Efficiency Of Solar Power Plants Research Articles

Spatio-temporal Kriging for spatial irradiance estimation with short-term forecasting in a thermosolar power plant

This article proposes a method to improve the efficiency of solar power plants by estimating and forecasting the spatial distribution of direct normal irradiance (DNI) using a sensor network and anemometer data. For this purpose, the proposed approach employs spatio-temporal kriging with an anisotropic spatio-temporal variogram that depends on wind speed to accurately estimate the distribution of DNI in real-time, making it useful for short-term forecast and nowcast of DNI. Finally, the method is validated using synthetic data from varying sky conditions, outperforming another state-of-the-art technique.

DEVELOPMENT TRENDS OF SOLAR POWER ENGINEERING BASED ON THE MATERIALS OF THE SCIENTIFIC AND PRACTICAL CONFERENCE «RENEWABLE ENERGY AND ENERGY EFFICIENCY IN THE 21

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.

Review on Design and Fabrication of Solar Panel Cleaning Mechanism

Today energy demands are increasing sharply therefore as a Renewable source of power, solar energy has an important role in producing the electricity direct from sunlight. The surface of solar panel in solar power plants becomes dirty due to natural factors such as dust and bird droppings. This project aims at increasing the efficiency of solar power plants by solving the problem of accumulation of dust on the surface of solar panel which leads to reduction in plant output and overall plant efficiency. It proposes to develop a Solar Panel Cleaning System which could remove the accumulated dust on its surface on a regular basis and maintain the solar power plant output.[8]

Solar power forecasting model as a renewable generation source on virtual power plants

This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.

Optimizing solar power plant efficiency through advanced analytical framework and comparative analysis

As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience.

Heat transfer in a fluidized bed tubular solar receiver. On-sun experimental investigation

Using particles as heat transfer fluid in a solar receiver is an attractive way to increase the global efficiency of solar power plants. In the case of the particle-in-tube solar receiver concept, the fluidized particles circulate vertically in tubes thanks to a controlled pressure gradient and an additional air injection. Experiments are conducted with olivine particles in a one-tube mock-up at the 1 MW CNRS solar furnace (France). The tube of high aspect ratio (more than 3 m height over 48 mm internal diameter) is irradiated along a 1-m height with several solar flux configurations that correspond to realistic operation conditions of a solar thermal power plant. The novelties of this paper lie in extending the operating parameters compared to earlier studies and in linking the fluidization regimes to wall heat transfer. Slugging, turbulent fluidization and fast fluidization regimes are detected within the receiver tube. It is shown that the system can tolerate high solar flux densities, up to 800 kW/m2. The system proves to be highly controllable and flexible with respect to particle mass flux variation and transient operations. Particle temperature increase ranges from 100 to 650 °C depending on the particle mass flux. Maximum thermal efficiency and particle outlet temperature of respectively ∼ 60 % and 680 °C are obtained, proving that this technology can be combined with highly efficient thermodynamic cycles. Global wall-to-particle heat transfer coefficient varies strongly with particle mass flux. It reaches a quasi-plateau above 15 kg/(m2s) with a mean value of 1000 ± 200 W/(m2K) and peaks data up to 1500 W/(m2K). A dimensionless heat transfer index is derived to account for the effects of the particle mass flux on the experimental results. It highlights that both increasing the temperature and working in the turbulent fluidization regime result in the increase of the heat transfer between the tube walls and the particles.

Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression

This study investigates the surface parameters and environmental factors affecting the energy production of a 500 kWp photovoltaic (PV) solar power plant in Igdir province. Using both the PV panel characteristics and the weather conditions specific to the power plant location, a total of 7 detailed features were included. The estimation of the power plant efficiency, a novel contribution not found in previous studies, is also a major focus. The performance evaluation of different models, including feed-forward neural networks and multiple linear regression, demonstrates the effectiveness of artificial neural networks in capturing the complex relationships between features and efficiency despite limited data availability. Principal Component Analysis (PCA) was used to reduce feature dimensions, showing that even with a reduced feature set, accurate efficiency prediction is still achievable. Prediction using PCA is one of the novelties of the paper. The effects of solar irradiation, module power, and module temperature on power plant efficiency are revealed. The results provide valuable insights for optimizing energy investments in the Igdir region and highlight the potential of artificial neural networks in energy forecasting, demonstrating their suitability for capturing complex patterns in solar power plant efficiency.

Four-Phase Interleaved Boost Converter for Maximum Power Extraction in PV System

The use of renewable energy is increasing, thus affecting the rapid use of DC-DC converter technology. The use of a conventional boost converter has a weakness, namely high current ripple. This input current ripple will enter the voltage source, so that the switching noise will spread to other circuits. This problem can be overcome by using a multiphase converter topology that can suppress current ripple. But the conventional multiphase topology has large power losses, because the value of the inductor used tends to be large. Ripple currents will also reduce the efficiency of solar power plants. This study aims to design a four-phase interleaved boost converter that is applied to PV (photovoltaic) with maximum power extraction. The topology method of this circuit is to assemble a conventional boost converter in parallel up to 4 levels, so that it can suppress the input current ripple. This topology modification series also uses the interleaved scaling method in order to reduce heat distribution on each switch. The test method in this study uses a standard boost converter circuit as a comparison, and observes its characteristics and performance through simulation. The converter system is also operated as Maximum Power Point Tracking (MPPT) using the PSO and P&O algorithm controls as a comparison. Based on the simulation results, the MPPT tracking speed has the same speed, which is 2 ms, but the speed to achieve ripple stability in the standard circuit and four-phase interleaved boost converter is 4.5 ms and 6.1 ms, respectively.

WSe2 nanowires-based nanofluids for concentrating solar power

Tungsten selenide belongs to the family of inorganic compounds denominated transition metal dichalcogenides (TMDCs). There is emerging interest in these compounds in the field of optoelectronics, catalysis, sensing or energy storage, among others. Most works focus on the use of these materials in their 2D form but there is scarce research on the study of TMDCs nanomaterials with one-dimensional morphology. In this work, we explore the thermophysical properties of nanofluids based on 1D-WSe2 nanostructures with the aim of studying the feasibility of these nanofluids as heat transfer fluids in concentrating solar power plants. In this respect, nanofluids with a high heat transfer rate could increase the thermal efficiency of solar power plants, which would reduce the energy dependence on fossil fuels. Nanofluids of 0.02 wt%, 0.05 wt% and 0.10 wt% WSe2 concentrations have been prepared by the two-step method considering a thermal fluid used in solar power plants as the base fluid. The results of extinction coefficient evolution, ζ potential and particle size in suspension show a high colloidal stability over time of the prepared nanofluids mainly because of the high aspect ratio of the 1D-WSe2 nanomaterial. Additionally, the one-dimensionality and length of the synthesized nanowires favors the transport of heat in controlled directions, obtaining increases in thermal conductivity with respect to the base fluid of up to 16.8% in the highest concentration nanofluid. Improvements in isobaric specific heat of up to 15.7% and heat transfer of up to 20.8% compared to the base fluid have also been found. The results of this paper provide evidence that the presence of WSe2 nanowires induces increases in the thermal properties of the fluid commonly used in concentrating solar power plants without inducing agglomeration or sedimentation problems. Therefore, the nanofluids based on 1D-WSe2 nanostructures prepared in this work have a high potential to be used as heat transfer fluids in concentrating solar power plants based on parabolic trough collectors.

РАЗРАБОТКА И ИССЛЕДОВАНИЕ СПОСОБОВ ПОВЫШЕНИЯ ЭЛЕКТРОЭНЕРГЕТИЧЕСКОЙ ЭФФЕКТИВНОСТИ СОЛНЕЧНЫХ ЭЛЕКТРОСТАНЦИЙ В УСЛОВИЯХ СЕВЕРА

The article is devoted to the issues of development and research results of ways to improve the electric power efficiency of solar power plants in the conditions of the North. Field studies of this work were carried out during 2020-2022 in the central part of Yakutia. In the course of the research, key photoelectric, electrical and technical and economic regularities, statistical methods for processing experimental data, including the method of calculating the student’s t-criterion, were applied. Algorithms have been developed for conducting field studies to idеntify the nature of the influence of climatic factors of the North on the functioning of solar power plants. The indicators of the influence of climatic factors of the North on the operation of solar power plants are obtained. А model for assessing the energy potential of solar power plants has been developed, taking into account the nature of the influence of climatic factors of the North, the reliability of calculations of which increases by 5.61% and approaches the actual indicators of electricity generation from existing solar power plants. As a result of the research, ways have been developed to reduce the negative impact of the climatic factors of the North on the functioning of solar power plants, allowing to increase their production by 15 ... 25%. Positive indicators of the effect of the introduction of the developed methods on the parameters of operational reliability, technical, economic and environmental indicators have been obtained. The results of the research can be applied when updating the guidelines for improving the electric power efficiency of mini- and microgrid facilities operating with the use of solar power plants in the conditions of the North.

Tracker for solar power plants

The article investigates a device for tracking the position of the sun during the day - a tracker for solar power plants. The practice of using solar trackers as a device to increase the efficiency of solar power plants is considered. The relevance of this development in Ukraine and prospects for its development are determined. Methods and principles of increasing the efficiency of solar energy production, expediency of using trackers for solar power plants are analyzed. The aim of the article is to present the stages of development of a biaxial solar tracker and the algorithm of the controlling the angle of inclination of solar panels placed on a moving platform, relative to the obtained data on the position of the sun. The article presents a tracker for solar power plants, its structure and algorithm. It is stated that the principle of operation is to analyze the current position of the sun and automatically set the movable platform with solar panels in the most effective position.

Innovative high-speed method for detecting hotspots in high-density solar panels by machine vision

Abstract The occurrence of hotspots in photovoltaic panels is one of the most common problems of solar power plants, which reduces the output power of photovoltaic arrays and can also cause irreparable damage to the solar cells. There are several ways to identify hotspots, including using custom datasets using thermographic camera images, which will be later used to teach YOLO and Faster R-CNN computer vision algorithms. In practice, it is observed that the YOLO algorithm is many times faster than the Faster R-CNN in high-density solar panels. Therefore, the applied method is the safest choice for automatic hotspot detection in large-scale photovoltaic power plants to improve overall efficiency. In this paper, by comparing the performance of methods such as Faster R-CNN with YOLO, we concluded that the YOLO algorithm has far better advantages in terms of quality of detection, and speed. Therefore, this factor makes the use of YOLO significantly helps to speed up the troubleshooting of solar modules caused by hotspots, and this factor improves the efficiency of solar power plants in the long run. Meanwhile, in the studies for this paper, the results extracted by Python have been optimized as an algorithm to be used for hotspot detection.

Solar energy development in households: ways to improve state policy in Ukraine and Latvia

ABSTRACT The paper investigates state policy to promote solar energy in Latvia’s and Ukraine’s households. Comparing different approaches to stimulating sectors’ development in both countries, the article evaluates the economic efficiency of solar power plants implementation based on the Levelized Cost of Electricity method and investment projects payback periods. The calculations were performed for solar power plants installed capacities ranging from 1 to 30 kW. The results revealed the fundamental differences in solar energy advancement due to the existing support mechanisms in the countries. While high feed-in tariff rates contribute to the rapid deployment of solar power plants in Ukraine’s, Latvia’s net-metering system hinders solar energy development in households. The research forms recommendations for improving policy by adjusting Ukrainian feed-in tariffs to trends in reducing solar energy costs, introducing solar energy in the Mandatory Procurement Component for Latvia, and applying preferential public financing to solar power plants construction in both states. Highlights State policy to promote solar energy in Ukraine’s and Latvia’s homes is analyzed The economic efficiency of small solar power plants is estimated for both countries Feed-in tariff in Ukraine ensures a quick payback period for solar power projects Net metering in Latvia does not provide investment attractiveness of solar plants Both countries need energy policy changes for balanced solar power development

Analysis of the solar power plant efficiency installed in the premises of a hospital — Case of the Pediatric Charles De Gaulle of Ouagadougou

A 50 kWp photovoltaic (PV) power plant installed in a hospital had its efficiencies analyzed in this work. Two emergency services of the hospital have been chosen to be fed with produced energy. In the present paper, easy sizing up means were used. The PV system is synchronized to the electrical grid to ensure the solar energy production and the batteries charging in priority. In case of electric grid perturbation, the batteries provide with the stored energy. For the studied system, the month of May is the better energy production month among the eight studied months with almost perfect efficiency in terms of meeting the needs of the two connected services. However, this month is one of the heat wave months in the sahelian strip. The batteries energy is very requested to ensure the energy supply in the two emergency services of the hospital. The best operation month is May. However to find the optimum operation, it well be better to install off-grid system. The temperature and local dust are also degradation factors to take into account. This paper shows that the PV off-grid can be used to improve the resilient of the population in areas with high security challenges. The failure of the PV plant is estimated at 0%. Indeed, once the SONABEL grid is desynchronized, the grid inverters use the energy in batteries to continue the injection. The environmental constraints such as low illumination, high temperature, dust, etc. must be added in the perturbation factors of the solar energy production.

A theoretical study of molten carbonate fuel cell combined with a solar power plant and Cu–Cl thermochemical cycle based on techno-economic analysis

A novel power and hydrogen coproduction system is designed and analyzed from energetic and economic point of view. Power is simultaneously produced from parabolic trough collector power plant and molten carbonate fuel cell whereas hydrogen is generated in a three-steps Cu–Cl thermochemical cycle. The key component of the system is the molten carbonate fuel cell that provides heat to others (Cu–Cl thermochemical cycle and steam accumulator). A mathematic model is developed for energetic and economic analyses. A parametric study is performed to assess the impact of some parameters on the system performance. From calculations, it is deduced that electric energy from fuel cell, solar plant and output hydrogen mass are respectively 578 GWh, 25 GWh and 306 tons. The overall energy efficiency of the proposed plants is 46.80 % and its LCOE is 7.64 c€/kWh. The use of MCFC waste heat allows increasing the solar power plant efficiency by 2.15 % and reducing the annual hydrogen consumption by 3 %. Parametric analysis shows that the amount of heat recovery impacts the energy efficiency of fuel cell and Cu–Cl cycle. Also, current density is a key parameter that influences the system efficiency.

Solar Cell Performance Test against Load Variations

Solar cell is a converter of light energy into electrical energy. This study aims to examine the characteristics of the solar cell to load variations. The research was conducted at the Semarang State Polytechnic Energy Conversion Lab. The solar cell used in the research of the monocrystalline solar cell type KTENG CP-520S. The research method is carried out by measuring the value of solar intensity, voltage and electric current from the solar cell, then calculating the value of the power and efficiency of the solar cell. The greater the intensity of sunlight, the better the characteristics of the solar cell with a note that the solar cell surface temperature must be stable at 250C. The results showed that the highest input power of the solar cell was 5293.69 watts at a lamp load of 115 watts at 833.6 W/m2 of radiation during sunny weather. The highest value of the output power of the solar cell is 191.52 watts when the lamp is loaded with 190 watts of radiation at 739.4 W/m2 during sunny weather. While the highest value of the load output power is 212.43 watts when the lamp is loaded with 200 watts of radiation at 724.4 W/m2 when the weather is sunny. The highest efficiency of the solar cell is 4.13% when the lamp is loaded with 200 watts of radiation at 724.4 W/m2 when the weather is sunny. And the highest value of Solar Power Plant efficiency is 4.61% at a 200 watt lamp loading at 724.4 W/m2 radiation when the weather is sunny.

Lightning Protection, Cost Analysis and Improved Efficiency of Solar Power Plant for Irrigation System

The constraints in the path of sustainable, cost-effective, and efficient photovoltaic power supply to the irrigation system in remote areas are addressed in this work. The intrinsic thermal losses in the PV system due to high working temperature and shading losses that are caused by dirt are mitigated through water cleaning mechanisms. Moreover, the protection against lightning strikes and surges is assimilated in the system to ensure the durability of the PV system. Lastly, cost analysis of 0.4 MW PV plant for the Area of 7444.69 m2 has been performed by the Homer Pro, and comparison is made with the same size of a Hydro power plant to estimate the economic feasibility of power generation for the purpose of irrigation through the pump house. The water-cooling mechanism resulted in the gain of one volt per panel of 260 W, which is a significant improvement with regard to collective PV plant generation. As the water cleaning mechanism for dust removal is accompanied with the cooling process, it results in the two volts rise per panel. Additionally, a cost analysis of 0.4 MW PV system provided a significant budget saving estimating USD ~2 million as compared to that of a Hydel power plant of the same size.

Improving the energy characteristics of a solar photoelectric station of spherical and cylindrical surface shapes for the power supply of remote rural areas and agricultural enterprises

One of the actual tasks of generating electricity is to increase the energy efficiency of solar photoelectric stations (SPS) in uneven lighting. The purpose of the work is to study the energy characteristics of the SPS under uneven lighting to improve the energy efficiency of solar power plants, including those for agro–industrial enterprises located and operating in remote rural areas, on sloping lands, mountainous and foothill terrain, where the efficiency of the SPS can be higher than on the plain due to a higher level of illumination. The methods of mathematical analysis with application of geometric optics, photoelectric conversion methods and construction physics in solar engineering are used. Analytical calculations were carried out to clarify the design parameters of the SPS that are made in the form of a sphere and a cylinder. The article presents the results of theoretical studies of the electrical characteristics of SPS made in the form of a sphere and a cylinder under uneven illumination of solar photoelectric modules (SPM). The relationship of electrical power and the number of SPS in the SPS is determined by the methods of mathematical statistics. As a result of studies of the electrical characteristics of SPS in uneven lighting, the following results were obtained: for SPS in the form of a sphere, small-area SPS have energy efficiency 32% more than large-area SPS; for SPS in the form of a cylinder of small-area SPS, energy efficiency increases by 19% compared to large-area SPS. The relationship between electrical power and the number of SPS was determined using the coefficient of determination, which is equal to: for a spherical surface - 36.2%, for a cylindrical one - 26.6%.

Experimental studies of factors of influence on solar generation power plants

Intensive development of grid-connected solar power plants leads to significant problems of management and reliability of the country's electrical systems, due to the stochastic nature of weather phenomena, such as clouds, rainfall, fog and others. Therefore, forecasting the generation of solar power plants is an important issue as improving the reliability of the country's electrical system and improving the economic efficiency of solar power plants by reducing fines for imbalances in electricity. Experimental studies have been conducted to determine the influence of natural factors, such as the angle of the sun over the horizon, temperature of photovoltaic modules, solar radiation, contamination of module surfaces, precipitation and fog on the generation of 2.4 MW solar power plant located in Kharkiv region of Ukraine. The study found a clear relationship between the active power of the solar station, solar radiation, ultraviolet index and the surface temperature of the photovoltaic module. The nature of changes in solar radiation during 12 months of observations was established, the minimum solar radiation of 200 W/m2 was recorded in December, the maximum solar radiation of about 1200 W/m2 was observed in June. Based on statistical data, the dependences for determining the difference between the surface temperature of solar panels and the air temperature on a clear day depending on the hourly generation of the solar power plant are obtained. It is established that the generation of a solar power plant at 100 % cloud cover is from 20 to 28 % compared to the generation on clear days. Contamination of the surface of photovoltaic modules and their cleaning depending on the intensity and amount of precipitation has a significant impact on the generation of a solar power plant. Fog has a significant effect on lasing, which reduces lasing by 50 %. Graphs of specific generation (ratio of hourly generation of solar power plant, in terms of solar panel temperature 25 °C, to the average hourly angle of the sun above the horizon), which will help determine the generation of solar power plant for each hour during the day. To increase the accuracy of generation forecasting, the specific generation graphs are divided into two intervals: the first and second half of the day. Analysis of specific power graphs suggests that the specific generation varies throughout the year due to different air temperatures, air vapor content and the thickness of the layer of air through which the sun's rays pass, depending on the location of the sun above the horizon.

Efficient utilization of waste heat from molten carbonate fuel cell in parabolic trough power plant for electricity and hydrogen coproduction

Direct steam generating parabolic trough power plant is an important technology to match future electric energy demand. One of the problems related to its emergence is energy storage. Solar-to-hydrogen is a promising technology for solar energy storage. Electrolysis is among the most processes of hydrogen production recently investigated. High temperature steam electrolysis is a clean process to efficiently produce hydrogen. In this paper, steam electrolysis process using solar energy is used to produce hydrogen. A heat recovery steam generator generates high temperature steam thanks to the molten carbonate fuel cell's waste heat. The analytical study investigates the energy efficiency of solar power plant, molten carbonate fuel cell and electrolyser. The impact of waste heat utilization on electricity and hydrogen generation is analysed. The results of calculations done with MATLAB software show that fuel cell produces 7.73 MWth of thermal energy at design conditions. 73.37 tonnes of hydrogen and 14.26 GWh of electricity are yearly produced. The annual energy efficiency of electrolyser is 70% while the annual mean electric efficiency of solar power plant is 18.30%.The proposed configuration based on the yearly electricity production and hydrogen generation has presented a good performance.