Photovoltaic Generation Research Articles

A twenty-year dataset of hourly energy generation and consumption from district campus building energy systems

Distributed energy resources (DERs) would play a crucial role in the transition towards decentralized and decarbonized energy systems. However, due to the limited availability of long-term, high-resolution datasets, there has been little research on the descriptive analysis of distributed energy systems throughout the lifespan of distributed power generators and beyond. To address this challenge, this study has collected and described a twenty-year dataset (from 2002 to 2021) consisting of hourly energy generation and consumption profiles from a campus distributed energy system, covering the entire lifespan of on-site generators. The dataset includes supply-side data such as gas consumption from combined heating and power (CHP) units (fuel cell, gas engine), absorption chiller, gas boiler, solar photovoltaic generation, CHPs output, and grid electricity import. Additionally, real-life electricity, hot water, space heating, and cooling energy load profiles from individual buildings within the campus were also collected. This long-term dataset can be utilized in various scenarios, providing researchers and policymakers with comprehensive insights into the energy efficiency of distributed energy systems.

The Current Situation and Development Trends of the Solar Photovoltaic Industry in China and the United States

Climate change and environmental pollution have made clean energy increasingly valued, among which solar photovoltaic power generation occupies a pivotal position. In terms of the development of clean energy in the world today, major countries have mastered the core technology and occupied the main installed power generation capacity. China and the United States, the two major energy powers in the world, are also the major powers in the world today. They have completely different political systems and economic policies, and there are also many differences in the development of the solar photovoltaic power generation industry. This paper will conduct an in-depth comparative analysis of the development of the solar photovoltaic industry in China and the United States from the aspects of policy environment, key enterprises, development trends, etc., to more clearly reveal the differences and causes of the development of the solar photovoltaic industry between the two countries, and provide references for future cooperation and competition.

Spatio-Temporal Photovoltaic Power Prediction with Fourier Graph Neural Network

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information.

Short-Term Photovoltaic Power Forecasting Using PV Data and Sky Images in an Auto Cross Modal Correlation Attention Multimodal Framework

The accurate prediction of photovoltaic (PV) power generation is crucial for improving virtual power plant (VPP) efficiency and power system stability. However, short-term PV power forecasting remains highly challenging due to the significant impact of weather changes, especially the complexity of cloud motion. To this end, this paper proposes an end-to-end innovative deep learning framework for data fusion based on multimodal learning, which utilizes a new auto cross modal correlation attention (ACMCA) mechanism designed in this paper for feature extraction and fusion by combining historical PV power generation time-series data and sky image data, thereby enhancing the model’s prediction performance under complex weather conditions. In this paper, the effectiveness of the proposed model was verified through a large number of experiments, and the experimental results showed that the model’s forecast skill (FS) reached 24.2% under all weather conditions 15 min in advance, and 24.32% under cloudy conditions with the largest fluctuations. This paper also compared the model with a variety of existing unimodal and multimodal models, respectively. The experimental results showed that the model in this paper outperformed other benchmark methods in all indices under different weather conditions, demonstrating stronger adaptability and robustness.

Bagging ensemble of artificial neural networks with weighted averaging and grey wolf optimization for solar photovoltaic power generation prediction

ABSTRACT Solar power generation is highly uncertain and intermittent due to its strong dependence on climate and meteorological factors. In this study, we have proposed a bagging ensemble of artificial neural network (BagANN) model with weighted averaging optimized using grey wolf optimization (GWO) and investigate the impact of climatic and meteorological factors on the output power prediction of the solar power plant. Initially, we have preprocessed the data by applying feature engineering, normalization techniques, and appropriate features are selected for power prediction. The hyperparameters of the BagANN model are tuned and optimized using the GWO technique to improve the models predictive accuracy. Experiments are conducted to evaluate the generalization performance of the developed model using five-fold and 10-fold cross-validation techniques. The performance results are assessed using RMSE, coefficient of determination ( R 2 ), adjusted ( R 2 ), and the MAPE. Results show that the BagANN with weighted averaging has a higher level of precision with R 2 of 98.5% predictive accuracy, has less prediction errors with 0.1263 MSE and 0.0267 MAPE than the other developed models. From the result, it is evident that the BagANN model has significant improvements compared to other models available in the literature in terms of robustness of the prediction and predictive accuracy.

Advancements in Preventing Sn2+ Oxidation in Tin‐Based Perovskite Solar Cells: A Review

Materials for perovskite solar cell (PSC) are being developed as possible contenders for the upcoming photovoltaics generation. However, despite their high efficiency, perovskite materials containing lead are not suitable for commercialization due to their toxic nature. As a result, tin (Sn)‐based perovskites have emerged as a promising alternative. Tin‐based perovskites possess similar ionic sizes to lead and exhibit exceptional light absorption properties. Nevertheless, these materials are hindered by the oxidation from Sn2+ to Sn4+, which results in poor stability and suboptimal conversion efficiency. This review provides a comprehensive exploration of the oxidation mechanism of Sn2+ and presents an in‐depth discussion of recent advancements in various strategies aimed at preventing this oxidation in FASnI3 PSCs.

Opinions on dynamic topology reconfiguration of distribution networks for PV hosting capacity enhancement

This paper investigates on dynamic topology reconfiguration of distribution networks to enhance PV hosting capacity. Firstly, a dynamic topology reconfiguration model of distribution networks considering N-1 security constraints is proposed to accommodate intermittent PV generation, and thus high penetration of renewable resources can be ensured to satisfy operational requirements under both normal and fault conditions with load transfer of feeders. Then, a hosting capacity enhancement strategy is presented to decrease risks of voltage and line overloading with reactive power flexibility of energy storage inverters, and is implemented to enhance the maximum hosting capacity of distributed renewable energy sources. Finally, simulation results have validated the effectiveness of the proposed method for PV hosting capacity enhancement of distribution networks.

Research on Fault Prediction and Health Management of Grid-Connected Inverter based on Data Drive

Grid-connected inverter is the core component of photovoltaic power generation system, and its stable operation is very important to the overall performance of the system. However, the inverter is easily influenced by environmental factors and its own complexity, which leads to frequent failures and affects power generation efficiency and power grid stability. Firstly, an innovative fault prediction model of grid-connected inverter is constructed by using machine learning and deep learning technology. The prediction accuracy and robustness of the model are improved by integrating the prediction results of basic learners such as decision tree and support vector machine (SVM). The experimental results show that the integrated learning model achieves an accuracy of 0.95 and a F1 score of 0.94 under the optimal parameter configuration. At the same time, the Long Short Term Memory Network (LSTM) model is introduced to effectively capture the complex nonlinear relationship and time dependence in the data, which further improves the prediction performance, and the highest accuracy rate can reach 0.96. Based on the fault prediction model, a comprehensive health management strategy of grid-connected inverter is formulated in this paper. The strategy framework includes four links: data collection and monitoring, fault prediction and evaluation, preventive maintenance plan formulation, fault early warning and response. By monitoring the key parameters of the inverter in real time and comparing with the fault prediction model, the early warning and timely response of the fault can be realized. The experimental results show that after the implementation of health management strategy, the failure rate of inverter is significantly reduced, the response time of fault warning is significantly shortened, and the operation reliability is significantly improved.

Design of Comprehensive Monitoring on Hybrid Photovoltaic and Thermoelectric Generator Using IoT

The study aims to design and develop a more efficient measurement monitoring system based on influential parameters for the performance of hybrid PV and TEG modules using IoT with the Thingspeak application. The parameters measured include the PV top and bottom surface temperatures and output current and voltage, the surface temperatures of the TEG's hot and cold sides, their respective current and voltage outputs, air humidity, and solar intensity. This IoT-based monitoring system experimental method utilizes two types of PV, polycrystalline and monocrystalline, each rated at 50 Wp, in a hybrid configuration with 5 TEG modules attached to the back of the solar panel. The monitoring design results indicate that sensor measurements were accurate and data readings were reliable. The temperature difference between the two sides of the TEG was measured up to 21.7°C, and the hybrid efficiency of the monocrystalline PV with TEG showed better results compared to the polycrystalline setup, achieving optimum efficiency above 6%, with the PV surface temperature maintained at an average of 50°C. Additionally, IoT monitoring revealed the effect of air humidity on TEG performance: lower air humidity resulted in a larger ?T, peaking at 21.7°C with humidity at 39.1%. Therefore, this study recommends using IoT technology for observational data collection on system performance, particularly for PV and TEG, with sensor component modifications tailored to the characteristics of the targeted sensor object.

Quasi‐Z‐Source Cascaded Multilevel Inverter With Master‐Division Control for Photovoltaic Grid Connection

ABSTRACTThe quasi‐Z‐source cascaded multilevel inverter (qZS‐CMI) can achieve the boost function through the shoot‐through state without the requirement of an additional DC boost circuit. Thus, the efficiency of the system is improved, and each power module can be controlled independently, which makes it more suitable for distributed photovoltaic power generation systems. This paper proposes a multi‐carrier phase‐shifted PWM (MPSPWM) that inserts a shoot‐through signal at the switching moment, in order to address the high switching frequency problem caused by simple boost modulation in qZS‐CMI. In order to improve the dynamic response of the system and reduce the grid‐connected current distortion of qZS‐CMI, this paper proposes a master‐division grid‐connected control strategy. The DC‐link voltage division controllers use the sliding mode control to achieve DC‐link voltage stabilization, and the grid‐connected current master controller adopts an improved deadbeat control to achieve fast tracking of grid‐connected current. When the output power of PV arrays is inconsistent, the differential power allocation between modules is realized by setting the module power allocation factor. Finally, the effectiveness and feasibility of the above theory are verified by three‐module cascaded qZS‐CMI simulation and experiment.

Scientific production on environmental impacts in photovoltaic energy generation: a systematic review of publications from 2012 to 2022

This study aimed to the scientific production on the environmental impacts of photovoltaic energy from 2012 to 2022. It employed a qualitative and exploratory approach, utilizing bibliographic, systematic, and bibliometric research methods. The analysis focused on articles available in Capes journals that included the keywords "photovoltaic" and "environmental impacts" in their content, as well as those with full-text availability on the platform. A total of 22 articles were individually evaluated, and information was cross-referenced between them. Manual analysis of the full texts revealed that while environmental impacts were not the central theme of the research, they were present in other areas, particularly in analyses of financial viability or studies on the efficiency of photovoltaic systems. This study contributed to the understanding of the main central themes researched in relation to photovoltaic energy and how the environmental impacts caused by it are addressed in studies on solar energy, but mainly it identified the need for studies that centrally address these impacts.

ST-YOLO: A defect detection method for photovoltaic modules based on infrared thermal imaging and machine vision technology.

Photovoltaic panels are the core components of photovoltaic power generation systems, and their quality directly affects power generation efficiency and circuit safety. To address the shortcomings of existing photovoltaic defect detection technologies, such as high labor costs, large workloads, high sensor failure rates, low reliability, high false alarm rates, high network demands, and slow detection speeds of traditional algorithms, we propose an algorithm named ST-YOLO specifically for photovoltaic module defect detection. This algorithm is based on YOLOv8s. First, it introduces the C2f-SCconv convolution module, which is based on SCconv convolution. This module reduces the computational burden of model parameters and improves detection speed through lightweight design. Additionally, the Triplet Attention mechanism is incorporated, significantly enhancing detection accuracy without substantially increasing model parameter computations. Experiments on a self-built photovoltaic array infrared defect image dataset show that ST-YOLO, compared to the baseline YOLOv8s, achieves a 15% reduction in model weight, a 2.9% improvement in Precision, and a 1.4% increase in mAP@0.5. Compared to YOLOv7-Tiny and YOLOv5s, ST-YOLO also demonstrates superior detection performance and advantages. This indicates that ST-YOLO has significant application value in photovoltaic defect detection.

Systematic Analysis Based on the Optimal Design of Photovoltaic Inverter Circuit

As a clean and renewable energy source, solar energy is of great significance to reduce greenhouse gas and harmful gas emissions, stabilize energy supply, promote industrial innovation, and help achieve sustainable development and carbon neutrality goals. In this paper, the structure and working principle of photovoltaic cells are introduced, and the characteristics of solar photovoltaic cells and thin film photovoltaic cells are compared and analyzed. Then, the application scenarios of solar photovoltaic power generation and thin film photovoltaic power generation are compared, and the future development direction of photovoltaic cells is prospected. Finally, the structure and working mechanism of the inverter circuit are introduced, some problems in the current photovoltaic inverter circuit are summarized, and then some optimization design of photovoltaic inverter circuit based on the maximum power point tracking technology and leakage current related problems are analyzed, and suggestions on how to optimize the design of photovoltaic inverter circuit are given.

Contrasting responses of soil bacterial and fungal networks to photovoltaic power station.

The rapid expansion of solar photovoltaic (PV) power generation raises concerns regarding its impact on terrestrial ecosystems. Although the influence of PV panels on soil conditions and plant biomass is acknowledged, their effects on the assembly processes and co-occurrence networks of soil microbial communities remain understudied. Clarifying this influence is crucial for understanding the effects of photovoltaic panels on soil ecosystem functions. In this study, we first explored the effects of PV panels on soil properties. Then, using amplicon sequencing, we analyzed the impact of PV panels on soil microbial diversity and function, focusing specifically on the assembly processes and co-occurrence networks of bacterial and fungal communities. Our results indicate that the installation of PV panels improved soil conditions, leading to concurrent effects on microbial community structure and function. This process appears to be deterministic, driven primarily by homogeneous selection. Notably, PV panels increased the complexity of bacterial networks while decreasing their stability. In contrast, PV panels did not affect the complexity of fungal networks despite their stability increased. These findings provide new evidence that soil bacterial networks are more sensitive to PV panels installation than fungal networks, deepening our understanding of land-use change effects on soil ecosystem functions. Moreover, our study demonstrates that higher complexity does not necessarily mean higher stability at least in soil microbial systems, challenging the notion that ecological complexity favors their stability.

Research on harmonic suppression method of SPWM inverter circuit for photovoltaic power generation system

To effectively reduce and suppress the harmonics generated by single-phase inverter circuits, this paper mainly investigates the harmonic characteristics of single-phase Sinusoidal Pulse Width Modulation (SPWM) inverter in the output voltage and its suppression methods. According to the SPWM principle and Fourier series theory, the mathematical model and simulation of the output voltage of the SPWM inverter are established, and the harmonic characteristics of the SPWM inverter are analyzed. Secondly, the harmonic content and distribution of the output voltage under three different carrier control methods are investigated. Then, two effective methods for suppressing the harmonics of the output voltage of single-phase SPWM inverters are investigated: selecting the appropriate carrier ratio and injecting the proper number of harmonics. Finally, the study shows that a single-phase unipolar frequency doubling SPWM inverter can effectively filter out the high harmonics, making the output voltage waveform smoother and closer to the sinusoidal waveform. This study will have a certain positive impact on the future development of energy-saving power supply, high-quality power supply, and high-performance power supply technology in China, which can realize the efficient and low-pollution use of electric energy.

A STAND-ALONE HYBRID POWER SYSTEM BASED ON PV ENERGY AND HYDROGEN FUEL CELLS WITH ENERGY STORAGE SYSTEMS

This article is focused on the construction of a stand-alone residential 5-kW hybrid power system to feed different domestic loads at a typical house in Thi-Qar City, Iraq, including lighting loads, Table fan, Smartphone charger, TV, Microwave and Cooler. The stand-alone residential 5-kW hybrid power system consists of PV generator, PEMFC, storage batteries, Electrolyzer and hydrogen tank. The battery and hydrogen subsystem, composed of a fuel cell (FC), electrolyzer, and hydrogen storage tank, act as energy storage and support systems. The research in steps includes the design of the hybrid system. A program has been used in MATLAB to size system components in order to match the load of the site in the most cost-effective way. Evaluation of the economic feasibility of the same hybrid system when using a solar tracking system for photovoltaic cells. The simulation results demonstrate the hybrid system's ability to supply the required electrical energy to the loads, as well as the increase in hydrogen production when using the solar tracking system, which makes it possible to store excess hydrogen in the long term for later use when the photovoltaic energy decreases according to the seasons of the year.

A photovoltaic power estimation model based on the improved local cloud occlusion index algorithm considering Sun block luminance

Abstract Accurate estimation of photovoltaic (PV) power generation is of great significance to the operation and optimization of the power grid. However, due to the rapid movement of clouds in a short period of time, the changes in PV power are intermittent and fluctuating. Nowadays, sky images have been widely used for PV power estimation, which can effectively extract overall or local cloud information. However, Sun halos are often mistaken for being part of clouds and lead to incorrect cloud coverage calculations. To solve this problem, this paper proposes a method to calculate the local cloud occlusion index based on an improved two-dimensional Gaussian function. In addition, a new parameter, Sun block luminance, is introduced to quantify the lighting conditions in the solar area. On the other hand, this paper constructs a novel hybrid model, which introduces asymmetric convolution and SE attention module to enhance the extraction ability of sky image features. The test results show that, under sunny and cloudy conditions, the RMSE, MAE and R 2 values of the proposed model are 0.562, 0.462 and 0.989 on sunny days, 3.119, 2.248 and 0.774 on cloudy days, respectively, proving that the proposed model is superior to benchmark models.

Hierarchical Porous Silicon-Carbon Encapsulated Phase Change Materials for Efficient Photothermoelectric Conversion.

Scale-up applications in solar energy storage of phase change materials (PCMs) are hindered by the limitation of solid-liquid leakage and the lack of light absorption ability. Porous silicon-carbon (PSC) with a high specific surface area was prepared from a phytolith (Phy) silicon-carbon ore by the alkali-melting method, taking advantage of the natural mineral rich in light-trapping carbon structures in Phy. Stearic acid (SA) was impregnated into the PSC to produce integrated photothermal composite phase change materials (SA/PSC). The performance analysis of the form-stable PCMs (FSPCMs) shows that SA/PSC800 has good shape stability and excellent photothermal conversion efficiency and storage capacity, with a high photothermal conversion efficiency of 98.87%. The enthalpy change of the phase change was weak after 200 cycles, indicating good cycle regeneration. A solar thermoelectric generator (STEG) system for light-heat-electric energy conversion and storage was constructed using SA/PSC800 as a hot-side material integrated with a thermoelectric generator. Under a simulated solar light intensity of 200 mW/cm2, the output power generated by the STEG system through the Seeback effect can keep the small bulb and LED glowing for 21 and 18 s, respectively. Therefore, the prepared composite FSPCMs have promising applications in battery-coupled photovoltaic power generation.

Modeling, Control Study, and Power Management Strategy of a Hybrid Grid-Connected AC/DC Microgrid with High Integration of Renewable Energies and Green Hydrogen Sources

Abstract This first quarter of the 21st century is increasingly marked by population growth, digital and industrial de- velopments, growing need for electricity supply, and climate change. All this, to name just a few, have made the establishment of a stable, flexible, controlled, well-designed, extensive and clean power system a necessity. Conse- quently, distributed microgrid generation based on alternative/renewable energies and/or low-carbon technologies has emerged. In this paper, we study the modeling, the control, and the power management strategy of a grid-connected hybrid Alternating/Direct Current (AC/DC) microgrid based on a wind turbine generation system using doubly fed induction generator, a photovoltaic generation system, and storage elements including hydrogen storage system and batteries. Adequate modeling is described, and the overall system monitoring is presented and applied to manage appropriate power sharing and to control active and reactive powers, in order to match load and weather fluctuation behaviour. Simulations are carried out using MATLAB/Simulink simulation tool. Simulations reveal convenient re- sults in terms of the bidirectional interlinking converter capabilities regarding power balance establishment between the two subgrids, reactive power compensation to ensure a unity power factor, and DC-bus voltage regulation at 1200 V. In addition, the primary and secondary controls are approved for each distributed generation of the studied system to attain the assigned power references, regardless of whether the subgrid is heavily or lightly loaded throughout the four considered case studies, showing satisfactory tracking and interacting performances, and thus stimulating a stable system implementation.

Robustness-Based Evaluation of GHG Emissions and Energy Use at Neighborhood Level

Evaluating neighborhood performance is crucial for achieving long-term zero-carbon goals, enabling efficient energy, cost, and resource sharing among buildings. This task requires balancing multiple criteria and managing uncertainties, emphasizing the importance of performance robustness alongside high performance. This article introduces a flexible multi-criteria approach for evaluating neighborhood performance, focusing on greenhouse gas (GHG) emissions and energy use across different life cycle stages. Flytårnet, a Norwegian neighborhood with zero-emission ambitions, serves as a case study. The methodology incorporates the T-robust method, an established robustness-based approach, to select high-performance, resilient neighborhood designs under various uncertainties. Results indicate that when assessing buildings as key components and considering energy delivered during the operational phase, including photovoltaic generation, the supplied energy ranges from 25 to 80 kWh/m2/year. Over a 60-year period, life cycle GHG emissions span from 4 to 12 kg CO2-eq./m2/year, accounting for uncertainties and encompassing material production and replacement, as well as energy consumption and generation. However, the optimal design choice varies based on whether life cycle stages beyond the use stage are considered. This research provides valuable insights for decision-makers and designers seeking effective neighborhood designs in early-stage planning, considering diverse and conflicting performance criteria to achieve zero-emission goals.