reduce these losses is the distribution of photovoltaic (PV) power plants of up to 3 MWp in them. This paper presents a new hybrid optimization algorithm to localize and size PV power plants to minimize energy losses in radial distribution networks. The method localizes the power plants using Empirical Discrete Metaheuristic in conjunction with a novel strategy that reduces the execution time of the algorithm. The sizes are found using the Coordinate Search Method, which performs one‐dimensional searches in the directions of the variables of the objective function. The algorithm was run on a 33‐bus IEEE test system in which up to five power plants were optimally installed, taking into account load variations at the distribution level and changes in the weather conditions under which the power plants operate. It should be noted that the execution times for each scenario did not exceed 30 s. The algorithm was validated by comparing it with other methods found in the literature and proved to be more efficient than them. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) generation has become the major type of solar energy utilization as the world's energy demand grows. However, the layout of large-scale public PV plants changes the original underlying conditions and has an impact on the local surface energy budget and distribution. Based on the ecohydrological model T&C, taking the fixed utility-scale PV plants as the typical object proposes a PV module, and the T&C-PV model is developed to simulate the impact of PV plants on the regional surface energy budget. Compared with the original T&C model, a substantially improvement of the simulation of energy flux in the PV plant paved underlying surface has been achieved in the T&C-PV model. This model is applied to PV power plants in Xinjiang Province, China, and the results show that the sensible heat flux increased by 12% and the latent heat flux decreased by 25%, with the percentage of transpiration decreasing from 90.1% to 74.5% and the percentage of evaporation increasing from 9.9% to 25.5% after the PV power plants are set up. • Developed a PV module to simulate the energy budget of PV panels. • Established a T&C-PV model based on the ecohydrological model T&C. • Evaluate the change of heat flux near surface under the PV panel. • Quantitively estimate the influence of the PV plant on energy balance. [ABSTRACT FROM AUTHOR]

Large-scale photovoltaic power plants located in highland mountainous areas are vulnerable to landslides due to extreme rainfall, posing a significant threat to the normal operation of photovoltaic power plants. However, limited research has been conducted on landslide risk assessment specifically tailored to large photovoltaic power plants, with most studies focusing on static assessments that lack long-term sustainability in risk assessment and prediction. In this paper, a dynamic study on landslide risk at a large photovoltaic power plant project under extreme rainfall conditions is conducted. Firstly, the factors in landslide susceptibility assessment based on typical landslide characteristics in the study area are selected and an assessment index database using mapping units to extract the relevant factors is established. Subsequently, the ANP-FBN model is employed to evaluate the landslide susceptibility of large photovoltaic power plant sites. Furthermore, an assessment index system for landslide hazard vulnerability is developed by considering population, economic, and material vulnerabilities, and the AHP method is adapted to assess landslides vulnerability in the study area. Finally, the landslide rainfall threshold with the susceptibility and vulnerability assessment results are coupled to achieve a dynamic assessment of landslide hazard risk at large photovoltaic power plant sites under extreme rainfall conditions. The findings highlight that the central valley and the eastern steep slope of the study area are the primary "high" and "very high" risk areas. Moreover, with the increase in rainfall duration, the risk level of landslide hazards in the study area also rises. [ABSTRACT FROM AUTHOR]

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model's predictions and actual future production values, demonstrating the model's efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems. [ABSTRACT FROM AUTHOR]

Copyright of EMERG: Energy. Environment. Efficiency. Resources. Globalization is the property of Romanian National Committee of World Energy Council 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.)

As the construction of photovoltaic power plants continues to expand, investors have placed great importance on the suitability assessment of site selection. In this study, we have developed a multi-level evaluation system and proposed an AHP–XGBoost–GIS comprehensive evaluation model for assessing site suitability in the Beijing–Tianjin–Hebei region. The findings revealed that approximately 48,800 km2 (21.59% of the total area) constituted a suitable area in this region, surpassing previous research results. Generally suitable areas accounted for the largest proportion at 35.89%. Suitable areas in Beijing and Tianjin were relatively limited, with most of them concentrated in Baoding City, Zhangjiakou City, and Chengde City on the northwest side of the “Yanshan-Taihang Mountains”, where there are ample wastelands with gentle slopes conducive to constructing photovoltaic power plants southeast of the “Yanshan-Taihang Mountains”, and solar radiation conditions were better. However, ecological and economic factors restricted suitable areas resulting in smaller coverage including cities such as Handan, Xingtai, Qinhuangdao, and Tangshan, among others. This study successfully overcame limitations associated with traditional subjective evaluation methods by providing decision support for regional energy allocation planning and spatial planning efforts, as well as environmental protection endeavors.

The amendment of the Law on Energy and the adoption of the new Law on Renewable Energy Sources enabled a sudden increase in the installed capacity of photovoltaic (PV) power plants in the Republic of Serbia in 2022. Thanks to the new modality called prosumers, interest in the construction of PV power plants has increased, both with households and with commercial entities. The general situation in the world, crises, pandemics and wars have caused a drastic increase in the price of energy products, and therefore electricity. To contribute to the reduction of the negative impact of climate change, it is an ideal opportunity to use the production of electricity from one's own power plant to compensate for an increased cost of energy. The most affordable and simplest is the construction of the PV power plant because solar energy reaches every roof. This paper describes the possibilities of building PV power plants in the status of prosumers from the technical and legal aspects. In addition to examples from practice, the current state of installed capacities of PV power plants in the Republic of Serbia in the status of prosumers is given.

The use of solar trackers can help increase the production and time period of electricity generation in photovoltaic power plants. Different types of trackers in terms of rotation mechanisms and sun tracking systems have been used in these types of power plants in recent years. In this article, a comparison has made between the electricity produced of fixed and tracking structures in a number of power plants which are located in different cities of Iran but in similar geographical locations. Following this, software modeling is used to evaluate various sun tracking scenarios in the design of sample power plants. Finally, a techno-economic analysis has been made to evaluate the decision-making regarding the construction of such power plants in Iran using this technology. Using east-west detectors has had a positive effect on increasing the production of power plants, especially in summer. Due to the higher initial costs of using this technology in the power plants, as well as the higher maintenance costs, the economization of the power plants' business plan is extremely dependent on other economic conditions governing the project. Using trackers alone cannot lead to a better situation in a project's lifetime. From the national point of view, if domestic companies can produce east-west single-axis tracking technology at an acceptable cost and provide related services in the long term, it would be beneficial for the power grid. One of the attractive proposals for the power plants under construction will be the use of this technology, because of additional electricity production in peak hours in summer. Annual experimental data from different tracking/fixed PV power plant have been used for the first time in this techno-economic investigations, they are validated with the simulation process, and the results have been predicted for a wide range of tracking scenarios. Techno-economic analyses of this type of power plant are an essential need for policy makers and investors in Iran's energy market. [ABSTRACT FROM AUTHOR]

For each photovoltaic power plant, it is extremely important to perform an analysis of its efficiency, as well as an analysis of all parameters that may affect efficiency. The electric energy of the photovoltaic system, which is delivered to the electric power system on a daily basis, is determined through the average daily insolation, the surface of the panel and the average efficiency value. One of the parameters that affect the conversion efficiency of a photovoltaic power plant is a decrease in the conversion efficiency due to an increase in panel temperature. In this paper an example is a real photovoltaic power plant with a nominal power of 50 kW, which is installed on the rooftop of the building of the Institute "Mihajlo Pupin", located in Zvezdara forest, Belgrade, Serbia. The correlation analysis of the estimated temperature of the photovoltaic panel was performed using two models and the measured temperature of the photovoltaic panel. The temperature of the photovoltaic panel was estimated using models, one of which does not take into account, and the other takes into account the influence of wind speed on the temperature of the panel. [ABSTRACT FROM AUTHOR]

To realize the green scenario, foreseen in the Energy Development Strategy in the Republic of North Macedonia until 2040, the existing coal-fired thermal power plants with an installed capacity of 800 MW should be successively closed in the coming period. For the same reason, the Republic of North Macedonia became a member of the PPCA Alliance (Powering Past Coal Alliance) and the first country from the Western Balkans that, together with Spain committed to abandon the production of electricity from coal by 2030. Hence, the problem that has been elaborated is very relevant because it deals with the continuation of the existence of MPC "Oslomej" through energy transition, that is, the replacement of electricity production from coal, with the production from solar energy. The analysis in this paper is focused and aims to address the importance of the further operation of MPC "Oslomej", with the construction and commissioning of the first photovoltaic plant PVPP Oslomej 1 of 10 MW. Afterward, MPC "Oslomej" will continue with the construction of PVPP Oslomej 2 of 10 MW and PVPP Oslomej 3 of 100 MW, which will completely replace the existing coal-fired thermal power plant TPP "Oslomej" of 120 MW. The construction of these photovoltaic power plants in the mine of the Oslomej plant, during the energy crisis is the first example in SouthEastern Europe, for which the European energy community reacted positively and expressed support to JSC ESM. [ABSTRACT FROM AUTHOR]

The aim of the considerations presented in the article was a stand-alone groundbased photovoltaic power plant. The article is devoted to the qualitative analysis of some lightning protection configurations. These types of constructions often require an individual look at the design and execution of lightning protection installations, which causes problems with the selection of optimal solutions. These problems relate primarily to the way the lightning rods are arranged to create protection zones, but also to the way they are attached: to the supporting structure for PV modules or as free-standing. Another problem raised in the article is the way how lightning current is discharged from rods to the ground and how it is dispersed there. Due to the vast area of such facilities and the requirements for electrical safety, it is necessary to consider and design a ground system with optimal electrical parameters, but also technical and economic ones. All these elements have their impact on the value of voltages induced in the electrical installation, which is also presented in the content of the article as the magnetic field distribution and calculation of induced voltages in an exemplary configuration. Finally, this article will compare described technical solutions encountered in selecting the best protection method for this type of photovoltaic installation.

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150 %– 170 % and 160 %– 180 % for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh. • A bidding strategy for over-install RES-BESS plants (e.g. , wind-BESS and PV-BESS) is proposed. • A scenario-based stochastic optimization scheme is developed to maximize the expected profits of producers in electricity markets. • The proposed scheme considers battery degradation and maximum export capacity constraints. • A financial analysis is performed to assess the profitability for over-installing hybrid RES plants. [ABSTRACT FROM AUTHOR]

Unlicensed installations of electrical energy generation from renewable energy sources continue increasingly in Turkey. Especially, the high rate of increase in electricity prices has enabled consumers to direct their investments toward renewable energy sources. In this study, techno-economic analyses of unlicensed grid-connected solar power generation plants, which were designed as both land and rooftop installations with the capacity of 1215 kWp/1000 kWe in different climatic conditions of seven cities in Turkey (Ağrı, Antalya, Balıkesir, İstanbul, İzmir, Konya and Samsun), were conducted. The technical part of the analyses was carried out using the PVsyst simulation software. Also, levelized cost of energy, net present value, internal rate of return, project and equity returns were calculated for all cities. In the economic analysis, 100% offsetting, 50% offsetting- 50% electricity sale, and 100% electricity sale options were used. For the annual electrical energy production, the highest value was obtained for the land installation in Antalya with 1988 MWh/year, and the lowest value was calculated for the rooftop installation in Samsun with 1234 MWh/year. For the system performance ratio, the highest and lowest values were found for the rooftop installation in Ağrı with 80.07% and the land installation in Antalya with 75.92%, respectively. The highest net present value and internal rate of return were obtained for Antalya by considering all scenarios. [ABSTRACT FROM AUTHOR]

Changeability of the energy input from renewable sources, as well as climate change, accelerating by the day, dictate the need to study the latest climate changes in the country in order to examine the possibilities of using electricity generating installations and systems based on renewable energy sources, as well as for agro–climatic purposes in this territory. The first part of the research presents the results of studying the dynamics of changes in weather and climatic conditions in the territory of the Republic of Uzbekistan, the Fergana Valley in particular, during the period of 2005–2021 by means of collecting and processing climatic data obtained from nine weather stations located in the valley and suitable for scientific research purposes. It was revealed that during the period of 2005–2021, the maximum change in the average annual temperature in the areas of Fergana oblast was 1.58°C and the number of days with an average ambient temperature above +25°C varied between 100 and 121 days; direct beam solar radiation increased significantly; there was also a sharp drop in wind speed and the number of days with dust storm and drifting dust; and the amount of precipitation in the region in recent years was significantly less than in 2005. Due to the fact that the level of warming in the valley affects the generation capacity of crystalline silicon photovoltaic modules (PVMs), in order to predict their production capacities with the least error, it is necessary to prepare the most accurate source data on climatic and actinometrical parameters for their performance evaluation and survey works in the applicable part of the region. [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.)

Industrial development, increased demand for energy, limited fossil fuels, and the prevention of environmental damage have led to the use of renewable energy to produce power. The combined system includes renewable wind and solar energy systems or other renewable energy. The hybrid system has been widely used in recent decades to provide energy. In this research, an optimal integrated system has been used to generate electricity. The proposed system includes wind, hydropower, and photovoltaic systems. to estimate the maximum power generation and reduce fluctuations in electricity generation, three proposed algorithms have been used and three common solutions have been applied to solve the optimization problem. The performance of the combined system in different weather conditions has also been assessed. The results showed that among the three proposed algorithms, the model improved the Barnacles Mating Optimizer Algorithm (IBMO) has the best distribution and convergence. Among the three common solutions, a common solution has the most logical solution to the optimization problem. And among the different hydrological conditions, the highest electricity generation is related to the wet season. the most important role in the hybrid system related the hydropower so that despite the reduction of electricity produced in the wind and photovoltaic power plants, the hydropower plant compensates for the drop in production of these systems and causes the production flow to continue.

In this article, a simulation model of an autonomous PVdiesel power supply (PDPS) system with direct current is considered. The autonomous PDPS is implemented in the Simulink programming environment of the Matlab software package. The system includes: a photovoltaic power station (PPS), a diesel generator (DG), an energy storage system (ESS), a remote consumer as well as auxiliary converters. The PDPS model makes it possible to analyze the energy balance between generation, consumption, and the energy exchange available from the ESS connected via a DC bus (DCB). With the help of the proposed model, it is possible to analyze and optimize the technical and economic indicators of DC power plants with the priority of the PPS contribution: reducing the consumption of diesel fuel and carbonation of the air reducing the cost of 1 kWh for the remote consumer and controlling the switching in the power plants as per logical conditions. The obtained technical and economic indicators of the PDPS using direct current are superior to similar systems based on alternating current due to an increase in the contribution of the PPS to the energy balance. As a result of experiments with changing input conditions, i.e. annual changes in insolation and temperature at different latitudes, the authors justify technical, economic, and environmental criteria for the effectiveness of decision-making and recommendations for the design and operation of PDPS power plants. Intelligent algorithms for effective control of PV-diesel power systems based on DC are developed and suggested.

Large-scale photovoltaic power plants located in highland mountainous areas are vulnerable to landslides due to extreme rainfall, posing a significant threat to the normal operation of photovoltaic power plants. However, limited research has been conducted on landslide risk assessment specifically tailored to large photovoltaic power plants, with most studies focusing on static assessments that lack long-term sustainability in risk assessment and prediction. In this paper, a dynamic study on landslide risk at a large photovoltaic power plant project under extreme rainfall conditions is conducted. Firstly, the factors in landslide susceptibility assessment based on typical landslide characteristics in the study area are selected and an assessment index database using mapping units to extract the relevant factors is established. Subsequently, the ANP-FBN model is employed to evaluate the landslide susceptibility of large photovoltaic power plant sites. Furthermore, an assessment index system for landslide hazard vulnerability is developed by considering population, economic, and material vulnerabilities, and the AHP method is adapted to assess landslides vulnerability in the study area. Finally, the landslide rainfall threshold with the susceptibility and vulnerability assessment results are coupled to achieve a dynamic assessment of landslide hazard risk at large photovoltaic power plant sites under extreme rainfall conditions. The findings highlight that the central valley and the eastern steep slope of the study area are the primary “high” and “very high” risk areas. Moreover, with the increase in rainfall duration, the risk level of landslide hazards in the study area also rises.

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model’s predictions and actual future production values, demonstrating the model’s efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems.

Photovoltaic (PV) systems are dependent on solar irradiation and environmental temperature to achieve their best performance. One of the challenges in the photovoltaic industry is performing maintenance as soon as a system is not working at its full generation capacity. The lack of a proper maintenance schedule affects power generation performance and can also decrease the lifetime of photovoltaic modules. Regarding the impact of environmental variables on the performance of PV systems, research has shown that soiling is the third most common reason for power loss in photovoltaic power plants, after solar irradiance and environmental temperature. This paper proposes a new statistical predictor for forecasting PV power generation by measuring environmental variables and the estimated mass particles (soiling) on the PV system. Our proposal was based on the fit of a nonlinear mixed-effects model, according to a log-logistic function. Two advantages of this approach are that it assumes a nonlinear relationship between the generated power and the environmental conditions (solar irradiance and the presence of suspended particulates) and that random errors may be correlated since the power generation measurements are recorded longitudinally. We evaluated the model using a dataset comprising environmental variables and power samples that were collected from October 2019 to April 2020 in a PV power plant in mid-west Brazil. The fitted model presented a maximum mean squared error (MSE) of 0.0032 and a linear coefficient correlation between the predicted and observed values of 0.9997 . The estimated average daily loss due to soiling was 1.4 % . [ABSTRACT FROM AUTHOR]

Saharan sand dust events present notable challenges to solar energy systems, particularly in regions with prevalent solar photovoltaic (PV) deployment. Experimental measurements and simulations using PVsyst software were conducted on two identical PV power plants—one cleaned and the other exposed to sandstorms. Empirical findings, supported by simulations, indicate that sand dust accumulation has negative effects on energy and output power, with a soiling rate of 0.25 %/day. Monthly power generation, energy generation, PV efficiency, and performance ratio decreased with increasing soiling percentages. Simulation results show that in July, for stationary PV power stations, monthly energy generation decreased from 661 kWh to 633 kWh and 576 kWh, with soiling percentages of 5 % and 15 %, respectively. Similarly, monthly power generation for two-axis solar tracking systems decreased from 984 kWh to 946 kWh and 865 kWh under the same soiling conditions. Simulation results indicate that in July, for fixed PV systems, the monthly PV efficiency could have declined from 10.96 % to 10.55 % and 9.55 % under soiling percentages of 5 % and 15 %, respectively. Similarly, for two-axis solar tracking systems, the monthly PV efficiency was found to decrease from 10.87 % to 10.45 % and 9.56 % under the same soiling conditions. In July, simulation results indicated that for fixed PV systems, the monthly performance ratio could have decreased from 73.53 % to 70.43 % and 63.98 % under soiling percentages of 5 % and 15 %, respectively. Similarly, the monthly performance ratio for two-axis solar tracking systems was found to decrease from 73.18 % to 70.34 % and 64.34 % under the same soiling conditions. The study highlights the importance of understanding soiling effects for investors, PV engineers, and researchers to develop mitigation strategies and maintenance protocols for PV power plants in dust-prone regions to sustain high performance. • Experimental measurements and simulations using PVsyst software were conducted on two identical PV power plants—one cleaned and the other exposed to sandstorms. Empirical findings, supported by simulations, indicate that sand dust accumulation has negative effects on energy and output power, with a soiling rate of 0.25%/day. • Monthly power generation, energy generation, PV efficiency, and performance ratio decreased with increasing soiling percentages. • Simulation results show that in July, for stationary PV power stations, monthly energy generation decreased from 661 kWh to 633 kWh and 576 kWh, with soiling percentages of 5% and 15%, respectively. Similarly, monthly power generation for two-axis solar tracking systems decreased from 984 kWh to 946 kWh and 865 kWh under the same soiling conditions. • Simulation results indicate that in July, for fixed PV systems, the monthly PV efficiency could have declined from 10.96% to 10.55% and 9.55% under soiling percentages of 5% and 15%, respectively. Similarly, for two-axis solar tracking systems, the monthly PV efficiency was found to decrease from 10.87% to 10.45% and 9.56% under the same soiling conditions. • In July, simulation results indicated that for fixed PV systems, the monthly performance ratio could have decreased from 73.53% to 70.43% and 63.98% under soiling percentages of 5% and 15%, respectively. Similarly, the monthly performance ratio for two-axis solar tracking systems was found to decrease from 73.18% to 70.34% and 64.34% under the same soiling conditions. [ABSTRACT FROM AUTHOR]

Regions with limited space for constructing renewable power generation systems need to maximize electricity generation by optimizing the operational efficiency of existing plants and selecting an optimal location for the new construction of PV power plants with favorable weather conditions and surrounding environment. Utilizing monthly input and output data, including four inputs (solar irradiation, temperature, number of modules, and photovoltaic (PV) array rated capacity) and one output (electricity generation) from utility-scale PV power plants, meta-frontier data envelopment analysis was employed in this study to identify factors contributing to power generation inefficiency. Additionally, a Monte Carlo experiment analyzed the impact of solar irradiation uncertainty on power generation efficiency. The findings revealed that the average power generation inefficiency during the study period was 0.445, primarily attributable to seasonal and technical factors. Since these factors are difficult to control once a power plant is in operation, it is important to select an optimal site for power plants by considering meteorological and geographical data. Moreover, the results indicated a significant gap between observed and simulated values of power generation efficiency, arising from variations in weather conditions, power plant site area, and the data used in the research. Employing PV modules with higher electricity output levels can boost the DC/AC ratio, thereby increasing power generation, enhancing efficiency, and contributing to a stable power supply, thus reducing daily and seasonal fluctuations in power generation. [Display omitted] • This work aimed to improve power generation efficiency of photovoltaic power plants. • First work to study impact of meteorological factor on power generation efficiency. • Uncertainties in photovoltaic power generation efficiency analysis clarified. • Impact of seasonality/technical factors on power generation efficiency quantified. • Results can contribute to improving new/existing renewable power generation systems. [ABSTRACT FROM AUTHOR]

• Study of PV power plant with quasi-Z-source cascaded H-bridge multilevel inverter under failure. • Control system to provide a suitable DC voltage at the input of the VSI and ensure the grid power delivery. • Control tested from simulation results under different operating conditions and HIL experimental results. • Correct response of the developed control strategy with failure in qZSI module. At the present time, quasi-Z-source cascaded H-bridge multilevel inverters (qZS-CHBMLI) have gained attention in photovoltaic (PV) applications, due to their advantages over traditional multilevel inverters. They allow single-stage power conversion of a PV power plant by independently adjusting the DC voltage in each module; thus, each PV array operates at the maximum power. This advantage permits dispensing with a DC/DC boost converter and reduces the number of modules required. This paper develops a novel control strategy for a qZS-CHBMLI connected to a single-phase system that guarantees proper operation when a fault occurs in a quasi-impedance source inverter (qZSI). The control strategy is employed in a grid-connected qZS-CHBMLI with three modules, each one with a quasi-impedance network, a 4.8 kW PV array and a voltage source inverter (VSI), with H-bridge topology. The purpose of the control system is to deliver a suitable DC voltage at the input of the VSI and ensure the grid power requirements after a fault in a module. The proposed control system is evaluated through a 40 s simulation in MATLAB/Simulink® in two cases: A) analysis of the system behaviour when there is a variation in the operation limit equation; and B) evaluation of the control strategy under dissimilar operating conditions. In addition, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is employed to validate the results. The results confirmed the correct response of the applied strategy with the failure of a single module. [ABSTRACT FROM AUTHOR]

Summary: Solar energy power prediction is a vital process for designing and operating solar power plants to evaluate the system performance and reduce the cost of grid management as it decreases the grid imbalance for a more stable grid. This study introduces a new power prediction approach to enhance the power prediction quality by combining different solar models. The study investigates the performance of 4.5 MWp solar field located at the California Polytechnic State University at San Luis Obispo, California, United States to validate this new approach. The system is real‐time monitored for 1 year to study its performance. A representative identical model for the actual farm is then designed and built on a solar performance modeling software to validate the software. The actual energy generation of the field is compared with the predicted energy. The model is optimized by selecting different transposition and decomposition models to achieve the most accurate energy prediction. After that, the model is used in a computational framework to predict the energy injected into the grid for photovoltaic solar farms in Egypt based on the new approach methodology. Egypt is considered a solar belt country. Following the validation, a pilot model is used to predict energy generation for the selected locations in Egypt. Egypt's case study is then used as a pilot project to optimize some design parameters to study its effect and the suitability of the power generation in Egypt's weather. The results show that the relative error using the new approach for the whole year when using the proposed approach of combining the solar models is 2.9%. The performance ratio of the actual farm is 79% for the whole year, and the total energy produced is 10.92 GWh/year. The power prediction for the Egyptian sites concludes that 14 GWh/year energy potential can be achieved from the Red Sea location from the suggested model and the most suitable modules are polycrystalline. Furthermore, predicting a 13% increase in power generation due to using tracking technology compared with the usage of polycrystalline with fixed mounts. [ABSTRACT FROM AUTHOR]

Photovoltaic power plants have non-linear voltage-current characteristic, with specific maximum power point, which depends on operating conditions, viz. irradiation and temperature. In targeting the maximum power, it is by far known that the photovoltaic arrays have to operate at the maximum power point despite unpredicted weather changes. For this reason the controllers of all photovoltaic power electronic converters employ some method for maximum power point tracking. This paper makes an emphasis on model predictive controller as a control method for controlling the maximum power point tracking through the utilization of the well-known algorithm namely the Perturb and Observe technique. Further, during the advanced stages of this research study, the model will compare the results obtained for tracking the maximum power point from model predictive controller and a PID-controller as they are integrated Perturb and Observe algorithm. The obtained results will verify that the adaptive PID-controller Perturb and Observe algorithm has limitation for tracking the precise MPP during the transient insulation conditions. As compared to the proposed adaptive/modified model predictive controller for Perturb and Observe algorithm we illustrate that by adopting this method we will get to establish more accurate and efficient results of the obtained power in any dynamic environmental conditions: advantages as on regulation time (six times under the accepted experimental conditions) and by the number of fluctuations.

This paper presents a multi-objective dynamic economic dispatch model with renewable obligation requirements. The two objective functions that are presented in this paper aim to increase the level of renewable energy sources in the grid while minimising the total operating cost and respecting the spinning reserves required to maintain continuity of supply. The proposed model incorporates thermal power plants, photovoltaic and wind power plants into the grid. The paper presents a Pareto optimal solution which is a compromise between maximising the renewable energy source generation while minimising the operating costs. A renewable obligation policy is implemented in the proposed model to ensure that renewable energy source generators are utilised and any failure to attain the required renewable obligation is penalised in line with the renewable obligation framework. The proposed model is modified into a single objective optimisation problem and numerical tests are performed on the modified IEEE 24 RTS system and IEEE 118 bus system to test the effectiveness of the model. A comparative study evaluates the impact of the proposed model on the traditional dynamic economic dispatch in terms of the achieved renewable energy source penetration level and economic performance. The numerical simulations show that the proposed model is robust and can attain high renewable energy source penetration levels.

The use of solar trackers can help increase the production and time period of electricity generation in photovoltaic power plants. Different types of trackers in terms of rotation mechanisms and sun tracking systems have been used in these types of power plants in recent years. In this article, a comparison has made between the electricity produced of fixed and tracking structures in a number of power plants which are located in different cities of Iran but in similar geographical locations. Following this, software modeling is used to evaluate various sun tracking scenarios in the design of sample power plants. Finally, a techno-economic analysis has been made to evaluate the decision-making regarding the construction of such power plants in Iran using this technology. Using east-west detectors has had a positive effect on increasing the production of power plants, especially in summer. Due to the higher initial costs of using this technology in the power plants, as well as the higher maintenance costs, the economization of the power plants’ business plan is extremely dependent on other economic conditions governing the project. Using trackers alone cannot lead to a better situation in a project’s lifetime. From the national point of view, if domestic companies can produce east-west single-axis tracking technology at an acceptable cost and provide related services in the long term, it would be beneficial for the power grid. One of the attractive proposals for the power plants under construction will be the use of this technology, because of additional electricity production in peak hours in summer. Annual experimental data from different tracking/fixed PV power plant have been used for the first time in this techno-economic investigations, they are validated with the simulation process, and the results have been predicted for a wide range of tracking scenarios. Techno-economic analyses of this type of power plant are an essential need for policy makers and investors in Iran’s energy market.

Many countries have initiated strategies to produce energy from alternative sources, reduce the economic dependence on conventional sources, and limit dioxide carbon emissions. Algeria is one of the countries producing conventional energy sources (petrol and gas), the growing of fossils energy consumption in Algeria reduces its oil product exports. This paper focuses on a comprehensive view of the global electrical energy situation and exactly the renewable energy sector in Algeria with specific details of the electrical network named Pole InSalih-Adrar-Timimoun (PIAT). Also, three renewable energy centers were revised completely technically, Also, an investigation of the Algerian renewable energy plan in horizons 2030 was attained in this paper. [ABSTRACT FROM AUTHOR]

The fact that traditional energy sources have limited reserves and have a negative impact on the environment increases the demand for renewable energy sources. Environmental, economic, and sustainability concerns have led researchers, investors, and policy makers to seek the potential of renewable energy sources. Suitable site selection for new-generation renewable resources is vital in large-scale projects. In this paper, geographic information systems and multicriteria decision-making (MCDM) methods were integrated to exploit and construct the best location for solar PV power plants in Kayseri, Turkey. Three main, twelve subcriteria, and their indicators related to the study area were determined. The rank-based (i.e., rank sum, rank reciprocal weights, and rank order centroid weights) and analytical hierarchical process (AHP) MCDM methods were used to determine the weights of the criteria. Thirty-three existing solar PV power plants were used to verify the success of MCDM methods. Four MCDM methods yielded effective results according to the proposed criteria, and most of the existing solar PV power plants match the convenient regions on the suitability map provided by geographic information systems-based rank reciprocal method. In addition, according to the experimental study, Sarız, Tomarza, and Incesu districts of Kayseri were the most suitable sites. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Many countries have initiated strategies to produce energy from alternative sources, reduce the economic dependence on conventional sources, and limit dioxide carbon emissions. Algeria is one of the countries producing conventional energy sources (petrol and gas), the growing of fossils energy consumption in Algeria reduces its oil product exports. This paper focuses on a comprehensive view of the global electrical energy situation and exactly the renewable energy sector in Algeria with specific details of the electrical network named Pole InSalih-Adrar-Timimoun (PIAT). Also, three renewable energy centers were revised completely technically, Also, an investigation of the Algerian renewable energy plan in horizons 2030 was attained in this paper.