Solar Photovoltaic Power Plant Research Articles

Power and efficiency enhancement of solar photovoltaic power plants through grouped string voltage balancing approach

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

Improving capacity configuration and load scheduling for an integrated multi-sourced cogeneration system

The integration of renewable energy sources and fossil fuel e.g. coal in a heat and power cogeneration system has been proven to be an effective way to reduce CO2 emissions. An integrated multi-sourced cogeneration (IMC) system consisting of wind, solar photovoltaic (PV), and coal-fired combined heat and power (CHP) plant on the energy supply side, is studied in this paper. A bi-level collaborative optimization model for capacity configuration and load scheduling of the wind-PV-coal IMC system is developed. In the upper level, the capacity of wind farm and PV farm are optimized with the enumeration method, while the load scheduling under typical annual conditions is optimized with adaptive particle swarm optimization algorithm in the lower level. The results show that the optimal capacities of wind and PV are 120 MW and 280 MW for the IMC system, respectively. The annual and typical day load scheduling with optimal wind and PV capacities are analyzed. The result of the load scheduling shows that heating and power load distribution unevenly between the two CHP units contributes to reducing total standard coal consumption, which saves 34.96 t in a typical day during the heating season. This study can provide a reference for the capacity configuration and operation scheduling of the IMC system.

Energy, economic and environmental evaluation of the energy storage capabilities of artificial lakes based on pumped-hydro storage systems with a case study of Daghgoli artificial recreational lake

In the present study, Daghgoli artificial recreational lake has been investigated as an energy storage system. This lake with a capacity of 54,000 (m3) is located in the largest metropolis of northwestern Iran, above the Eynali Mountains, in the vicinity of the city. The elevation of this lake is about 450 (m) from the downstream lake with a capacity of 60,000 (m3). The energy storage capacity of the lake has been evaluated based on the pumped-hydro method. Then, the capacity of this large “battery” to store a part of the excess energy generated by the Tabriz thermal power plant has been evaluated. In addition to the technical evaluation, a full economic analysis as well as environmental investigation of application of the lake as an energy storage system has been conducted. The capacity that can be develop the existing small wind farm in the vicinity of the lake so that its generated energy can be fully stored by the energy storage system based on Daghgoli Lake has also been determined. Finally, the capacity of the photovoltaic solar power plant that can be established in the flat plain adjacent to the lake to store its generated electricity by the lake to be used at night or peak hours has been also evaluated. The results showed that using the presented energy storage plan, 74.7(MWh) of excess electrical energy generated by the thermal power plant can be stored daily. By releasing this energy during peak hours, 13.24 (MW) of electricity can be generated with an energy conversion efficiency about 71 %. At the same time, the emission of 8508 tons of carbon dioxide can be prevented during a year. The investment payback period of the project is estimated to be 4.7 years. By using the designed energy storage system, a wind farm consisting of 16 turbines with a capacity of 660 (kW) or a photovoltaic solar plant with a capacity of 9.34 (MW) can be created.

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

Design an energy storage system for a 1 MW photovoltaic powerplant based on compressed-gas-refrigeration for buildings cooling and ice production

An energy storage system was designed for a 1 (MW) photovoltaic solar power plant. This power plant is located in a university campus in the hot desert region, which requires continuous cooling of its buildings consists of a large number of classrooms. Due to the desert nature of the region, it was not possible to use conventional energy storage systems based on pumped-hydro; because in considered climate, it is not possible to provide a large volume of water and compensate for the evaporated water. Therefore, an energy storage system based on compressed gas refrigeration was used. The effect of system parameters including the maximum pressure on its performance including COP, total space can be cooled, ice making capacity and also the required volume for high-pressure vessel was studied. The effect of using different gases including air, nitrogen, ammonia, carbon dioxide and argon as the working fluid was studied. Also, the effect of the mixture of nitrogen and carbon dioxide and different molar percentages of them on the performance of the system was studied. The results showed that the designed energy storage system can well supply the cooling load of the university building where the solar power plant is located.

Techno-economic performance assessment of selected power plants in Uganda

ABSTRACT Amidst the current global pursuit for access to energy by all as well as energy transition, the actual performance of existing power plants, particularly in developing countries, has often attracted little interest in scientific research. This study evaluated the performance of selected power plants in Uganda using the techno-economic performance assessment model based on actual operational data of hydropower, solar photovoltaic, cogeneration, and thermal power plants for the period 2010–2021. The analysis revealed that the average combined capacity factors are 19.8%, 22.9%, 18.4% and 58.6%, respectively, for thermal power plants, co-generation power plants, solar power plants and hydropower power plants. Furthermore, the levelised cost of electricity for the selected power plants’ financial performance is found to be lower than the retail price for a unit of electricity in Uganda. Overall, the study revealed that the real-life techno-economic performance of the selected power plants in Uganda agrees with the globally expected performance ranges.

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

The world is moving forward to a transition in the form of increasing the contribution of renewable energy sources in the energy sector, and among these, solar photovoltaic-based power generation is catching pace. Several factors are responsible for the lowering of outputs due to different degradation causes such as hotspots, corrosion, humidity, ultraviolet (UV) irradiation, temperature effects, dust, aging, weathering, yellowing, snail trails, discoloration, junction box failure, delamination, cracks, and faults from the solar photovoltaic (PV) plants. This paper presents a comprehensive review of the various form of degradation and their implications on solar PV power plant performance. The review has been carried out considering the different degradation causes and their identification methods in solar PV plant. Further, the analysis has been done on the basis of the earlier studies to understand the rates of degradation for various solar PV power plants in various climatic conditions. The PV technologies used in solar power plants are also responsible for the change in the performance of power plants over time; therefore, degradation based on different solar PV cell technologies is also analyzed. The visual inspection tools like thermal imaging with IR cameras help identify areas with abnormal heat patterns, indicating potential issues like cell or interconnect failures, loose electrical connections, or bypass diode malfunctions while EL cameras are used to identify low-level electrical excitation and defects such as cracks, hotspots, and cell-level degradation.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering.

Effect of Platform Configurations and Environmental Conditions on the Performance of Floating Solar Photovoltaic Structures

The growth and development of floating solar photovoltaic (FPV) power plants is a prominent topic within renewable energy technology. One reason contributing to this desired technology design concept is the possibility of land acquisition issues, whereas the usage of the ocean provides a greater technical alternative area. The objective of the research is to present an innovative design for a floating structure, focusing on investigating and comparing the seakeeping performance of several hull configurations: catamaran, trimaran, quadrimaran and pentamaran. The final computational simulation results indicate a linear negative trend in the motion response graphs, particularly in specific significant response values for heave (Global Z), roll (Global RX), and pitch (Global RY), as the hull configuration increases.

Comparative Numerical Energy and Performance Analysis of Solar Photovoltaic and Solar Thermal Power Generation

This study analyses fixed and tracking solar photovoltaics and, solar thermal power. Performance in different locations was analysed for each configuration and technology using simulations based on a Typical Meteorological Year data. The study analysed a 25MWp solar photovoltaic and solar thermal power plants in each of the eleven selected locations in Zimbabwe. The performance of the solar photovoltaic and solar thermal power plants under different meteorological variables were assessed for all the selected locations. It was shown that different configurations together with different technologies have different conversion efficiencies. A high solar thermal conversion efficiency was found to be 18.718% in Gweru while it was 15.502% for solar photovoltaics in Mutare. The study also showed that highest insolation and clearness index values were found in Gweru. The average energy generated by the fixed photovoltaic collectors, tracking photovoltaic collectors and the Concentrating Solar Power plant were respectively 47.38GWh, 68.18GWh and 192.86GWh. There was a maximum percentage difference in the LCoE generated of 32.03% between the fixed PV collectors and the Concentrating Solar Power plant and a difference of 4.74% was realised between the tracking photovoltaics and Concentration Solar Power.

A GIS-based catastrophe approach for optimal site selection for installation of solar power plants, East Azerbaijan province case study, Iran.

Due to depletion of fossil fuels and environmental issues, renewable energy consumption is increasingly growing. Solar energy as the most abundant renewable energy source available is becoming more popular around the world. In the current study, the optimal sites for solar photovoltaic power plants in East Azerbaijan province, Northwest Iran, were investigated. A total of 17 variables were categorized into four groups: climatic, geomorphological, environmental, and access-economic. In order to integrate the variables, a model based on catastrophe theory in the context of GIS was applied. The relative importance and weight of the criteria are computed based on the internal mechanism of the catastrophic system, thus greatly reducing subjectivism and uncertainties of the decision-making process. Five optimal sites located in the western part of the province within the counties of Malekan, Bonab, Ajabshir, Shabestar, and Tabriz were identified as suitable sites for the construction of solar photovoltaic power plants, where there are ideal conditions in terms of many environmental-human variables such as high potential of solar energy, high sunshine hours, low relative humidity, suitable slope, poor vegetation, distance to protected areas, proximity to the population centers, excellent access to the roads and to the main power lines.

Monte Carlo simulation-based simulation of shadow occlusion efficiency of a heliostat field

Tower solar photovoltaic power plant has a broad application prospect as a sustainable energy solution, in which the shadow occlusion efficiency, as an important part of the optical efficiency of the mirror field, has always been a computational difficulty. In this paper, the geometric projection method and Monte Carlo algorithm are used to explore the shadow occlusion efficiency of a heliostat field, and the two parts of shadowing and occlusion are simulated based on the coordinate transformation under the mirror field coordinate system. By calculating the shadow occlusion efficiency, the energy loss of the heliostat system can be evaluated, and then the system design can be optimized to improve the energy harvesting efficiency of the system.

PVsyst and Homer for the Evaluation of a Project to Size a Photovoltaic Solar Power Plant in Reinforcement of the CEET Network in Lomé in Togo by Characterization of the Electrical Energy Consumed

PVsyst and Homer for the Evaluation of a Project to Size a Photovoltaic Solar Power Plant in Reinforcement of the CEET Network in Lomé in Togo by Characterization of the Electrical Energy Consumed

Jellyfish search algorithm for optimization operation of hybrid pumped storage-wind-thermal-solar photovoltaic systems

This study applies Jellyfish Search Algorithm and five other algorithms to minimize the electricity generation cost of two hybrid systems for one operating day. The first system comprises one pumped storage hydroelectric plant and two thermal power plants. The second system is expanded by integrating one wind and one solar photovoltaic power plant into the first system. For each system during one operating day, the pumped storage hydroelectric plant with only generation mode acts as a conventional hydroelectric plant in the first scenario. Still, it can run pumps to store water and produce electricity in the second scenario. As a result, JSA can reach smaller costs than all compared algorithms, from about 1 % to higher than 10 % for two scenarios in the two systems. The comparisons of generation cost indicate the second scenario with the pumped storage hydroelectric plant can reach a smaller cost than the first scenario with the conventional hydroelectric power plant by $53,359.7, corresponding to 7.4 % in the first system and $39,472.8, corresponding to 6.95 % in the second system. Therefore, the water storage function of the pumped storage hydroelectric plant is very effective in reducing the electricity generation costs for hybrid power systems.

PERANCANGAN JARINGAN DISTRIBUSI UNTUK PLTS TERAPUNG DAN PLTMH DI BENDUNGAN SIDAN

Blahkiuh Feeder is a 20 kV radial distribution network spanning 78.65 kms, which is the longest network in the Mengwi Network Unit. The length of the distribution lines also results in relatively high power losses. Therefore, the installation of distributed generation (DG) utilizing the potential of solar photovoltaic (PV) and micro-hydro power plants (MHP) available at the Sidan Dam has the potential to improve voltage drop and power loss values on the Blahkiuh Feeder. This research was conducted to analyze voltage and power losses due to DG installation at the Sidan Dam, with power flow simulation during peak daytime load at 14:00 and peak nighttime load at 19:00. Simulation results indicate that when interconnected with the daytime peak load at 14:00, DG interconnection results in a decrease in power loss values by 0,8% or 64 kW, and interconnection with the nighttime peak load at 19:00 also leads to a decrease in power loss values by 0.61% or 55 kW. Distributed Generation interconnection on the Blahkiuh Feeder also leads to improvements in voltage values, where interconnection during daytime at 14:00 results in voltage improvement at 41 busbars units under undervoltage condition, and interconnection during night time at 19:00 leads to voltage improvement at only 34 busbars units undervoltage condition out of a total of 50 undervoltage busbars.

Demarcation of suitable site for solar photovoltaic power plant installation in Bangladesh using geospatial techniques

The major goal of this research is to adopt analytical hierarchy process (AHP) based geospatial technique to select suitable zone for the solar photovoltaic (PV) power plants. Nine thematic layers altogether—slope, global horizontal irradiation (GHI), relative humidity, direct normal irradiation (DNI), elevation, distance from major roads, distance from protected areas, rainfall, and land use/land cover (LULC)—are combined through overlay analysis in ArcGIS to create the final map of suitability for the placement of solar photovoltaic (PV) power plants in Bangladesh. This map has been classified into five categories namely, restricted zone, less suitable zone, moderate suitable zone, good suitable zone, and excellent suitable zone. These categories are covered by 7.28%, 16.61%, 28.51%, 27.77%, 21.83% land of total area in Bangladesh respectively. The findings of this research have been presented that ‘the excellent suitable’ and ‘good suitable’ areas for the construction of solar power plants are in the western and northwestern part (Rajshahi, Pabna, Sirajganj, Natore, Naogaon, Chapainawabganj, Bogura, Faridpur, Jessore, Jehenaidha, Magura, Kushtia, Choudanga, Meherpur) of the study area which contain a high value of global horizontal irradiation, direct normal irradiation, elevation and low value of slope, rainfall, temperature, relative humidity. Besides the restricted and less suitable zone for installing solar photovoltaic (PV) power plants indicates a high value of rainfall, slope, temperature, relative humidity and low value of global horizontal irradiation, direct normal irradiation, and elevation. Bangladesh's currently operational solar plants were taken into consideration for this study's validation purposes. The proposed framework may potentially be used in different locales on a national and worldwide scale. This study offers a consistent GIS process for the accurate, inexpensive implementation of a solar energy plan to achieve environmentally friendly goals.

Techno-economic feasibility study of solar photovoltaic power plant using RETScreen to achieve Indonesia energy transition

Indonesia, a key player in the global energy transition, faces surging electricity demand and ambitious renewable energy goals. In response, the government introduced a new regulation about renewable energy tariffs, including tariffs for photovoltaic (PV). However, there remains a gap in the academic literature regarding PV power plant feasibility studies under these tariffs. To address this gap, this study investigates the feasibility of a utility-scale solar photovoltaic (PV) power plant in Indonesia, focusing on the newly implemented renewable energy tariffs based on Independent Power Producers (IPPs) and Indonesia's state-owned electricity company (PLN) perspectives. Five scenarios were developed based on the proposed 26 MW solar power plant on Nias Island utilizing RETScreen software. The results showed that based on the IPP perspective, the newly implemented renewable energy tariff was inadequate to make the project feasible, however, an introduction of a 10 USD/t CO2 emission incentive would make the project financially viable for IPPs. Therefore, it is recommended to introduce emission incentives as a strategic approach to attract investors and stimulate investment in Indonesia's PV power plants market, to accelerate Indonesia's energy transition. Conversely, the results also showed that the project is very profitable for PLN due to the significant cost-savings from the de-dieselization, leading to a reduction in the average generation cost for Nias.

Optimization of Optical Efficiency Modeling and Heliostat Field Layout of Tower Solar Thermal Power Station

The aim of this paper is to study and optimize the layout of heliostat mirror fields in solar photovoltaic power plants to improve energy efficiency. By analyzing the key factors of optical efficiency, the optical efficiency model is constructed. The optical efficiency of a single heliostat mirror is calculated using five efficiency factors, namely, shadow shading efficiency, cosine efficiency, atmospheric transmittance, collector truncation efficiency, and mirror reflectivity. The DELSOL layout is used as the initial mirror field, and the mirror field design is optimized by the particle swarm algorithm. The optimal design parameters of the heliostat mirror field including the position of the absorption tower, the size of the heliostat, and the installation height were calculated by adjusting the positions of the heliostat and the absorption tower many times. The results show that the annual average optical efficiency of the heliostat field is 0.6127, and the annual average thermal power output is 37.4508 MW. Through optimization, the annual average optical efficiency of the heliostat field is increased to 0.75, and the annual average thermal power output reaches 64.98 MW. In addition, the results show that the heliostat field has a seasonal characteristic. The optical efficiency and the annual average thermal power output change monthly.

Assessing the Performance of Off-Grid Solar Photovoltaic Power Plants in Supporting Aquaponics Systems

In an aquaponics system, the electricity produced by photovoltaic solar power (PLTS) is stored in batteries and used to power the water pumps. Using natural microorganisms to transform fish waste into plant nutrients, aquaponics is a cultivation method that blends fish and plant cultivation. The aquaponics system helps reduce the amount of trash released into the environment and is beneficial to the environment. The main goal of this study is to evaluate an off-grid PLTS system's functionality as a DC water pump power source in an aquaponics setting. This study's goal is to evaluate how well the PLTS operates when powering the DC water pump that circulates water in the aquaponics system.According to performance tests, the off-grid PLTS generates an average energy of 503.8 Wh, which is enough to power the DC pump for 20 hours a day of water circulation. The water pump uses only 382.6 Wh of electricity each day. With an average solar panel efficiency of 4.97%, an average solar charge controller (SCC) efficiency of 60.51%, and an average pump efficiency of 79.91%, the Off-grid PLTS demonstrates efficiency. With a daily solar irradiation of 7.8 kWh/m2, the average energy loss in the solar panel is 5.29 kWh/kWp, whereas the average energy loss in the system is 0.10 kWh/kWp. All things considered, these results provide insight into how well the Off-grid PLTS functions as a power source for the DC water pump in an aquaponics system.