Photovoltaic Plants Research Articles

Operating Limits and Control Variables in Photovoltaic Solar Plants with a Net Effective Capacity of 5 MW Connected to the SIN in Colombia

Operating Limits and Control Variables in Photovoltaic Solar Plants with a Net Effective Capacity of 5 MW Connected to the SIN in Colombia

Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies

Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies

Coordinated control algorithm of hydrogen production-battery based hybrid energy storage system for suppressing fluctuation of PV power

Photovoltaic (PV) power generation has issues of volatility and intermittency. Currently, PV plants are generally equipped with 10% rated capacity lithium-ion (Li) battery energy storage systems in China, who often fail to suppress fluctuation in the output power of PV plants effectively and meet the grid-connected standard. The hybrid energy storage system (HESS) combining with hydrogen production and Li battery system can produce hydrogen by water electrolysis during the peak period of PV power generation, effectively improving PV utilization efficiency, while smoothing PV power fluctuation and improving grid connection electricity quality. Firstly, models of the solid oxide electrolysis cell (SOEC) and alkaline electrolysis cell (AEC) systems for hydrogen production, and Li battery energy storage system are established, and the transient response characteristics of each system are analyzed. Secondly, an adaptive wavelet packet decomposition (AWPD) method for PV power signal decomposition is proposed based on the wavelet packet decomposition (WPD) method. Thirdly, a capacity configuration method for HESS are proposed based on the APWD method. Fourthly, a coordinated control strategy for HESS is proposed with the transient response characteristics of different energy storage systems and the state of charge for Li battery system. Finally, the proposed method is validated through simulation experiments based on the actual power data of the PV plant. The results show that the developed methods can effectively utilize partial PV power generation to produce hydrogen, improve PV utilization, and the combined output power of PV plant and HESS can fulfill the grid-connected standard.

Multi‐Criteria Decision Analysis‐Based Solutions for the Installation of Photovoltaic (PV) Solar Power Plants in an Energy Deficit State of India: An Effort Toward SDG‐7 (Affordable and Sustainable Energy)

ABSTRACTSustainable Development Goal‐7 (SDG‐7) of the United Nations promotes the use of renewable and affordable energy. Solar energy holds significant promise as an effective and affordable renewable source in tropical countries. GIS‐based Multi‐Criteria Decision Analysis (MCDA) approach has been applied to evaluate land suitability for installing ground‐mounted and grid‐connected solar photovoltaic power plants in Purulia district of West Bengal (India) to satisfy the growing demand of energy in an eco‐friendly manner. The study reveals that variation in Global Horizontal Irradiance (GHI) is the most relevant parameter for installing solar PV power plants in the district among the 13 physical parameters, followed by the aspect, slope, and proximity to the grid. The site‐suitable map created using the weights of these factors has revealed that about 33.4% of the district's total land area is ideal for installing solar PV power plants, with most of these areas being in the western and central regions.

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

Design Of PV Plant to Improve a Voltage Profile Of 11 KV Grid in The South West of Libya

The use of photovoltaic (PV) system for electricity generation is growing up in the last decades. Thesouthern part of Libya suffers from voltage fluctuation and intermittence of electricity during thesummer time. The aim of this paper is firstly to study and analysis a distribution grid of small villagelocated in the southern west region of Libya. We analyzed the existed 11KV distribution overheadline supplied electricity of the village called Takarkibah using Power-World Simulator in order tointegrate it with PV plant. The results obtained show that injection of PV power about 70 % of thegrid power capacity gives good grid performance. After that, we use SAM simulation program todesign PV plant to improve the voltage profile in the distribution grid. Six designs (scenarios) aresimulated using different solar modules and inverters. The simulation results obtained from SAMillustrated the power capacity, energy yield, and losses of the designed PV plant.

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Long-term performance analysis of a large-scale photoVoltaic plant in extreme desert conditions

This study comprehensively evaluates the performance and operational challenges of a 9 MW grid-connected photovoltaic (PV) system in Timmimoun, southern Algeria, after eight years of operation. We identified a significant linear correlation (R2 = 0.73) between increased ambient temperatures and decreased PV module efficiency, underscoring the need for effective thermal management strategies. The investigation also examines potential solutions, such as cooling mechanisms, optimization of panel orientation and tilt angles, and development of temperature-resistant materials. Adhering to the International Energy Agency IEC 61724 standard, our analysis reveals a yield factor of 5.04 h/day, a yield ratio of 7.04 h/day, a performance ratio of 73 %, and a capacity factor of 21 % in 2023. Over its operational lifespan, the system has generated 133.43 GW h of energy, mitigating 1.01 105 tons of CO2 emissions. This long-term study provides critical insights into the performance and reliability of PV systems in hot desert climates, offering valuable guidance for future large-scale solar installations and contributing to the transition towards a sustainable energy future.

Iterative sizing methodology for photovoltaic plants coupled with battery energy storage systems to ensure smooth power output and power availability

Photovoltaic (PV) solar energy is a fundamental technology that will help transition from a fossil fuel–based energy mix to a future with high shares of renewable energy. To do so, PV plants coupled with energy storage systems can accumulate excess power and dispatch it when PV generation changes, performing PV smoothing. While coupling PV plants with battery energy storage systems (BESS) offers a solution, current methodologies often need to thoroughly describe the interplay between BESS energy capacity, power rating, and the long–term impacts of battery degradation. This paper addresses this gap by proposing a four–step methodology that optimizes BESS sizing for PV plants, accounting for both cycling and calendar aging effects on system performance and the economic implications of battery replacements. We use a model that considers the degradation of PV modules and two Li–ion battery types (LiFePO4, LFP, and LiNiMnCoO2, NMC). A 16.3 MW PV plant is simulated along with the BESS to test the methodology. The results indicate that an LFP–based BESS of 2.5 MWh with a rated power of 1.25 MW ensures a stable output power with variation below 10% of the rated PV plant 98 % of the time. In contrast, an NMC–based BESS, while effective in reducing non–compliance, incurs higher costs due to more frequent battery replacements, making it less economically viable. The methodology and results presented in this paper provide valuable insights for designing cost–effective and reliable energy storage solutions in PV plants, ensuring compliance with set power availability.

Potentials of Green Hydrogen Production in P2G Systems Based on FPV Installations Deployed on Pit Lakes in Former Mining Sites by 2050 in Poland

Green hydrogen production is expected to play a major role in the context of the shift towards sustainable energy stipulated in the Fit for 55 package. Green hydrogen and its derivatives have the capacity to act as effective energy storage vectors, while fuel cell-powered vehicles will foster net-zero emission mobility. This study evaluates the potential of green hydrogen production in Power-to-Gas (P2G) systems operated in former mining sites where sand and gravel aggregate has been extracted from lakes and rivers under wet conditions (below the water table). The potential of hydrogen production was assessed for the selected administrative unit in Poland, the West Pomerania province. Attention is given to the legal and organisational aspects of operating mining companies to identify the sites suitable for the installation of floating photovoltaic facilities by 2050. The method relies on the use of GIS tools, which utilise geospatial data to identify potential sites for investments. Basing on the geospatial model and considering technical and organisational constraints, the schedule was developed, showing the potential availability of the site over time. Knowing the surface area of the water reservoir, the installed power of the floating photovoltaic plant, and the production capacity of the power generation facility and electrolysers, the capacity of hydrogen production in the P2G system can be evaluated. It appears that by 2050 it should be feasible to produce green fuel in the P2G system to support a fleet of city buses for two of the largest urban agglomerations in the West Pomerania province. Simulations revealed that with a water coverage ratio increase and the planned growth of green hydrogen generation, it should be feasible to produce fuel for net-zero emission urban mobility systems to power 200 buses by 2030, 550 buses by 2040, and 900 buses by 2050 (for the bus models Maxi (40 seats) and Mega (60 seats)). The results of the research can significantly contribute to the development of projects focused on the production of green hydrogen in a decentralised system. The disclosure of potential and available locations over time can be compared with competitive solutions in terms of spatial planning, environmental and societal impact, and the economics of the undertaking.

Analysis of the efficiency of a Solar Photovoltaic Power Plant linked to the electrical grid

The growing need for energy in emerging nations gives rise to apprehensions over energy security, so underscoring the criticality of harnessing the potential of renewable resources. Empirical evidence has demonstrated that photovoltaic systems integrated into the network are the optimal options for large-scale renewable energy. The analysis of the performance of these network-connected centrals can enhance the design, operation, and maintenance of new systems. The commissioning of a 12 MW photovoltaic solar power plant in Sidi Bel Abbès, located in the Dhaya region, has established it as one of the largest solar power plants in Algeria. The site yields a mean solar radiation of 5.21 kilowatt-hours per square metre per day and an average yearly temperature of 18.9 degrees Celsius. The present work provides an analysis of the yearly performance and conceptual aspects of the photovoltaic central unit. We compare the performance outcomes of the power plant with the simulated values generated by the SolarGis PvPlanner program. Analysis of real-world performance in relation to simulations allows for the detection of inconsistencies and the enhancement of future initiatives. SolarGis PvPlanner is an essential modelling program for accurately forecasting performance and effectively strategising solar projects, therefore guaranteeing sustainable and dependable energy for the future.

Cost of Green Hydrogen

Acting in accordance with the requirements of the 2015 Paris Agreement, Poland, as well as other European Union countries, have committed to achieving climate neutrality by 2050. One of the solutions to reduce emissions of harmful substances into the environment is the implementation of large-scale hydrogen technologies. This article presents the cost of producing green hydrogen produced using an alkaline electrolyzer, with electricity supplied from a photovoltaic farm. The analysis was performed using the Monte Carlo method, and for baseline assumptions including an electricity price of 0.053 EUR/kWh, the cost of producing green hydrogen was 5.321 EUR/kgH2. In addition, this article presents a sensitivity analysis showing the impact of the electricity price before and after the energy crisis and other variables on the cost of green hydrogen production. The large change occurring in electricity prices (from 0.035 EUR/kWh to 0.24 EUR/kWh) significantly affected the levelized cost of green hydrogen (LCOH), which could change by up to 14 EUR/kgH2 in recent years. The results of the analysis showed that the parameters that successively have the greatest impact on the cost of green hydrogen production are the operating time of the plant and the unit capital expenditure. The development of green hydrogen production facilities, along with the scaling of technology in the future, can reduce the cost of its production.

Application of three Transformer neural networks for short-term photovoltaic power prediction: A case study

In order to solve the potential safety hazards caused by the fluctuation of photovoltaic (PV) power generation, it is necessary to predict it in advance and take countermeasures as soon as possible. Based on the three models of vanilla Transformer, Informer, and Autoformer, this paper considers three prediction scenarios: zero-cost prediction, low-cost prediction, and high-cost prediction, and realizes the power prediction under two prediction horizons of 4 h and 24 h for a matrix of a centralized PV power station in Hubei Province, China. The results of some configurations meet the industry-recommended metric requirements, and the overall performance of the vanilla Transformer is better than Informer and Autoformer. After comparing the three models and different prediction intervals, and considering the practical industrial demand, this paper gives recommended configurations for both 4 h and 24 h predictions. The practical rolling prediction performance of the recommended configurations demonstrates the applicability and flexibility of the proposed methods.

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.