Solar Photovoltaic Plant Research Articles

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.

Combining solar panels with plants for sustainable energy and food production: state of the art

The need for alternative energy sources becomes extensive because of the escalating cost of fossil fuels. The goal of this paper is to examine the effectiveness of combining photovoltaics and agriculture for better yield. Photovoltaic (PV) solar plants will compete with farms for available land. In this study, the methodologies are discussed how it is possible to maximize land utilization by placing solar arrays and food crops on the same plot of land. The term is proposed "agrivoltaic system" to describe this setup. Conventional solutions (discrimination of agricultural and energy extracting) were compared to two agrivoltaic schemes with varying density of PV arrays using land equivalent ratios. We utilized a crop model to simulate the amount of sunlight reaching the crop from an array of solar panels and to speculate on the yield reduction that would result from the partial shading. These early findings suggest that agrivoltaic systems may be highly effective; the two densities of PV panels were anticipated to boost worldwide land production by 73%. One possible explanation for the success of these hybrid systems is the presence of facilitation mechanisms analogous to those seen in agroforestry. At the end it is suggested that in places where arable land is rare, new solar plants may find it beneficial to produce both power and food.

A comprehensive evaluation and advancements in dissolved gas analysis for biodegradable oil in photovoltaic solar plants

Over the past three decades, the energy industry has embraced the adoption of a new type of biodegradable oil. Presently, the prevailing international standard for its evaluation is IEEE C57.155–2014. Various working groups, including those within CIGRE A2.68, IEC, and industry manufacturers, have embarked on endeavors to explore new evidence and methodologies for assessing the accuracy and alternatives in detecting failures. These failures are often indicated by elevated levels of seven gases: Hydrogen, Ethane, Acetylene, Methane, Ethylene, Carbon Monoxide, and Carbon Dioxide. Consequently, instances of high oil temperatures have been observed during the optimization of dimensions near hot-spot limits, particularly in renewable energy applications. This research article presents an in-depth analysis of a substantial database comprising 2834 units, aimed at establishing new thresholds for dissolved gas analysis applied to biodegradable oil in photovoltaic solar plants. The developed methodology successfully identified 844 oil samples in poor condition, 332 samples exhibiting one or more failure modes without fault or disconnection, and 1658 units in good condition. Validation of the instrumentation was conducted on 87 transformers through internal inspection, verifying the classification according to the new methodology for evaluating relevant variables and limits of gases through Gaussian distribution and seven algorithms. This validation aimed to enable early detection of failures and establish a health index, achieving an accuracy rate of 90.8 % with support vector machine, 97.6 % with random forest, and 98.3 % with XGBoost. Future research directions should consider evaluating viscosity as a factor in assessing the quality and degradation of transformers utilizing biodegradable oil.

Investigation of hybrid power-to-hydrogen/natural gas and hydrogen-to-X system in Cameroon

In Sub-Saharan Africa (SSA), the capacity to generate energy faces significant hurdles. Despite efforts to integrate renewable energy sources and natural gas power plants into the energy portfolio, the desired reduction in environmental impact and alleviation of energy poverty remain elusive. Hence, exploring a spectrum of hybrid technologies, encompassing storage and hydrogen-based solutions, is imperative to optimize energy production while mitigating harmful emissions. To exemplify this necessity, the 216 MW Kribi gas power plant in Cameroon is the case study. The primary aim is to investigate cutting-edge emissions and energy schemes within the SSA. This paper assessed the minimum complaint load technique and four power-to-fuel options from technical, financial, and environmental perspectives to assess the viability of a natural gas fuel system powered with hydrogen in a hybrid mode. The system generates hydrogen by using water electrolysis, with photovoltaic electricity and gas power plant. This research also assesses process efficiency, storage capacity, annual costs, carbon avoided costs, and production prices for various fuels. Results showed that the LCOE from a photovoltaic solar plant is 0.19$/kWh, with the Power-to-Hydrogen process (76.2% efficiency) being the most efficient, followed by the ammonia and urea processes. The study gives a detailed examination of the hybrid hydrogen natural gas fuel system. According to the annual cost breakdown, the primary costs are associated with the acquisition of electrical energy and electrolyser CAPEX and OPEX, which account for 95% of total costs. Urea is the cheapest mass fuel. However, it costs more in terms of energy. Hydrogen is the most cost-effective source of energy. In terms of energy storage and energy density by volume, the methane resulted as the most suitable solution, while the ammonia resulted as the best H2 storage medium in terms of kg of H2 per m3 of storage (108 kgH2/m3). By substituting the fuel system with 15% H2, the environmental effects are reduced by 1622 tons per year, while carbon capture technology gathered 16,664 tons of CO2 for methanation and urea operations, yielding a total carbon averted cost of 21 $/ton.

Convolutional neural networks and Internet of Things for fault detection by aerial monitoring of photovoltaic solar plants

Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access.This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method.

Real time performance assessment of utility grid interfaced solar photovoltaic plant

Continuous monitoring of large-scale solar photovoltaic (PV) installations is necessary to check the deterioration and monitor the performance of the PV plant. Fault diagnosis is crucial to ensure the PV plant operates safely and reliably. This paper presents a diagnosis methodology based on current-voltage (I-V) and PV characteristics to monitor and assess the behavior of solar PV. In this paper, I-V curve characterization using an I-V curve tracer is used to check the deterioration and diagnosis of the PV panels. The real-time performance of the 50.4 kWp rooftop solar grid interfaced PV plant is investigated and analyzed using I-V and PV curve tracers in real-time conditions. The overall performance of solar PV is assessed on a real-time test system in different scenarios such as variable climatic conditions, partial shading conditions, aging of solar panels, short circuit conditions, and dust decomposition. Furthermore, the performance assessment of solar PV is evaluated using performance indicators such as open circuit voltage index, short circuit current index, fill factor, and performance ratio.

Optimal Location of Solar Photovoltaic Plants Using Geographic Information Systems and Multi-Criteria Analysis

Nowadays, solar energy is considered to be one of the most developed renewable energy sources, and its production capacity has increased in recent years. To optimize yields and production, the correct selection of the location of these plants is essential. This research develops a methodological proposal that allows for detecting and evaluating the most appropriate places to implement solar photovoltaic plants almost automatically through GIS tools. A multi-criteria analysis is proposed to analyze large extensions of land with ten duly weighted criteria that cover the energy and territorial requirements that any installation must meet. The method assigns each site a location coefficient that reflects the weighting of the chosen criteria so that the value ordered from highest to lowest reflects the best to the worst location. Unlike other research works that can be considered similar, the methodological proposal is much more consistent than traditional alternatives as it uses a multi-criteria analysis and a weighting mechanism that is also statistically consistent, objective, and based on logical criteria. This innovative methodology is applied to Cantabria (north of Spain), although it could be used for other contexts.

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting

Industrial activities are transitioning towards decarbonization, focusing on renewable energy sources, particularly photovoltaic solar energy. However, the inherent high variability of photovoltaic energy poses challenges. Some of them can be partially addressed by predicting electricity production, which in the case of photovoltaic solar energy is heavily based on solar irradiance prediction. Although extensive research has been conducted in this field, there is a noticeable gap in research regarding very short-term (intra-minute) forecasting under high-variability scenarios. In this proposal, real data from a photovoltaic solar plant in Alderville (Canada) were used to predict irradiance with a horizon of 15 and 30 s. The objective is to make this prediction in near-real time. To achieve this, we propose the use of machine learning algorithms based on decision tree ensembles, due to their low computational training cost and known effectiveness. On the other hand, we propose pre-processing the data through a temporal and spatial correlation analysis between measurements from different sensors. Feature selection analysis allows us to determine the direction of the wind and consequently identify the most relevant panels for model training. This preprocessing enhances the model retraining without the need for external information such as sky images or wind speed and direction on days with highly variable cloud cover. The presented methodology offers promising results with significantly reduced training times, demonstrating the suitability of this semi-online training approach for highly variable time series forecasting.

A case study of techno-economic and environmental analysis of college rooftop for grid-connected PV power generation: Net zero 2050 pathway

In this study, a grid-connected solar photovoltaic plant (SPV) is designed, evaluated and analysed to meet the energy consumption of the College of Science and Technology in Siirt province, Turkey. The comprehensive financial and technical statistics in relation to parameters utilised within the system are presented alongside thorough economic and environmental evaluations. The design, economics, and technical evaluation of the system are supported by using PVsyst, PV*SOL and HOMER Pro. Based on the findings of the analysis, it has been determined that the photovoltaic installation is projected to provide an estimated 762 MWh of energy. In the economic evaluation conducted, the internal rate of return (IRR) was determined to be 19.55 %, the net present value (NPV) was calculated to be 346,085 USD with Levelized Cost of Energy (LCOE) of 0.1892 USD/kWh. In addition, the payback period of the photovoltaic plant is determined to be 17.4 years. The implementation of the photovoltaic panel system will result in an estimated reduction of roughly 6852 tonnes of carbon dioxide (tCO2) emissions.

A short- and medium-term forecasting model for roof PV systems with data pre-processing

This study worked with Chunghwa Telecom to collect data from 17 rooftop solar photovoltaic plants installed on top of office buildings, warehouses, and computer rooms in northern, central and southern Taiwan from January 2021 to June 2023. A data pre-processing method combining linear regression and K Nearest Neighbor (k-NN) was proposed to estimate missing values for weather and power generation data. Outliers were processed using historical data and parameters highly correlated with power generation volumes were used to train an artificial intelligence (AI) model. To verify the reliability of this data pre-processing method, this study developed multilayer perceptron (MLP) and long short-term memory (LSTM) models to make short-term and medium-term power generation forecasts for the 17 solar photovoltaic plants. Study results showed that the proposed data pre-processing method reduced normalized root mean square error (nRMSE) for short- and medium-term forecasts in the MLP model by 17.47% and 11.06%, respectively, and also reduced the nRMSE for short- and medium-term forecasts in the LSTM model by 20.20% and 8.03%, respectively.

Comparison of Results from Two Multi-Criteria Decision-Making Methods for Solar Photovoltaic Plant Site Location: Case Study Rio De Janeiro

Photovoltaic (PV) energy has become a low-cost, renewable, and environmentally friendly alternative to meet increasing energy demand. Nevertheless, there is still a lack of projects in this field in Brazil. Therefore, this study compares the results of two studies on the optimal site selection of PV in the Brazilian state of Rio de Janeiro. These studies used different methodologies to reach the conclusions and the resulting map. First, the final map of both studies was divided into a grid, and then the results of each cell were weighted for PV site selection. To compare the results using the maps, an intersection of the 10% of the grid cells with the best results from each study was formed. The results showed an 83% similarity between the different Multi-Criteria Decision-Making (MCDM) methods. The other part of the comparison focused on the following rank similarity coefficients: Spearman Correlation Coefficient, WS Coefficient, Spearman Weighted Correlation Coefficient, and Blest Correlation Coefficient. All these coefficients had values greater than 0.9, indicating a high degree of correlation between the results of the studies. Therefore, the two studies have a high degree of similarity and a high potential for installing photovoltaic solar power plants in Rio de Janeiro, especially in its intersection zones.

Unravelling spatiotemporal patterns of solar photovoltaic plants development in China in the 21st century

Solar energy, as an environmentally sustainable power source, is gaining increasing popularity worldwide, driving a surge in the number of solar photovoltaic (PV) plants. China, which has a prominent role in this domain, requires continuous updates to its PV plant data for spatiotemporal analyses. However, there remains an absence of a comprehensive and timely dataset of PV plants across China, leaving PV installation dates and other crucial attributes for comprehensive analyses underexplored. This study leverages Sentinel-2 data as a primary source to propose an optimized deep learning approach for PV plant extraction in China. Statistical analyses of PV plant attributes, including its installation date, size, site slope, and site land cover, were implemented from multiple data sources. Comprehensive analyses were conducted to unravel their spatiotemporal development patterns in the 21st century. The results indicate that as of 2023, China boasts 4347 PV plants, collectively spanning 4146 km2, which are predominantly concentrated in Northwest and North China. 2016 and 2017 marked substantial growth in China’s PV plants, while other years exhibited stability. These plants exhibit the distinct spatial characteristics of installing smaller PV plants on flat terrain covered by vegetation or barren land. Over time, a notable trend in the installation of China’s PV plants has been the increasing preference for establishing larger ones in smooth terrain, with a focus on preventing damage to natural resources. The results reveal China’s optimization of PV plant site selection and construction strategies, aligning with global environmental goals and sustainable energy practices.

Sustainable solar energy in urban areas: A novel framework for uncertainty, reliability, and 7E analysis

Nowadays, access to clean, reliable, and sustainable energy is essential for urban cities. Solar energy, a widely available renewable energy source (RES), is present in abundance throughout the world. Nonetheless, using this energy has challenges such as uncertainty and reliability. The present study proposes a novel two phase framework based on a multi-criteria analysis of solar plants in urban cities in terms of uncertainty and reliability to address this issue. In phase-1, solar plants were evaluated using multi criteria decision-making from technical, economic, environmental, social, political, and risk aspects based on Z-number theory. Different methods such as Z-EDAS, Z-TOPSIS, Z-ARAS, Z-COPRAS, Z-SAW, and Z-MABAC were used for robust assessment. In phase-2, a 10 MW solar plant was examined from seven indicators (energy, exergy, economic, environmental, energoeconomic, exergoeconomic, and enviroeconomic as 7E analysis). The 7E analysis showed the solar photovoltaic plant can generate annually 12.3 GWh of electricity, with an energy and exergy efficiency of 13.85 % and 14.9 %, respectively. Additionally, the Levelized cost of electricity (LCOE) was 57$/MWh. Furthermore, the proposed framework based on Z-number theory effectively consider uncertainty and reliability in solar site assessment in urban cities and can be used in future studies for RES and sustainable urban planning.

Sustainable planning: the case study of the Strait of Messina ports

The energy and environmental policy carried out by the Port System Authority of the Strait (AdSP), in charge of the management of the ports spread along the Strait of Messina, is reported. The Environmental and Energy Planning Document of Port Systems (DEASP, the Italian acronym) is the document explaining the AdSP sustainable strategy to reduce GHG emissions. It defines specific measures, in order to improve energy efficiency in buildings and infrastructures, promote the use of renewable energy in the port area, and confer environmental benefits for the citizens of neighboring territories and port users. The main actions developed are as follows: photovoltaic solar plants and tidal energy systems, electrification of the docks to allow the shore supply of ships, and the construction of a Liquefied Natural Gas (LNG) storage plant to replace more polluting marine fuels, together with awareness campaigns on “green” issues, involving the 3 million users of these ports. Starting from the socio-economic and environmental analysis of the territorial context managed by AdSP, the DEASP analyses all the activities carried out inside port areas and reports the energy consumptions of the concessionaries, in the way to calculate the carbon footprint and develop an environmental sustainable strategy to reduce pollutant emissions. The interventions foreseen are assessed through the cost-benefit analysis and allow reducing the GHG emissions in 2030 up to 46%.

Investigation of a community-based clean energy system holistically with renewable and hydrogen energy options for better sustainable development

This study develops a novel community-based integrated energy system where hydrogen and a combination of renewable energy sources are considered uniquely for implementation. In this regard, three different communities situated in Kenya, the United States and Australia are studied to cover their needs potentially by producing hydrogen, biogas, power, residential space and industrial heating, hot water and fresh water. where a multigenerational geothermal plant integrated with a solar power plant (using both concentrated solar power (CSP) and photovoltaic (PV) panels) is considered to provide the energy inputs. Innovations in hydrogen production and renewable energy are essential for reducing carbon emissions. By combining the production of hydrogen with renewable energy sources, this system seeks to move away from the reliance on fossil fuels and toward sustainability. The study investigates various research subjects using a variety of methods. The performance of the geothermal source is considered through energetic and exergetic thermodynamic analysis. The software System Advisor Model (SAM) and RETscreen software packages are used to analyze the other sub-systems, including CSP and PV units and a combined heat and power plant. The Australian, American, and Kenyan communities considered for this study are found to have promising potential for producing hydrogen and electricity from renewable sources. The geothermal output is expected to be 35.83 MW for electrical power, 122.8 MW for space heating, 151.9 MW for industrial heating, and 64.25 MW for hot water. The overall multigenerational geothermal energy and exergy efficiencies are reported as 65.15% and 63.54% respectively. These selected communities are expected to have annual solar power generation and hydrogen production capacities of 158 MW, 237 MW, 186 MW, 235 tons, 216 tons, and 313 tons respectively.

Optimal generation expansion planning model for solar PV generation on Run of River-based hydro-based power system using binary genetic algorithm

Abstract This paper presents a binary genetic algorithm approach developed to solve the optimal generation expansion planning problem for solar photo-voltaic (PV) based Run of River (ROR) hydro generation systems. The results show that the methodology effectively solves mixed-integer, constrained nonlinear generation expansion problems. The model is then used to optimize the generation expansion problem for the electricity grid in the eastern region of Nepal, considering the under construction and planned solar PV and ROR-based hydropower plants. The optimal problem is formulated to determine the optimal number, location and type of power plant to fulfill the demand for the electricity grid of the eastern zone grid of Nepal. Results show that the total generation from the optimal sites combining hydro and solar sites is found to be about 26% of that of total energy in the Nepalese grid. However, the total demand in the system under consideration for the year is about 1352 GWh. With eliminating line losses, a surplus generation of about 1377 GWh has been found. This surplus can be used as power pooling with neighboring countries. The system has a surplus during the wet season but needs to be improved during the dry season. Hence, appropriate policies can be formulated for power banking and pooling to balance the demand–supply in the system.

On-Grid Solar Traction System

World Railways is making significant progress in introducing high-speed trains to fulfil the increasing demands of the traveling public. Massive energy is needed for electric railroads. Many railroads operate their specialized power plants. An energy-storage grid-tied photovoltaic solar plant has been proposed as a strategy to boost the capacity of the rail network grid connection and enable the railway to become self-sufficient. To use the ballast-less rails as energy transporters, The current plan calls for installing solar panels along an HS train network. Ballast-free tracks require very little upkeep, and the space between them provides plenty of surface area for the installation of PV module arrays to harvest solar energy. This generated energy will feed into the main power line by using the concept of an on-grid solar system this energy is not more in numbers to drive the entire traction system, but it helps a lot. From this project we have developed hybrid parallel power sharing for the railway network, under this project the current will be shared in the main power line by matching the grid frequency and voltages. The primary power is solar when it can generate enough electricity. Still, in an abnormal condition, like in the absence of sunlight, the main power continuously feeds electricity without disturbing the traction system.

Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.

Random forest machine learning algorithm based seasonal multi‐step ahead short‐term solar photovoltaic power output forecasting

AbstractTo maintain grid stability, the energy levels produced by sources within the network must be equal to the energy consumed by customers. In current times, achieving energy balance mainly involves regulating the electrical energy sources, as consumption is typically beyond the control of grid operators. For improving the stability of the grid, accurate forecasting of photovoltaic power output from largely integrated solar photovoltaic plant connected to grid is required. In the present study, to improve the forecasting accuracy of the forecasting models, onsite measurements of the weather parameters and the photovoltaic power output from the 20 kW on‐grid were collected for a typical year which covers all four seasons and evaluated the random forest techniques and other techniques like deep neural networks, artificial neural networks and support vector regression (reference in this study). The simulation results show that the proposed random forest technique for the forecasting horizon of 15 and 30 min is performing well with 49% and 50% improvements in the accuracy respectively over reference model for the study location 22.78°N, 73.65°E, College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India.