Photovoltaic Plants Research Articles

Compensating energy demand of public transport and yielding green hydrogen with floating photovoltaic power plant

The last three decades have seen a dramatic increase in the renewable energy sector as a result of increased human energy consumption and environmental concerns about fossil fuels. Offshore renewable energy sources are the most alluring and promising technologies because of more energy potential, less space, and visual restrictions than onshore ones. Among those, floating solar photovoltaic (FPV) has a remarkable reputation. The present study focuses on a viable way to replace energy resources derived from fossil fuels with renewable solar energy. In this regard, electrical energy demand is investigated where a floating photovoltaic system and integrated hydrogen production unit are employed on water surface of Yamula Dam. Energy demand of public trams would be compensated with electricity generated by FPV and rest of energy would be utilized for hydrogen production. Key results illustrated that in various scenarios, the energy generation amounts were around 31×106 kW, 32×106 kW, and 39×106 kW, while the energy consumption amounts were approximately 24×106 kW. It was evident that the energy created more than offset the amount consumed. It was also note that the total costs of entire system were $94.1 M, $78.5 M and $71.2 M according to the different cases. It was also observed that in October and November, the remaining energy from the Bozankaya tram produced the most hydrogen with 125 kg, whereas in September and October, the remaining energy from the Sirio tram produced approximately 70 kg.

ANFIS controller for photovoltaic inverter transient and voltage stability enhancement

Grid operations are evolving as a result of the extension of existing huge scope inverter-based power stations and sun oriented (PV) power plants. In light of these alterations, the grid code has been updated to mandate the use of inverter based. In the event of grid failure, power plants should remain online and offer dynamic backup. The term “momentary cessation of operation” is commonly used to describe this characteristic. Very little is known about how PV power plants' temporary shutdowns contribute to the larger issue of unstable power supplies, and very few studies have been published on the topic. While trying to settle this issue, this study offers an ANFIS regulator engineering for PV inverters to work on that ephemeral solidness about coordinated dynamo associated through this network. This examination shows how each direct electric current connect condensers on that solar cell inverter might be utilized to assimilate a piece of the dynamic energy put away in the coordinated machine during the brief delays by utilizing the ANFIS regulator framework introduced. Besides, the recommended approach further develops voltage solidness by infusing responsive power. The viability of the recommended control system is affirmed by the introduced exploratory and reproduced discoveries.

Socio-environmental and technical factors assessment of photovoltaic hydrogen production in Antofagasta, Chile

This study introduces a method for identifying territories ideal for establishing photovoltaic (PV) plants for green hydrogen (GH2) production in the Antofagasta region of northern Chile, a location celebrated for its outstanding solar energy potential. Assessing the viability of PV plant installation necessitates a balanced consideration of technical aspects and socio-environmental constraints, such as the proximity to areas of ecological importance and indigenous communities, to identify potential zones for solar and non-conventional renewable energy (NCRE)-based hydrogen production.To tackle this challenge, we propose a methodology that utilizes geospatial analysis, integrating Geographic Information System (GIS) tools with sensitivity analysis, to determine the most suitable sites for PV plant installation in the Antofagasta region. Our geospatial analysis employs the QGIS software to identify these optimal locations, while sensitivity analysis uses the Sørensen–Dice coefficient method to assess the similarity among chosen socio-environmental variables.Applying this methodology to the Antofagasta region reveals that a significant area within a 15 km radius of existing road networks and electrical substations is favorable for photovoltaic projects. Our sensitivity analysis further highlights the limiting effects of socio-environmental factors and their interactions. Moreover, our research finds that enlarging areas of socio-environmental importance could increase the total hydrogen production by about 10% per commune, indicating the impact of these factors on the potential for renewable energy production.

Exploring a path of vegetation restoration best suited for a photovoltaic plant in the Hobq desert

To investigate the soil improvement effects of different vegetation restoration measures during the operation and maintenance of photovoltaic power plants in the Hobq Desert. This study determined the soil grain size composition and soil nutrient content of 0–5 cm under four vegetation restoration measures and within the mechanical sand barriers by laser diffraction techniques and chemical experiments. The results showed that: (1) Soil particle size composition in the study area is predominantly sandy, with 1.02%, 6.63%, 5.34% and 2.61% less sand in Leymus chinensis (YC), Glycyrrhiza uralensis (GC), Artemisia ordosica Krasch (YH) and Hedysarum scoparium (HB), respectively, compared to mechanical sand barriers (CK). Soil particle distributions of YC, GC, and HB showed better sorting and more concentrated patterns, and all four vegetation restoration measures had higher fractal dimensions than CK. (2) AN (Alkali-hydrolyzable Nitrogen) content performance: The content of YC, GC, and HB was significantly higher than that of YH and CK, AP (Available Phosphorus) content did not differ significantly between measures, AK (Available Potassium) content was higher than that of CK in all measures, with YC content being the highest and SOM content being the highest for GC. (3) Soil total nutrient is unstable across vegetation restoration measures. Overall, the performance showed that the three vegetation restoration measures, YC, GC, and HB, were more evident in the soil total nutrient content improvement. Each particle size characteristic parameter and soil nutrient response vegetation restoration measures ameliorate sandy soil in PV power plants during operation and maintenance. YC and GC have the most apparent effect on soil amelioration.

OPTIMAL TOPOLOGY OF ELECTRIC CIRCUITS IN PV-PANELS AND PV-PLANTS WITH USING CONTROLLED CONNECTIONS

The article considers the topology of electric circuits of photovoltaic panels and photovoltaic plants. The existing principles of connection are considered, advantages and disadvantages are analyzed. A parallel-serial connection cell for connecting two power sources is proposed. It is possible to implement parallel connection, serial connection and shunt, simultaneously, in one device. Switching in this cell is controlled and dynamic. The topology of combining a large number of photovoltaic devices is demonstrated. A topological structure in the form of a tree is used. The advantages and disadvantages of this approach are shown. A modern technological approach to the implementation of such a controlled system is proposed. Conclusions are made.

Research on Land-Based Wind/Solar Power Station Site Deformation Monitoring Based on SBAS-InSAR Technology

INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability. 
 OBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County. 
 METHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results. 
 RESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay. 
 CONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.

Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO’s exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.

The total factor characteristics evaluation of photovoltaic power by coarse-fine-grained method

The study of photovoltaic(PV) power uncertainty is of great significance to promote the collaborative optimization of source-grid-load-storage. On the one hand, using a single probability distribution model to describe the fluctuation, intermittency and randomness of PV power is limited and controversial; On the other hand, qualitative analysis oriented to only one or two of them is characterized with poor interpretability and one-sidedness. So that, a evaluation framework for the total factor characteristics named "dual-DMU" is established corresponding to the discrete, mutation and unordered phenomenon of PV power, and a coarse-fine-grained evaluation method is proposed, which can realize the comprehensive description and all-sided evaluation of PV power. Combined with the historical data of a rooftop PV power plant, the analysis reveals the variability of total factor characteristics in different seasons, weather types, and typical time periods, and the methodology can provide a theoretical basis and decision-making support for the construction of PV power forecasting models, energy storage capacity optimization, and adjustable load resource allocation.

Fostering Sustainability through the Integration of Renewable Energy in an Agricultural Hydroponic Greenhouse

This research explores the feasibility of integrating renewable energy sources, such as solar and wind, to power a hydroponic greenhouse. In this way, the latter’s energy autonomy is ensured. The study begins by evaluating the annual electricity consumption of the examined system. A renewable energy system capable of meeting its energy requirements throughout the year is also designed. The main objective is to assess the efficiency of two types of renewable energy sources, namely photovoltaic panels and wind turbines, and to improve their integration within the agricultural chamber by implementing a model simulation. Two scenarios were examined: the first one represents a photovoltaic power plant with storage, connected to the grid, while the second scenario presents a wind power plant connected to the grid. This numerical analysis is supplemented by a one-year experimental study of a photovoltaic installation connected to the network with storage, which in turn is connected to the experimental device. To handle energy within the renewable energy greenhouse, an energy management system was developed based on a fuzzy logic controller. This system aims to maintain energy balance and ensure continuous power supply. The energy management system optimizes energy flow to minimize consumption, reduce grid dependence, and improve overall system efficiency, resulting in cost savings and certain environmental benefits.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

An indicator-based approach to assess and compare the environmental and socio-economic consequences of photovoltaic parks in Romania's development regions

An indicator-based approach to assess and compare the environmental and socio-economic consequences of photovoltaic parks in Romania's development regions

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.

Feasibility analysis and modeling of a solar hybrid system for residential electric vehicle charging

The process of transforming sunshine energy into electrical power is known as solar power generation. Photovoltaic (PV) technology has recently proved its cost-effectiveness and low environmental impact in generating power. The key goals of this study are to develop a solar PV system for charging electric vehicles (EVs) while utilizing the residential apartment's current domestic power supply. This study focuses on modeling grid-interactive solar PV systems for charging EVs inside a 40-unit residential apartment complex. The Solar Pro tool is used to do the techno-economic analysis of the modeled PV system. The research investigates the installation of a rooftop solar plant devoted to delivering electricity to EV charging devices on a real-time five-story residential building. The performance of the PV plant is tested under a variety of scenarios, including EV loading, shadow mapping, and local meteorological conditions. The PV plant's size is optimized at 150 kW, taking into consideration economic aspects as well as the actual proportions of the structure. In addition, the MiPower tool is used to do a load flow study of the modeled system, which includes both the grid and the PV system. This research evaluates line losses, line loading, and voltage levels at each bus at maximum loading circumstances.

Development and verification of a discrete event simulation tool for high-fidelity modelling of offshore wind and solar farm decommissioning campaigns

The number of offshore wind turbines to be decommissioned is expected to grow in the next decade but some of the oldest wind farms in the North Sea region are already approaching the end of their service lives. This means that several detailed decommissioning plans will need to be prepared in the coming 2-5 years. So far, only small-scale wind farms consisting of relatively small turbines located in shallow, near-shore, or confined waters have been decommissioned. The limited experience and data available yield high uncertainty in the estimation of costs, duration, and environmental footprint of upcoming offshore wind farm decommissioning campaigns. High-fidelity logistic simulation tools as well as high-level cost models are both needed to support end-of-life decision-making. While several holistic cost models have been proposed, the literature lacks high-fidelity models to support the detailed planning and execution of offshore wind farm decommissioning projects. Given that offshore operations are the greatest source of costs, emissions, and safety risks in decommissioning campaigns, these require special attention when seeking optimization opportunities. In this work, a high-fidelity logistic simulation module for the decommissioning of offshore wind and solar farms has been developed within the discrete event simulation platform UWiSE. The new UWiSE Decommission module enables detailed assessment of alternative decommissioning scenarios taking into account offshore operation complexities like weather and resource dependencies, which are not included or heavily approximated in holistic cost models. The new tool has been verified against publicly available analytical cost model results from recent literature. A short assessment of the impact of weather delays on cost and duration estimates for the considered offshore wind farm decommissioning campaign has also been performed as an example of how UWiSE Decommission may bring value to the planning of offshore decommissioning projects.

Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

The uncertainty of weather conditions has always been a major challenge limiting the performance of photovoltaic (PV) power prediction. Enhancing the accuracy and stability of PV power prediction is crucial for optimizing grid operation. In response to this challenge, this study introduces a hybrid model that integrates an attention mechanism with the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). This model aims to mitigate the adverse impact of weather variability on the accuracy of PV power prediction by effectively extracting key features from multidimensional time series data. Additionally, through ablation experiments, this research further assesses the contribution of each model component to performance. Validated with actual data collected from a 1 MW PV power station in China, our proposed model demonstrates significant performance advantages compared to eight advanced prediction models. Ablation study results reveal that removing the CNN component led to a 58.2% increase in MAE and a 53.9% increase in RMSE, while the removal of the attention mechanism resulted in an 83.8% increase in maximum error. These findings underscore the substantial enhancement in prediction accuracy achieved through the integration of CNN and BiLSTM, and the introduction of the attention mechanism significantly boosts the model's prediction stability.

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.

Large-Scale Photovoltaic Farms in the Qinghai Desert: Soil Quality Assessment Methods

Large-Scale Photovoltaic Farms in the Qinghai Desert: Soil Quality Assessment Methods

Event tree-based risk and financial assessment for power plants

Reliability and risk analyses in engineering have seldom been linked to financial analyses despite the significant role that risk assessment plays in the field of finance. Cash flow analyses for power plants typically do not account for contingent and successive events. Hence, the related financial assessments rarely incorporate a quantification of the adverse effects resulting from the combination of subsequent failures that could lead to extreme outcomes. The proposed approach aims to address this gap by employing the event tree method.The novel contribution lies in introducing an event tree-based method that enables a granular evaluation of the financial performance of power plants and can serve as a foundation for the development of a variety of sensitivity and optimization analyses. The method enhances the accuracy of financial performance estimates by specifically targeting the assessment of operation and maintenance costs. The results show the importance of an improved assessment of the expenses related to equipment failures, which are approximately one-third of the revenue and add up to around 380,000 EUR for the lifespan of а photovoltaic plant with an installed capacity of 1 MW.

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.

Monitoring system for a self-consumption photovoltaic system

Climatic conditions and temperature levels often affect the infrastructure of photovoltaic systems, causing the scheduled generation to not be as expected. The objective was to design a monitoring system for an experimental 3.4 kWp photovoltaic plant that is located on the terrace of building 1 of the Faculty of Engineering and Applied Sciences of the Technical University of Manabí. An automation process was developed to optimize the performance of the installation, using an innovative perspective, the study determinedly faces the initial challenges associated with the software previously used for the design of a data storage system when incorporated into the institutional geoportal. The successful implementation of the Oxley Solar mobile application and, consequently, of the PZM-0043 module, emerges as a comprehensive solution that ensures the continuity of monitoring and contributes significantly to improving the efficiency of the photovoltaic system. The Oxley Solar app overcomes previous limitations by enabling efficient data extraction via Bluetooth and subsequent transmission via Wi-Fi, facilitating more effective storage. The result was the introduction of the PZM-0043 module that adds a layer of automation to the system, guaranteeing continuous data transmission to the database to be stored in the geoportal.