Large-scale Solar Power Plants Research Articles

Temperature homogenization control of parabolic trough solar collector field based on hydraulic calculation and extended Kalman filter

Parabolic Trough Collector fields serve as the heat-absorbing components in large-scale commercial solar trough power plants. However, they frequently encounter challenges related to uneven spatial distribution of solar irradiation and heat transfer fluid flow. These disparities often lead to temperature inhomogeneity across the field, thereby jeopardizing both the safety and economic efficiency of the collector field. To address these issues, this study first introduces a proportional-integral and feedforward based flow control strategy designed to stabilize the fluid outlet temperature in the collector field. Subsequently, a flow allocation scheme aimed at achieving temperature uniformity across individual collector loops is presented. This scheme relies on the effective irradiation estimates for each collector loop, calculated using the extended Kalman filter algorithm and a suggested hydraulic model. Simulation results demonstrate that these control strategies not only stabilize the outlet temperature of the collector field at the desired set value but also significantly diminish temperature variances among individual collector loops, with reduced computational overhead. The maximum temperature difference between loops can be reduced from 126.3 °C to 12.7 °C under uneven radiation intensity, ranging from 563 W/m2 to 885 W/m2, by the proposed temperature homogenization control strategy.

Lean Photovoltaik

Abstract The expansion of renewable energies poses a significant challenge worldwide. Numerous national and international agreements and laws promote and demand a much stronger expansion than before. A significant contribution is expected from the increased use of photovoltaics (PV). Large scale solar power plants cover vast areas and consist of hundreds of thousands of modules. With increasing quantities, the potential to make installation more efficient using factory operation methods also increases. However, established approaches such as Lean Production Systems (LPS) cannot be transferred from the factory to the PV construction site without adaptations, due to the different preconditions. In a previous article, the challenges in the installation of PV power plants and the potential for the implementation of LPS have been analysed based on a study of the current state of production efficiency. For this purpose, solar projects by Sunstall Inc., USA, were accompanied. In this article, the implementation and the results of an experimental application of LPS on these solar projects are presented.

GEOSPATIAL ANALYSIS OF THE POTENTIAL OF THE TERRITORIES OF UKRAINE FOR THE LOCATION OF SOLAR POWER PLANTS BASED ON SATELLITE DATA

Climate change necessitates the relevance of renewable energy utilization worldwide. The Green Deal defines the energy development policy in Europe until 2030. This challenge holds particular significance for Ukraine in the context of post-war energy infrastructure recovery. Therefore, an important issue is the analysis of the suitability of Ukrainian territories for the installation of large-scale solar power plants (solar farms) and the optimization of their placement. This research aims to determine the suitability of Ukrainian territories for solar power plant placement using satellite data on climate and terrain characteristics. Among the factors determining the suitability of the territory for solar farms, the greatest impact lies in climatic indicators, including data on the total global horizontal solar irradiation (GHI), accumulated temperature above 25 °C at a height of 2 meters, average annual wind speed, and map of accumulated annual precipitation from the ERA5-Land dataset. In this study, terrain maps containing information on elevations, slopes, and terrain shading from the Shuttle Radar Topography Mission (SRTM) project were also used to determine the suitability of the territories. The suitability of the territories is determined through geospatial analysis using weighted sums. Based on the research results, a suitability map was constructed, depicting the distribution of zones with different suitability coefficients (ranging from 0 to 1). It was found that a significant portion of Ukraine’s territory is favorable for the placement of solar power plants. Over 48 % of the country has moderate suitability values (0.3…0.4). The obtained suitability map was used to analyze the optimal placement of already constructed major solar power plants in Ukraine. Data from Wikimapia was utilized to determine the locations of these energy facilities. Overall, all the analyzed large-scale solar power plants in Ukraine were situated in optimal territories. The research also revealed that certain regions, such as Odesa, Poltava, Kharkiv, Zaporizhzhia, Dnipro, Donetsk, and Luhansk, have good suitability values (0.3…0.4) but are not fully utilized. These areas have significant potential for the future construction of powerful and productive solar power plants

Floating Photovoltaic Potential in the Rawa Biru Area of South Papua Province

National energy policy and renewable energy development policy towards 23% in 2025, the first target is to increase solar energy capacity as a priority where development includes rooftop solar power plants, large scale solar power plants, and floating solar power plants. The legal basis for the development of renewable energy is the policies of the central government and regional governments through regulations made. South Papua with the potential of Rawa Biru which is one of the sources of clean water in Merauke. Rawa Biru with a watershed area (DAS) of 4,791.671 km2 and an actual water body area of ​​95 ha. Simulation is done using Google Earth and Solargis software. Potential of floating PV in Rawa Biru with data maps, solar azimuth and PV configurations. Area of 95 ha floating PV potential 53,25 MWp, generating potential 79,93 GWh/year and area of 8 ha floating PV potential 2,14 MWp, generating potential 3,24 GWh/year.

Efficiency Modeling using the Mirror Surface of Heliostats in a Solar Power Tower

Analyzing the geographical location and climatic conditions of Turkmenistan, we conclude that solar energy is a priority among all renewable sources since the duration of sunny days in the country is from 270 to 320 days a year. Development, creation of large-scale solar power plants and use in remote desert regions of Turkmenistan is an urgent problem. The article considers the possibilities of creating a solar power plant based on a tower-type layout. The modes of the optical system in the natural and climatic conditions of Turkmenistan are calculated using a mathematical model, the coefficient of efficiency of using the mirror surface of the installation and the distribution of the level of instantaneous local values are determined. Figure 1 shows the movement of the Sun in the sky, which varies depending on the time of year. The results of the study show that the maximum increase in the energy efficiency coefficient can be achieved with the location of solar panels perpendicular to solar radiation with the dense placement of heliostats on the north side of the tower, then energy efficiency will increase by 25-40% in summer and 10-15% in winter.

Implementation of String Inverters for 50 MW Large Scale Solar Power Plant in Malaysia

The growth of large-scale solar photovoltaic (LSSPV) power plant site development in Malaysia has increased the demand for the reliability and potentiality of the system in achieving efficient monitoring, operation, and maintenance for the plants. A configuration of string inverters is applied in a 50 MW large-scale solar photovoltaic installation because of its flexibility and scalable technological advancements. This installation demonstrates that the plant can optimise the power output and monitor the performance of the system up to the string level with a cost-efficient implementation. The output from each photovoltaic (PV) string provides a guarantee of better maximum power point tracking (MPPT) capability. Instead of having shorter DC wires compared to the central inverter, this system offers the potentiality of monitoring the solar PV module at a string level, which also affords scalability for future expansion. The tests conducted verify that the string inverter configuration serves as a reliable outcome for grid code test (GCT) requirements. Furthermore, the string inverters provide a potential approach to achieving maximum power generation in different climate conditions, thus reducing the need for manpower in operation and maintenance activities. The string system offers a feasible implementation of effective configurations and decarbonisation for LSSPV in Malaysia without frequent shutdowns of plant generation due to defects or damage to PV equipment and modules.

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

The Cleaning Effect of Photovoltaic Modules According to Precipitation in the Operation Stage of a Large-Scale Solar Power Plant

A large-scale solar power plant costs a lot of money in the early stage of development and is greatly affected by the natural environment. Therefore, efficient operation is very important. The purpose of this study is to analyze the cleaning effect of photovoltaic modules according to precipitation during the operation stage of a large-scale solar power plant. The first analysis compared ‘average power generation on sunny days under standard cloudiness from after precipitation to the next precipitation’ and ‘average daily power generation per quarter’ and confirmed that precipitation had an effect on increasing power generation by 26%. The second analysis compared ‘average power generation on sunny days under the standard cloudiness from after precipitation to the next precipitation’ and ‘average daily power generation on a clear day just before precipitation’. It was confirmed that the average power generation efficiency of the entire sample increased by 4.8% on average after precipitation than before precipitation. This study quantitatively analyzed the cleaning effect of photovoltaic modules by precipitation through actual power generation data of large-scale solar power plants. This study has sufficient value in establishing an operation manual for decision-making on the appropriate input cost for cleaning photovoltaic modules and improvement of power generation efficiency.

Potential benefits and risks of solar photovoltaic power plants on arid and semi-arid ecosystems: an assessment of soil microbial and plant communities.

Exponential increase in photovoltaic installations arouses concerns regarding the impacts of large-scale solar power plants on dryland ecosystems. While the effects of photovoltaic panels on soil moisture content and plant biomass in arid ecosystems have been recognized, little is known about their influence on soil microbial communities. Here, we employed a combination of quantitative PCR, high-throughput sequencing, and soil property analysis to investigate the responses of soil microbial communities to solar panel installation. We also report on the responses of plant communities within the same solar farm. Our findings showed that soil microbial communities responded differently to the shading and precipitation-alternation effects of the photovoltaic panels in an arid ecosystem. By redirecting rainwater to the lower side, photovoltaic panels stimulated vegetation biomass and soil total organic carbon content in the middle and in front of the panels, positively contributing to carbon storage. The shade provided by the panels promoted the co-occurrence of soil microbes but inhibited the abundance of 16S rRNA gene in the soil. Increase in precipitation reduced 18S rRNA gene abundance, whereas decrease in precipitation led to decline in plant aboveground biomass, soil prokaryotic community alpha diversity, and dehydrogenase activity under the panels. These findings highlight the crucial role of precipitation in maintaining plant and soil microbial diversities in dryland ecosystems and are essential for estimating the potential risks of large-scale solar power plants on local and global climate change in the long term.

Technical-economic feasibility analysis of a large-scale parabolic trough collectors solar power plant in Brazil

This study aims to present the results of the application of a computational tool developed to perform a technical-economic feasibility analysis of large-scale Parabolic Trough Collectors (PTC) solar power plants in Brazil. The gross revenue from cash flow is obtained through an energy trading model for the Regulated Contracting Environment of the Brazilian Electricity Market. The economic viability analysis uses the Net Present Value, Modified Internal Rate of Return, Discounted Payback, and Levelized Cost of Energy indicators. The parametric structural optimization method is used to optimize the technical parameters of the plant. A case study was conducted to analyse the feasibility of implementing a 100 MW PTC in the five regions of Brazil. The results indicated that the best region in Brazil to receive this technology is the northeastern region. However, the project is economically unfeasible in all cities analysed. Therefore, a sensitivity analysis of the main parameters that affect the project's net present value is carried out to evaluate the impact of the economic parameters on the economic viability of an optimized PTC plant located in the city with the best technical performance. An optimistic scenario was evaluated considering the system's response to the parameters investigated in the sensitivity analysis. For this scenario, the results pointed to the project's economic viability.

Economic analysis of a large scale solar updraft tower power plant

This research article provides an economic analysis of a large-scale solar updraft tower power plant (SUTPP) having 100 MW capacity and installed in Udat, Rajasthan, India, with position coordinates of 27° 35′ and 72° 43′ for meteorological conditions. A cost model developed for optimized parametric values, received from thermal and dimensional optimization of previous study, to analyse the optimized cost of electricity generation and revenue analysis with the consideration of electricity selling, carbon credits, and the levelized electricity cost. The results of the analysis suggest that the proposed plant has a higher capital and operating cost than photovoltaic and wind energy, but it also has a lower levelized cost of electricity generation. As, for a fixed power output, the optimized cost of electricity generation is inversely proportional to Rcollector and Rtower, and obtained an optimum value between ₹9.16 to ₹10.57 for power plants ranging from 100 MW to 200 MW. The results also show that the cost of construction of such a power plant largely depends on plant configuration and can lie between ₹4.5cr. to ₹12cr. per MW, which indicates that a large-scale SUTPP can be installed economically. Although the optimum values of optimized cost of electricity generation came with larger dimensional values of the power plant, that also create challenges during the construction and operation of the power plant. Based on these findings, the authors conclude that, with proper subsidies and support, a large-scale SUTPP can be considered a promising renewable energy technology for power generation.

Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models

The advanced development of large-scale solar power plants (LSSPs) has made it necessary to improve accurate forecasting models for the output of solar energy. Solar energy is still hampered by the lack of predictability in its output, which remains a major hurdle in the solar industry. This paper focuses on triple deep learning (DL) techniques such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolutional Neural Network- Long-Short Term Memory CNN-LSTM to address this problem. These techniques are utilized in solar energy variables (SEVs) such as power generation (MWh), soiling loss (%), and performance ratio (PR %) to determine the optimal forecast model. The novelty of this research is that it is the first time that important solar system parameters such as PR and soiling loss have been studied to predict a feasible forecast model using a different DL scheme. The SEVs real-time dataset is procured from the largest solar plant in Pakistan, titled “Quaid-e-Azam Solar Park” (QASP). The main significance of the study is that ANN, RNN, and CNN-LSTM-based models were developed in the DL process through feature generation, data scaling, training, and testing steps to predict the optimal model. The prediction values were compared with the solar plant's actual values over the last 7 years, and then a comparison was made to predict the future forecast trend over the next 20 years. The aim and goal is to develop three models to investigate the accurate results of time-series forecasting on the SEVs dataset, as well as to evaluate the performance measure errors to determine the appropriate model. Based on the forecasting/prediction graphic and error results, it was demonstrated that the CNN-LSTM hybrid model is a more capable forecasting model for the output of power generation and PR values and a more accurate predictor of the future trend over the ANN and RNN models. However, the ANN model is slightly better performed in predicting of soling loss value than CNN-SLTM and RNN. Thus, the CNN-LSTM hybrid model is an optimal model for SEVs, which can guarantee a variety of LSSPs of similar nature in the following time-series forecasting investigations which shows key findings and its importance for the solar industrial forecast issue.

Spatial modelling the location choice of large-scale solar photovoltaic power plants: Application of interpretable machine learning techniques and the national inventory

The optimum site selection of solar photovoltaics power plant across a given geographic space is usually assessed by using the geographic information system based multi-criteria decision making methods with various restriction criteria, while such evaluation results vary with criteria weights and are difficult to be validated in real life practices. To address this issue, this paper uses a national inventory dataset of large-scale solar photovoltaics installations (the land coverage area ≥ 1 hm2) to investigate the spatial location choices of solar power plants with the aids of interpretable machine learning techniques. A total of 21 geospatial conditioning factors of solar energy development are considered. The location choices of solar photovoltaics installation are then modeled with the multi-Layer perceptron, random forest, extreme gradient boosting models for each land cover type (e.g. cropland, forest, grassland, and barren). The SHapley additive explanation and variable importance measure methods are adopted to identify key criteria and their influences on the solar photovoltaics installation location selection. Results indicate that the random forest model presented the better performance among three machine learning models. The relative importance of conditioning factors revealed that the vegetation index and distance to power grid were always the most important predictors of solar photovoltaics installation location. Furthermore, topographical factors and transportation convenience may have a moderate impact on the spatial distribution of solar photovoltaics power stations. Unexpectedly, most of resources endowment and socio-economic factors play a negligible role in determining the optimal siting of solar power farms. Simulated solar photovoltaics installations probability maps illustrated that the most suitable regions account for 4.6 % of China’s total land area. The evidence-based method proposed in this research can not only help identify suitable solar photovoltaics farm locations in terms of various decision-making criterion, but also provide a robust planning tool for sustainable development of solar energy sources.

LVRT and Reactive Power/Voltage Support of Utility-Scale PV Power Plants during Disturbance Conditions

This paper proposes a control technique for a large-scale grid-connected photovoltaic (PV) plant that maintains the connection of an inverter to the grid voltage under different types of faults, while injecting a reactive power to accommodate the required grid connection. This control strategy is suggested to improve the low-voltage ride-through (LVRT) capability of grid-connected PV power generation plants. A 20 MW solar PV power plant is modeled and simulated using Matlab/Simulink. The power plant is composed of 10 parallel groups of arrays with a power rating of 2 MWp. The solar PV arrays are connected to a medium-voltage side-rated 22 KV to the utility grid. A dynamic analysis of the grid-connected large-scale solar PV power plant is introduced. This analysis is accomplished in order to determine the impact of three-phase short-circuits at the point of common-coupling (PCC), where the solar PV power station is connected to ensure a practical voltage level by injecting active and reactive power. The reactive power support allows for faster restoration of voltage values at the PCC. When subjected to transient disturbances, the stability of the studied system relies on both the type of the disturbance and the initial operating situation. The disturbance may be either small, resulting from electrical load changes, or large, such as from a transmission line short-circuit (fault) and significant generator loss.

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.

Technical Analysis of a Large-Scale Solar Updraft Tower Power Plant

This study investigates the possibility of applying a large-scale solar updraft tower power plant in India with local ground conditions as an environmentally friendly and economically viable energy source. A reference model Solar Updraft Tower Power Plant (SUTPP) is constructed to examine the influence of the most prominent plant dimensional parameters, including collector radius (RCollector), tower height (HTower), and tower radius (RTower) with dimensional limits and intervals on the power output of the SUTPP. Udat, Rajasthan, India, is used as a reference location for meteorological conditions to evaluate SUTPP power output equations for a ranging power output, with position coordinates of 27°35′ and 72°43′. Multiple simulations for the objective function are carried out, and the outcomes are compared to the optimized dimensions of each set of plants. The model examines the effect of variation in ambient, plant geometry, and material conditions on power output and analyzes efficiency and power output for optimizing configuration. There exists no definitive approach to determining the proper correlation between the geometrical parameters of a SUTPP with optimized power output. For a fixed power output, the tower radius (RTower) serves as the most influencing dimensional parameter in SUTPP performance. A change in tower height (HTower) has a detrimental impact on SUTPP output and performance. An initial increase in collector radius (RCollector) has a positive influence on SUTPP performance; however, this effect reduces as collector radius (RCollector) increases.

Potential Assessment of 80 kW Floating Solar Photovoltaic System (FSPV) at Kaptai lake, Bangladesh

Based on the massive amount of electricity generated, the world's lifestyle and economy have greatly expanded. However, due to the scarcity of fossil fuels, the provision of power is under pressure in this country. Renewable energy provides an exception at this point, and solar energy—among all the renewable energy sources—plays a prominent role because it is environmentally friendly and indestructible. The cost and accessibility of land, however, poses a significant obstacle to the construction of land-based, large-scale solar power plants in Bangladesh. In a riverine nation like Bangladesh, a floating solar photovoltaic system offers a solution with a lot more possibilities. This research will outline the potential for floating solar photovoltaic systems in Bangladesh's Kaptai lake area and discuss feasible investigations with location prospect, FSPV costs, project revenue, environmental aspects and further research opportunities. The potential investigation for the establishment of FSPV based on financial parameters will be validated in this paper using RET-screen energy management software.

Solar desalination chimneys: Investigation on the feasibility of integrating solar chimneys with humidification–dehumidification systems

The feasibility of integrating a solar chimney with a humidification–dehumidification desalination system resulting in a solar desalination chimney is examined in this work using combined validated mathematical models. In the proposed system, the solar energy is used to create buoyant hot air that flows through a humidification and dehumidification system. A small turbine can be housed in the chimney to provide energy for pumping water if there is sufficient airflow. This integrated solar desalination chimney uses solar irradiation as an energy source for buoyant air motion and might not require additional energy input. Data available in the literature for experimental solar chimney setups were examined to evaluate the potential of this novel desalination approach which utilizes solar energy. A validated humidification–dehumidification model is used to investigate desalination capacity. Based on the integrated model, a large-scale solar chimney power plant in Manzanares, as an example, could operate efficiently with a turbine providing power to operate the desalination system and provide freshwater for over 800 households. Moreover, numerical simulation shows that small-scale integrated solar chimney desalination systems could be placed on the roof of a residential building and produce about 600 liters of freshwater daily to satisfy freshwater of a household.

Revealing 3D Morphological Evolution and Reaction Kinetics of Metals and Alloys in Molten Salts Via Synchrotron X-Ray Nano-Tomography and Multimodal Studies

The use of molten salts for large-scale solar concentrated power plants and molten salt reactors has been driving the research to better understand how metals and alloys interact with the molten salt. As the metals may undergo morphological, chemical, and structural change in molten salt environments, it is critical to understand the fundamental mechanisms in these changes. In this work, we will present how we utilized synchrotron X-ray nano-tomography to better understand the 3D morphological evolution of Ni, Cr, and their alloys in molten salt.The effects of temperature and additives in the salt on the morphological evolution will be discussed. At the higher temperature, a characteristic bicontinuous structure can form from molten salt dealloying a binary alloy. [1] This contrasts to the intergranular corrosion found in the same system reacted at a lower temperature. [2] Different additives in the salt were also found to alter the morphological changes of the alloys and can create planar corrosion, percolation dealloying, or redeposition.To complement the morphological studies by X-ray nano-tomography, a suite of X-ray and electron microscopy analyses were also carried out to better understand the chemical and structural (both short-and long-range ordering) evolution. Taking it as a multimodal approach, we will discuss how we couple the analysis from synchrotron operando X-ray absorption spectroscopy, diffraction, and imaging, as well as the multiscale imaging studies from both X-ray and electron microscopy. This work was supported as part of the Molten Salts in Extreme Environments (MSEE) Energy Frontier Research Center (EFRC), funded by the U.S. Department of Energy, Office of Science.

Performance Analysis of Energy Production of Large-Scale Solar Plants Based on Artificial Intelligence (Machine Learning) Technique

Due to the continual fusion reaction, the sun generates tremendous energy. This solar energy is freely available and can be extracted by installing a large-scale solar power plant. Therefore, such PV solar plants are key contributors to cutting the energy deficit in remote areas. This study focused on predicting a 10-year performance analysis of a large-scale solar power plant by using 1 year of real-time data from the Quaid-e-Azam Solar Park (QASP) situated in Bahawalpur, Pakistan. For the purpose of prediction, the ARIMA model was developed using Python, which is one of the best tools in machine learning. Since ARIMA is a statistical technique for prediction, by using the developed model through Python, we predicted the values of the performance ratio (PR), production amount (MWh), and plan of array (POA) of the solar plant for the next 10 years using 1 year of real-time data. This machine learning prediction technique is very effective and efficient, compared with other traditional prediction and forecasting techniques, for estimating the performance of the solar power plant and the status of the solar power plant in the long-term future. The forecasting/prediction results acquired from the process show that power production during the next ten years increases to approximately 400 MW and that POA will grow from 6.8 to 7.8 W/m2. However, a decline occurred in the performance ratio, which decreased from 76.7% to 73%. Based on these results, the ARIMA model for predicting solar power generation is effective and can be utilized for any solar power plant.