Solar Photovoltaic Power Plant Research Articles

Performance Ratio and Loss Analysis for a Grid-Connected Solar Photovoltaic System: Case of the 7MW Plant in Malbaza, Niger

The use of solar energy in sunny countries is an efficient way to overcome the energy shortage. The interest of this energy is not only economic but also environmental, as it emits few greenhouse gases. Niger, a vast landlocked country in the Sahel, is characterized by an average sunshine duration of 8.5 hours per day and an estimated average level of sunshine of around 5 to 7 kW/m2 per day. However, the rate of access to electricity in Niger remains very low. To address this problem, a 7MW solar photovoltaic power plant has been built by the State of Niger in the town of Malbaza. It is composed of monocrystalline photovoltaic panels and injects its energy into the national grid. The objective of this study is to compare the performance ratio determined by measurements with that obtained by simulation under PVsyst in order to evaluate its efficiency. The study is based on annual energy production data, recorded at the site, from 1 January 2021 to 31 December 2021. The annual average values of the global irradiation of the site, the energy produced by the system and the energy injected into the grid are respectively 249.86kWh/m², 1225.25MWh and 972.62MWh; those of the ambient temperature and the performance ratio (PR) are respectively 29.78°C and 0.795. The global losses caused by meteorological phenomena and those linked to the technologies of the various components of the system are evaluated at 20.10%.

Design of an IoT platform for data analytics based fault detection and classification in solar PV power plants using CFKC and ODENN

New policies are commenced all over the globe to diminish the use of fossil fuels, which gives rise to the augmented utilization of solar energy (SE). The photovoltaic (PV) system’s performance is extremely environmental variables reliant. Long-range transmission of SE is incompetent as well as complex to carry in the PV system. It can be affected by disparate sorts of faults, which cause severe energy loss all through the system operation. Thus, it is vital to incessantly monitor the solar PV (SPV) system to detect as well classify the faults by preventing energy losses. The IoT applications in SE production engage sensor devices that are fixed to the generation, and transmission, together with distribution equipment. These devices assist in monitoring the operation of the SPV power plant (SPVPP) system remotely in real-time. Presenting a new algorithm that can perform fault detection and classification in a PV system to a higher level of accuracy is the major contribution of this work. Thus, this work designs as well as develops an IoT platform for carrying out analytical tasks that can analyze data generated as of IoT operating systems to detect as well as classify faults in the SPVPP. Initially, the data collected from the dataset is pre-processed in which data duplication is performed using Hadoop distributed file system (HDFS) and then the fault is detected from the pre-processed data using the cosine function based k-means clustering (CFKC) technique in the SPV system. Finally, the obtained fault data is fed into the optimized deep learning centered ENN (ODENN) method which classifies the faults. The proposed techniques detect as well as classify the faults effectively that are experimentally proved by means of comparing them with the prevailing techniques, namely ENN, ANN and SVM, along with KNN in terms of some quality measures. The obtained results for ODENN showed an accuracy of 98.99%, specificity of 97.6%, and a sensitivity of 97.02%.

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.

Climate change mitigation with clean energy: a case study on the potential of solar photovoltaic power plants in eastern Iran

Human health and subsistence have always been, directly and indirectly, affected by climate change events. Although the general trend of temperature in the Late Holocene has been toward cooling, due to the growing population and intense human activities over the past 150 years, the global temperature has risen. To mitigate climate change, the widespread use of renewable energy and the reduction of fossil fuel utilization are feasible solutions. The largest source of renewable energy is solar radiation, and Iran is one of the countries with high solar energy potential. In this study, technical research is done on the potential of solar photovoltaic (PV) power plant installment in Lut Desert, located in eastern Iran, to provide all electricity consumption of the country. This region receives the maximum solar radiation per year. In the proposed power plant, 305 WLG solar panels and 60 kW SMA solar inverters are applied. The modeling of this project has been done in PVsyst software with the annual temperature data of the Lut region. The results show that this project requires 140 solar PV power plants with a 1 GW capacity in an area of 2100 km2.

Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications.

Application of 30 MWp grid-connected solar photovoltaic power plant for Djibouti Vision 2035

Nowadays, like most of African countries, Djibouti is facing challenging energy access. The country was suffering a shortage of electricity for a long period until the government of Djibouti signed an agreement with the Ethiopia electric power cooperation (EEPCO) company in 2011. Thereby, the electricity consumption of the country is mainly from imported fossil fuels and the interconnection project. In order to realize Djibouti Vision 2035, the Republic of Djibouti signed an agreement with an Emirati company (AMEA) to build the first solar photovoltaic power plant in Grand Bara. In this paper, sizing, and simulation of the 30 MWp grid-connected solar photovoltaic power plant will be done using PVsyst 7.2 software. A 400 W bifacial monocrystalline panel and 160 kW string inverters are used in this study. The simulation results from PVsyst software are to acquire the system production, the performance ratio, and the various power losses such as losses due to irradiance level. The average global horizontal irradiance of the site is 2317.2 kWh/m 2 while the average ambient temperature is 25.75° C. About 59.6 GWh of electricity power is injected into the grid with an average performance ratio of 83.21% in the year. This study is an example in terms of solar energy application in Africa and also its contributions to the socio-economic development of African countries will be observed in the near future.

Iskustva iz izgradnje solarnih fotonaponskih elektrana na stanicama za snabdevanje gorivom u svojstvu kupca-proizvođača

According to the current regulations, the construction of solar photovoltaic power plants in the Republic of Serbia is a demanding process, particularly when installing them on particular objects that carry a higher risk. The paper will present the experience of building solar photovoltaic power plants with a power of up to 50 kW at 15 petrol stations owned by NIS J.S.C. Novi Sad and implemented as a prosumer. The challenges that had to be overcome were: finding an original solution for the configuration of a power plant without changing the existing structure and affecting the external appearance of the facility according to corporate branding standard, optimizing the capacity of the power plant according to the consumption of the object, optimizing costs, building near danger zones and other issues related to work safety both during the execution of works and during the period of exploitation. The paper also reviews the prospect of simplifying the procedures for connecting to the electricity distribution system based on the experience gained.NIS J.S.C. has begun investment in construction of solar PV plants at additional 30 petrol stations based on gained experience.

Study of the Impact of Grid Disconnections on the Production of a Photovoltaic Solar Power Plant: Case of Diamniadio Power Plant

Today, renewable energy projects connected to the interconnected network, with powers of the order of tens of megawatts, are more and more numerous in sub-Saharan Africa. And financing these investments requires a reliable amortization schedule. In the context of photovoltaic systems connected to the interconnected electricity grid, the quintessence of damping is the amount of energy injected into the grid. Thus it is fundamental to know the parameters of this network and their variation. This paper presents an evaluation of the impact of power grid disturbances on the performance of a solar PV plant under real conditions. The CICAD photovoltaic solar plant, connected to the Senelec distribution network, with an installed capacity of 2 MWp is the study setting. An energy audit of the plant is carried out. Then the percentage of each loss is determined: voltage drops, module degradation, inverter efficiency. The duration of each disconnection is measured and recorded daily. The corresponding quantity of lost energy is thus calculated from meteorological data (irradiation, temperature, wind speed, illumination) recorded by the measurement unit in one-minute steps. The observation period is three months. The total duration of disconnections related to the instability of the electrical network during the study period is 46.7 hours. The amount of energy lost is estimated at 22.6 MWh. This represents 2.4% of the actual calculated production.

Optimal Location to Use Solar Energy in an Urban Situation

This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location. The test state’s Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province’s study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable. The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.

Transient Stability and Active Protection of Power Systems With Grid-Forming PV Power Plants

Photovoltaic (PV) power plants with grid-forming technology must withstand severe disturbances and remain operational. To address this challenge, this paper sets forth a grid-forming strategy for PV solar power plants so that they can ride through power system faults. This capability is accomplished by leveraging two-axis proportional-integral regulators with anti-windup functionality. This paper also demonstrates that fluctuations of solar irradiance can cause significant dc-link voltage variations and loss of synchronism of grid-forming PV plants. Hence, we develop an active dc-link protection method which depends on estimation of solar irradiance. The contributions of this paper are demonstrated via positive-sequence simulations of modified versions of the WSCC 9- and IEEE 39-bus grids.

Experimental Evaluation of the Performance Degradation of a Solar PV Plant Operating in a Sahelian Climate

Niger, a Sahelian country, is known for its extreme climatic conditions and its wealth of solar deposits. In Niger, the average duration of sunshine is 8.5 hours per day and its average level is estimated at about 5 to 7 kW/m² per day. However, the rate of access to electricity in Niger remains very low. To address this problem, a 7MW solar photovoltaic power plant has been built by the authorities in the town of Malbaza (13°58.54 N and 13°58.54 E). It has a capacity of 7 MW and is composed of monocrystalline photovoltaic solar panels. In this study, we propose a method for analysing the degradation of a solar photovoltaic power plant under its operating conditions. The study will help to assess the performance and efficiency of PV systems installed in this geographical area. For this study, we use annual energy production data, recorded at the site, from 2019 to 2021. The method is based on the performance ratio values measured during these three years of operation. An hourly IR energy production index was introduced to study the degradation and reliability of the system. The performance ratio values obtained from the measurements in 2019, 2020 and 2021 are 73.22, 72.73 and 70.84 respectively; the calculated hourly energy production index values are 0.921, 0.914 and 0.891 respectively. The PVsyst software was used to estimate the value of the performance ratio. The value obtained is 79.50%. Thus, a degradation of 1% per year over the three years of operation is estimated. Finally, a comparison was made with other studies.

Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda

Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.

A Review of Dust Deposition Mechanism and Self-Cleaning Methods for Solar Photovoltaic Modules

Large-scale solar photovoltaic (PV) power plants tend to be set in desert areas, which enjoy high irradiation and large spaces. However, due to frequent sandstorms, large amounts of contaminants and dirt are suspended in the air and deposited on photovoltaic modules, which greatly decreases the power efficiency and service life. To clean PV to improve efficiency, many methods were proposed. It was found that the application of the self-cleaning coating on PV modules can effectively reduce dust deposition and improve the efficiency of PV. This paper reviews the dust deposition mechanism on photovoltaic modules, classifies the very recent dust removal methods with a critical review, especially focusing on the mechanisms of super-hydrophobic and super-hydrophilic coatings, to serve as a reference for researchers and PV designers, and presents the current state of knowledge of the aspects mentioned above to promote sustainable improvement in PV efficiency. It was found that the behaviors of dust on photovoltaic modules are mainly deposition, rebound, and resuspension. Particles with a diameter of 1–100 μm are most easily deposited on photovoltaic modules. The use of self-cleaning coatings, especially super-hydrophobic coatings, is beneficial to the rebound and resuspension of particles. The research gaps and development prospects of self-cleaning coatings are also discussed in this paper.

Performance evaluation and financial viability analysis of grid associated 10 MWP solar photovoltaic power plant at UP India

The main aim of this simulation work is to assess the financial possibility analysis of 10 MWP grid-associated solar photovoltaic (PV) power plants in seven cities i.e. Lucknow, Agra, Meerut, Gorakhpur, Kanpur, Allahabad, and Varanasi of Uttar Pradesh (UP) state of India with the RETScreen Software. The presented research work demonstrates the method of selection of profitable locations for solar PV power plants according to financial viability indicators. It is found that Allahabad city is the most profitable site with values of 16,686 MWh of electricity exported to the grid (EEG), US$20,896.30/year of electricity export revenue (EER), 9.4 years of simple payback period (SPP), 7.7 years of equity payback period (EPP), 19545.9 tCO2/year of GHG emission reduction, US$3492.82/year of the annual life cycle savings (ALCS), 1.5 benefit-cost (B-C) ratio, US$27394.59 of net present value (NPV), 16.5% internal rate of return on equity (IRR-equity), 12.3% modified internal rate of return on equity (MIRR-equity), 5.4% internal rate of return on assets (IRR-assets), and 7% modified internal rate of return on assets (MIRR-assets). The second most profitable site is found in Gorakhpur city and Varanasi city is found the least profitable site. The other two cities, Agra and Kanpur are not found suitable because of the negative values of NPV and ALCS.

Analyzing the effects of government policy and solar photovoltaic hydrogen production on promoting CO2 capture and utilization by using evolutionary game analysis

This study aims at proposing a public-private partnership project including the government (GOVT), coal-fired power plant (CTPP) and solar photovoltaic power plant (PVPP) toward carbon emission reduction and neutralization in China. The project is formed using the CO2 capture and utilization (CCU) and solar PV hydrogen production technologies. When all the participators cooperate, the influence of typical factors on the project is evaluated using the evolutionary game theory method. The results show that increasing the CCU subsidy can strengthen the participating intention of the CTPP but will reduce that of the GOVT. Improving the green electricity subsidy can strengthen the participating intention of the PVPP but will decrease that of the GOVT. To ensure the smooth running of the project, a relatively higher carbon emission penalty and a higher sales price of methanol should be ensured, the PVPP should control its daily working hours appropriately, and the proportion of electricity quantity of the PVPP used for hydrogen production should be increased. In this study, the suggested ranges of CCU and green electricity subsidy coefficients are 0.03–0.05 $/kWh and 0.0035–0.0045 $/kWh, the carbon emission penalty coefficient should be greater than 11 $/t, and the suggested range of daily working hours of the PVPP is 7–8 h.

Site suitability analysis for implementing solar PV power plants using GIS and fuzzy MCDM based approach

Although Saudi Arabia can benefit from implementing photovoltaic (PV) solar power projects to generate power, there are some environmental, economic and technical challenges which can affect the efficiency and cost effectiveness of these facilities. The goal of this paper is to build a site suitability model to identify the suitable sites for implementing solar PV solar projects in Saudi Arabia. Fuzzy analytical hierarchy process (AHP), as a weighting technique, and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II) method are combined to appropriately evaluate the suitable sites. 12 factors divided into two criteria (technical and economical) were incorporated to ensure minimization of construction costs while maximizing power output from the PV power plants. The resulting suitability map shows that Saudi Arabia has huge potential for implementing solar PV projects with approximately 376,623 km2 (65.1 %) of the total studied area considered, “most to highly suitable”. In addition, a validation of the model’s predictivity was conducted through an evaluation of the suitability map with respect to the future solar PV projects that Saudi Arabia is developing. The results showed that 90.6 % of the future projects fell within, “most and highly suitable” areas provided by PROMETHEE II suitability map. Furthermore, a sensitivity analysis was carried out by using different preference functions and higher weights for the economic criteria to examine the effect of economic factors toward the suitability results.

Iskustva u izvođenju i eksploataciji fotonaponske elektrane instalisane snage 500 kW u fabrici „Gruner“ d.o.o-Vlasotince

The main purpose of the solar photovoltaic power plant (SPVPP), with installed power of 500 kW on the roof of the factory GRUNER Serbian Ltd in Vlasotince, is to electrical supply of consumers in the factory. Considering the geometric dimensions of the roof and the daily and monthly profiles of electricity consumption in the factory, at the beginning of 2021, SPVPP was designed. In this way It is achieves parallel operation with the electricity distribution network, with the possibility of delivering surplus electrical energy. SPVPP was performed by the company CONSEKO Ltd., Belgrade in the period 15.0715.10.2021 and was put into trial operation at the end of September 2021, and the use permit of the plant for operation was issued on 30.12.2021. Since that date, SPVPP has been officially in exploitation operation. The GRUNER factory is on March 30, 2022 concluded with JP EPS, Branch "EPS Snabdevanje" concluded a contract for full supply of electricity for the period 01.04.2022 to 31.12.2023. The paper presents some key experiences from the construction and operation of PVPP, which could be interesting for both designers and implementers. The realization of PVPP has made a certain contribution to the promotion of renewable energy sources and the use of "green" energy in the territory of the Republic of Serbia.

Energy Characteristics of Spherical and Cylindrical Solar Photovoltaic Power Plants on the Surface of Solar Houses

Energy Characteristics of Spherical and Cylindrical Solar Photovoltaic Power Plants on the Surface of Solar Houses

An Online Archimedes Optimization Algorithm Identifier-Controlled Adaptive Modified Virtual Inertia Control for Microgrids

Single widespread employment of renewable energy sources (RESs) contributes to the shortage in the inertia of the microgrid (MG). After this, frequency stability may regress as a result of power imbalance or minor load fluctuations. This paper proposes an explicit adaptive modified virtual inertia control (VIC) based on an online Archimedes optimization algorithm (AOA) identifier for MG containing thermal, wind, and solar photovoltaic power plants. The Rung Kutta approach is used to construct the proposed online identifier, which acts as a model of the MG. AOA predicts the coefficients of the online identifier based on the input and output of MG to mimic the frequency deviation of the MG online. AOA estimates online the inertia and damping coefficients of the VIC system with an energy storage device based on online AOA identifier coefficients. The frequency deviation of the MG based on the proposed explicit adaptive modified VIC is compared with the conventional VIC based on fixed parameters and the VIC system based on optimal parameters using AOA offline under mutation in loads, weather-dependent input, and MG parameters using MATLAB/Simulink software. Furthermore, the proposed explicit adaptive modified VIC based on an online AOA identifier is evaluated with the adaptive VIC system based on fuzzy logic control, which adjusts only the inertial gain online. The simulation results demonstrate the capabilities of the proposed explicit adaptive modified VIC to improve the frequency stability and enhance low-inertia islanded MGs with RESs.

Techno-economic feasibility analyses of grid- connected solar photovoltaic power plants for small scale industries of Punjab, Pakistan

The globally soaring energy prices and electricity shortfall are major hurdles in the economic development of Pakistan. To cope with periodic power outages, small and medium enterprise (SME) business owners have to fall back on alternate power sources such as backup generators and uninterruptible power supplies (UPS), which further increase the per kWh cost of electricity, power quality issues, and greenhouse gas (GHG) emissions. On the contrary, grid-tied solar photovoltaic (PV) systems are not only economical and sustainable but support the national power grid to mitigate environmental emissions. This study aims to investigate and compare the techno-economic viability of grid-connected solar photovoltaic power plants for the manufacturing SME sector in four different districts of Punjab, Pakistan. Based on the technical, financial, and environmental indicators, a detailed techno-economic, sensitivity, and GHG emission analysis is conducted using RETScreen Expert software. The research findings clearly show that the proposed solar PV projects for all four locations are technically, financially, and environmentally viable, however, Sargodha as compared to other sites is the most feasible location with the highest capacity factor of 17.8 %, highest internal rate of return 14.9 %, lowest payback period 7.7 years, and least levelized cost of electricity 8.5 ¢/kWh. For validation, the simulation results are compared with performance metrics from PV plants erected in various parts of the world. Applying the same research approach to the whole industrial sector of Punjab recommends adding 13,469 MW of PV capacity to satisfy the industry’s 20446.21 GWh annual energy consumption and to cut emissions by 90,17,581 t CO2 per year. This research work presents guidelines for researchers to evaluate the feasibility of suitable PV technologies for the SME sector thereby helping investors to have a holistic view of potential investment zones.