Photovoltaic Plants Research Articles

Comprehensive case study on the technical feasibility of Green hydrogen production from photovoltaic and battery energy storage systems

AbstractThe growing demand for alternative energy sources to alleviate environmental impacts highlights the need to move from fossil fuels to renewable energy. This study demonstrated the technical feasibility of using a solar photovoltaic (PV) system for the production of green hydrogen. This research examined electrical and power data from a PV plant in Irecê, Bahia, using open data sources to provide insights into the production of green hydrogen from renewable sources. The system mainly depends on the use of a renewable source, PV solar energy, integrated with batteries, electrolyzers, and hydrogen tanks. Electrolyzer, battery, and hydrogen tank sizing analysis for optimal hydrogen production was effectively conducted using HOMER Energy software. The predicted system topology prioritizes a local DC network, optimizing efficiency for electrolyzers that have inherently low efficiency. The electrolyzer simulation involves initial Python‐based sizing and comprehensive sizing with HOMER Energy software, ensuring accuracy within a 10% discrepancy limit. This highlights the importance of analytical calculations and optimization software for sizing more complex systems.

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.

A Standardized Sky Condition Classification Method for Multiple Timescales and Its Applications in the Solar Industry

The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants.

Optimizing solar photovoltaic farm-based cogeneration systems with artificial intelligence (AI) and Cascade compressed air energy storage for stable power generation and peak shaving: A Japan-focused case study

This study proposes a novel solar cogeneration system that integrates compressed air energy storage units (CAES) and gas turbines (GT) with a solar farm consisting of photovoltaic panels. The primary objective of this research is to address the instability of solar energy production and help during peak energy consumption by utilizing CAES. The proposed system is modeled using EES software, and its performance is optimized using advanced artificial intelligence (AI) methods, including artificial neural networks and intelligent algorithms. The analysis identifies five critical decision variables that significantly impact system performance: the number of photovoltaic panels, CAES pressure ratio, CAES inlet pressure, gas turbine efficiency, and compressor efficiency. The results demonstrate that the optimized solar cogeneration system can achieve exergy efficiency of 36.44 % and a cost rate of 13.76 $/hour. The exergy analysis of the system indicates that the most significant destruction is related to the solar farm, gas turbine, and compressors. Furthermore, this study investigates the effect of weather in eight Japanese cities on system performance, considering two operating modes: with and without using system electricity (PV mode). The results show that the proposed solar cogeneration system has significant potential for clean electricity generation and CAES applications to overcome the instability of the solar system and help during peak energy consumption throughout the year. The study's findings highlight the attractive potential of integrating CAES and AI technologies in solar photovoltaic systems for stable and efficient power generation in Japan.

Short‐term power prediction of distributed PV based on multi‐scale feature fusion with TPE‐CBiGRU‐SCA

AbstractTo address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short‐term power prediction for distributed photovoltaic (PV) farms, a TPE‐CBiGRU‐SCA model based on multiscale feature fusion is proposed. First, multiscale feature fusion of meteorological features, temporal features, and hidden periodic features is performed in PV power to construct the model input features. Second, the relationships between PV power and its influencing factors are modelled from spatial and temporal scales using CNN and Bi‐GRU, respectively. The spatiotemporal features are then weighted and fused using the SCA attention mechanism. Finally, TPE‐based hyperparameter optimization is used to refine network parameters, achieving PV power prediction for a single field station. Validation with data from a PV field station shows that this method significantly enhances feature extraction comprehensiveness through multiscale fusion at both data and model layers. This improvement leads to a reduction in MAE and RMSE by 26.03% and 38.15%, respectively, and an increase in R2 to 96.22%, representing a 3.26% improvement over other models.

Performance of integrated FB and WEC for offshore floating solar photovoltaic farm considering the effect of hydroelasticity

The offshore floating solar photovoltaic (PV) farm deployed in the open sea could be subjected to large environmental loading due to waves or wakes from travelling ships. To reduce the motion of the solar farm, mitigation methods such as protection of the solar farm via floating breakwater (FB) could be deployed. The FB serves as a buffer between the floating farm and the incoming wave force, and as it attenuates the wave forces, this creates a sheltered zone on the leeward side of the FB. The energy generation from the solar farm could be further enhanced via the integration of attenuator-type wave energy converters (WEC) with the FB. This paper investigates the performance of the hybrid FB – WECs as a coastal protection structure cum wave energy extraction devices. The effect of FB on mitigating the hydroelastic response of the floating solar farm is investigated. Four types of FB, i.e., I-, L-, U- and BOX-shape FBs are considered and their effectiveness in wave attenuation under regular waves arriving from different directions are studied. In addition, due to the long structure length-to-wavelength ratio of the floating solar farm and FB, the hydroelastic response of the structures is taken into consideration.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Early Hotspot Detection in Photovoltaic Modules using Deep Learning Methods

Photovoltaic (PV) energy systems have been widely used in energy production especially in recent years due to their clean, reliable, environmentally friendly and resource continuity. The electrical energy to be obtained must be uninterrupted and of high quality. For this reason, faults that may cause production losses in PV power plants should be detected quickly and accurately. In this study, an approach that can automatically detect hotspot fault in solar PV modules is presented. In the proposed approach, firstly, Jet colormap based colored image transformation process is applied to gray scale infrared image data. Then, using three different deep learning models such as MobilNetV2, ResNet-18, InceptionV3, both original grey scale data and Jet colormap data are detected and evaluation metric values are obtained. The importance of this research lies in the potential of deep learning-based effective detection, especially in the early and rapid diagnosis of PV hotspot faults.

CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation

Solar irradiance is the main factor affecting the output of a photovoltaic (PV) power station. The dominant role of ground-based clouds on the variation of direct solar radiation in the whole sky. Ground-based cloud segmentation plays a crucial role in photovoltaic power generation prediction. Despite advancements, current cloud segmentation methods fail to meet the requirements of this application. While convolutional neural networks demonstrate impressive segmentation capabilities in ground-based cloud segmentation tasks, their inherent limitations hinder further performance enhancement. Hence, this paper introduces CloudSwinNet, a hybrid CNN-Transformer framework for ground-based cloud images fine-grained segmentation. CloudSwinNet leverages concepts from convolutional neural networks, including stepwise downsampling, local convolution, and skip connections. To further explore the fine-grained features in the ground-based cloud images, we incorporates the Fine-grained Feature Fusion Module (Fg-FFM) in encoder structure, while the jump connection structure integrates the GloRe spatial graph inference module. These additions enable comprehensive learning of multi-scale and long-range dependencies. We conducted extensive experiments on the fine-grained ground-based cloud dataset, demonstrating that CloudSwinNet outperforms six semantic segmentation networks in both qualitative and quantitative evaluations. The results of ablation experiments prove the effectiveness of the module introduced in this paper.

Long-term power forecasting of photovoltaic plants using artificial neural networks

Increasing the robustness of forecasting energy production models from a photovoltaic plant is crucial in investment decisions, while profitability is closely linked to its location since its energy production depends on the local climate conditions. This study aimed to mitigate uncertainty regarding the installation of photovoltaic systems by addressing the challenge of forecasting electrical energy production over the long term. Artificial Neural Network models were used for this purpose, predicting the power output of a photovoltaic plant based on the ambient temperature, cell temperature, and solar irradiance. Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures. Validation was carried out using mean squared error, mean absolute error, mean absolute percentage error, root mean squared error, and coefficient of determination as key metrics. Notably, the long short-term memory network demonstrated the highest accuracy in energy forecasting achieving a mean squared error of 0.0089 p.u, a mean absolute error of 0.0527 p.u, a root mean squared error of 0.0944 p.u, and a mean absolute percentage error of 49.0124 %. Nonetheless, the modular model came close to long short-term memory accuracy but with lower computational cost.

Photovoltaic Solar Farms Site Selection through “Policy Constraints–Construction Suitability”: A Case Study of Qilian County, Qinghai

The scientific selection of photovoltaic (PV) sites is essential for achieving sustainable development of renewable energy and ensuring regional ecological security. In western China, extensive land resources coexist with a fragile ecological environment. To this end, we propose a PV siting framework based on policy restrictions and construction suitability. This paper evaluated the PV construction suitability index (CSI) from four dimensions of topography, climate, location, and ecology and proposed typical “PV+” models. Then, Qilian County was selected as a case study. The results showed the following: (1) In Qilian, 59.97% (8333.18 km2) of the area is unsuitable for development due to policy restrictions, leaving 40.03% (5563.02 km2) available for PV construction. (2) The most suitable areas are approximately in the western and southern areas, where there is a lot in common with the reported PV sites under construction. (3) Three distinct PV development models are proposed according to policy guidelines and local circumstances, including the PV + pastoralism model, PV + mine rehabilitation model, and PV + hydropower model. The results can be used to determine the suitable areas for solar PV farms and the appropriate development model, as well as promote the sustainable development of renewable energy.

Factors and quantitative impact on electrical yield in fishery complementary photovoltaic power plant under different cloud cover conditions

The electrical yield of fishery complementary photovoltaic (FPV) power plants can be self-sustained through aquaculture, offering certain advantages over land-mounted photovoltaic systems. However, electrical yield was affected by environmental factors. The screening of impactful factors and quantification of the relationship between electrical yield and these factors are difficult for FPV power plants because of lacking observation materials. Therefore, electrical yield and its main impactful factors under various cloud cover conditions through in-situ observations were investigated in this study. The results indicated that distinct variations in electrical yield and impactful factors with different clearness index. Downward shortwave radiation (DSR), panel temperature, and vapor pressure deficit (VPD) jointly impact electrical yield. When panel temperature is below 30 °C, electrical yield capacity increases by 10 kWh for every 1 W m⁻2 rise in DSR. Moreover, a 1 kPa rise in VPD results in an approximate 9.50 kWh increase in electrical yield for each 1 W m⁻2 rise in DSR. This study will be crucial for effective electrical yield management planning for aquaculture in FPV power plants.

Interaction of Te(IV) and Te(VI) with the soil matrix – Sorption and fractionation as a function of soil composition

Tellurium is a technology-critical element (TCE), with relatively limited data on its behavior in the environment, especially the pedosphere. As with other TCEs, its more widespread use, especially in new energy sources, might lead to Te spillage during production or in the eventual waste. Investigation of tellurium's interaction with soil is a necessary step in the research into the physiochemical transformation and determining the mobility of different tellurium species. To broaden the hitherto scarce knowledge of tellurium behavior in the soil environment, selected soluble tellurium compounds were introduced into different types of well-characterized soil (content of fertilizers, organic matrix, clay minerals, Mn, Fe, pH). The study of Te(IV) and Te(VI) sorption indicated that after 7 days the sorption is quantitative and close to 100%. Addition of Fe2O3 to a soil deficient in Mn and Fe increases its sorption potential by about 10 percentage points. Based on fractionation study (0.11 mol L−1 CH3COOH, 0.1 mol L−1 ascorbic acid in oxalate buffer (pH 3), 30% H2O2 at 85 °C followed by 0.5 mol L−1 CH3COONH4), it was shown that the presence of Mn/Fe (oxyhydr)oxides plays an essential role in the mobility of Te, especially Te(VI), regardless of the soil type. In the soil poor in reducible fraction and rich in organic matrix (peat), the organic fraction was responsible for the immobilization of Te, especially Te(IV). Extraction of the mobile fraction after incubation in the presence of DI water (Te extraction: 7–8%), oxalic acid (5–7%) or citric acid (6%) (mimicking rhizosphere activity) indicated that these did not play a significant role in Te retention. Nevertheless, soil modification with biocarbon limited the effect of citrates on Te mobilization. This knowledge is fundamental, i.e. in the context of soil remediation processes and counteracting the migration of Te in the environment from anthropogenic sources (e.g. solar farms).

RCPI controller-based multilevel multistring grid following inverter for large rooftop PV power plant application

RCPI controller-based multilevel multistring grid following inverter for large rooftop PV power plant application

Design of distributed photovoltaic power station on the roof of 200 kW industrial plant

Abstract With the depletion of fossil energy and the increasing attention of people to environmental pollution, it was urgent to find clean and non-polluting alternative energy. Solar photovoltaic power generation had the advantages of low cost, long life, no pollution and stability. This project designed a photovoltaic power station on the roof of a 200 kW industrial plant. 360 single-crystal 550W photovoltaic modules were selected, the installation inclination was 20°. Through PVsyst simulation, the total power generation in 25 years was 4.8884 million kWh and the average annual power generation was about 195,500 kWh.

Design of real-time & historical database for photovoltaic power plant

Abstract The real-time & historical database of photovoltaic power plant monitoring system is used to store and manage the real-time and historical data, including power plant parameters, environmental data, and photovoltaic unit data and so on. Analyzing of this data is helpful for monitoring the status of the plant equipment, performing maintenance, and enhancing the operational efficiency of the power plant. The data models and database function of the database is designed. The data models include tags, data processing, and data storage models. Database function include the modules of the collection, storage, compression, and index query. The data collection module utilizes dynamic link libraries to access database data and configures parameters through a comprehensive data collection framework. The storage strategies involve in a combination of in-memory databases and disk-based historical databases, incorporating secondary caching technology and cloud server storage techniques. The data compression module is achieved through secondary compression methods. The index query module encompass time index files and the secondary index files to improve query speed and real-time system performance. The designed elements collectively establish an efficient monitoring system database.

State-of-the-Art of Floating PV Development in the Indonesian Waters

Abstract In recent years, there has been a growing emphasis on reducing Indonesia’s reliance on fossil fuels for electricity generation by promoting the development of renewable energy-based power plants. Among the renewable energy sources, solar energy holds great potential in Indonesia, given its abundant availability throughout the year. This presents favourable prospects for the establishment of solar photovoltaic (PV) power plants, both in ground-mounted and floating sub-structure configurations. However, the deployment of ground-mounted PV plants raises concerns regarding land usage, particularly for large-scale installations. Moreover, the environmental temperature in tropical regions like Indonesia can adversely affect the efficiency of PV panels in land installations. Water bodies offer an alternative location for PV installations to mitigate these challenges, as water can help alleviate the temperature impact on PV panels. Considering Indonesia’s archipelago geography, seas emerge as an attractive option for large-scale PV power plant development. However, numerous challenges arise when implementing floating PV (FPV) technology in open seas. This paper explores the ongoing advancements in marine FPV technology and addresses the challenges associated with installing FPV systems in Indonesian seas. These efforts aim to support the fundamental development of the country’s electricity infrastructure, encompassing aspects such as reliability, affordability, and social acceptability.

Improving primary frequency response in photovoltaic systems using hybrid inertia and intelligent control techniques

Abstract Low inertia poses a significant challenge to the widespread integration of photovoltaic (PV) systems into the grid, particularly affecting frequency stability. This paper addresses this challenge by proposing an advanced hybrid inertia (AHI) approach, characterized by the integration of real and virtual inertia, combined with the application of intelligent control techniques, to enhance the primary frequency response of a PV system. Internal inertia is provided by a synchronous generator (SG) acting as a compensator, while virtual inertia is generated through a real-time deloading strategy in the PV plant. The AHI is implemented on an isolated grid, utilizing various intelligent control techniques to improve frequency stability. The intelligent techniques employed include genetic algorithms (GA) and fuzzy logic controllers (FLC), which do not require mathematical modeling. This allows them to overcome the nonlinear dynamics of the PV plant and the uncertainties associated with climatic changes. These techniques are applied to the nonlinear components on the generator side, including the governor loop, DC-DC converter loop, and power reserve injection management loop. A frequency fluctuation scenario, induced by generating power perturbations in the proposed system, demonstrates and validates the improvement in frequency stability achieved by the HI and intelligent techniques. All tests are conducted using MATLAB Simulink. Various simulation results show a significant improvement in frequency stability through the incorporation of HI and intelligent control techniques.

Suitability Analysis of Solar Photovoltaic Farm Locations Using GIS: A Case Study of Nakuru County, Kenya

Renewable energy sources play a crucial role in reducing global reliance on fossil fuels. Advancements in technology has en- abled harnessing of renewable energy from solar, wind, and ocean tides to be viable. Solar energy, in particular, has gained significant global recognition as a renewable energy alternative. This study integrates Multi-Criteria Evaluation (MCE) and Geographic Information Systems (GIS) to assess Solar Photovoltaic Farms (SPVFs) suitability in Nakuru County, Kenya. The study considered seven criteria including; slope, solar radiation, aspect, land use land cover, proximity to roads, power transmission lines, and settlements. These were evaluated using the Analytical Hierarchy Process (AHP) to generate weights for each decision criterion. The weights were used to overlay independent criteria maps that were formed as a result of the re- classification of each criterion from which a composite rated map was developed. Similarly, a composite restriction map was created by leveraging on constraint criteria including; protected ecosystems, water bodies, settlement areas, slope over 20%, proximity to roads, proximity to the transmission line, and land use land cover. Results obtained from overlaying the composite rated and restricted maps reveal Nakuru County’s general suitability for SPVFs, except for Kuresoi North and Kuresoi South divisions which exhibit low solar radiation. Extremely-suitable areas ac- counted for 3.00% (224.14 km²), very-suitable areas 34.05% (2541.45 km2), moderately-suitable areas 7.76% (579.55 km2), marginally-suitable areas 1.47% (109.77 km2) while least-suitable areas covered 0.02% (1.39 km2). The study provides valuable data and information for government agencies and investors to identify potential Photovoltaic (PV) system sites. Furthermore, the government is encouraged to establish a favorable framework for solar PV exploration and provide incentives to the private sector to facilitate the establishment of SPVFs.