Solar Photovoltaic Power Generation System Research Articles

Application on ac station service system for gathering station based on roof photovoltaic system

Abstract With the increasingly severe global energy crisis and environmental pollution, it is imperative to develop and utilize clean and renewable energy. In order to achieve carbon peak and carbon neutrality as soon as possible, and reduce the proportion of fossil energy generation, decarbonization in the power industry is imperative. This article focuses on the design of rooftop photovoltaic systems at gathering stations. By installing solar photovoltaic panels on the roof of the gathering station building, a photovoltaic power generation system is constructed to convert solar energy radiated into electricity. The DC power generated by the solar photovoltaic power generation system is converted by an inverter and connected to the 380V bus of the station power system as a supplementary power supply for the station load, thereby increasing the proportion of green electricity consumption in the collection station and achieving the goal of carbon reduction

A new y-source high-gain buck-boost converter suitable for solar PV power generation system

A new y-source high-gain buck-boost converter suitable for solar PV power generation system

Prediction of power generation and maintenance using AOC‐ResNet50 network

AbstractWith the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large‐scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct‐ResNet50 network, and it is found that the AOC‐ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed.

Optimization and Feasibility Analysis of Rooftop Solar Photovoltaic (PV) Power Generation System Design

Optimization and Feasibility Analysis of Rooftop Solar Photovoltaic (PV) Power Generation System Design

The Effect of Radiation Intensity on the Performance of Direct-Expansion Solar PVT Heat Pump Systems

The objective of this study was to investigate the impact of solar radiation intensity on the performance of direct-expansion solar PVT heat pump systems. To this end, an experimental setup was constructed for direct-expansion photovoltaic (PVT) solar heat pump water heating systems and photovoltaic (PV) power generation systems. The system performance and main parameters were analyzed and discussed under different solar radiation intensities. The winter experiments in southern China revealed that the exhaust temperature of the heat pump unit varied considerably under clear conditions, while the back temperature remained stable, fluctuating between approximately −13.5 °C and 24 °C. In contrast, the power generation of PVT panels increased with the increase in radiation intensity, from 78.33 W to 122.68 W, for an increase of 56.6%. Furthermore, the total electricity generation of the PVT panels was higher than that of PV panels, with an increase of 8.7–8.3%. Nevertheless, discrepancies between experimental and theoretical data were observed, particularly under overcast conditions, where the back panel temperature error was pronounced. Additionally, the system exhibited enhanced stability at elevated temperatures in comparable environments, accompanied by an improvement in the system’s coefficient of performance (COP) by 5.67%.

Enhancing Solar Photovoltaic Panel Performance in India's Tropical Climate: An Experimental Study on the Influential Effects of Optical Filters

<p>Due to the increasing global energy demand and consequent climate change from burning fossil fuels, researchers are getting more focused on renewable energies as a potential solution to the world's energy problems. Solar energy is a prominent form of sustainable energy. The solar photovoltaic power generation system’s electrical performance is negatively influenced by several factors, including the impact of solar irradiance, incident angle, temperature, dust accumulation on the top of the solar cell, cracks in the solar panel, shadowing, shunt resistance, solar cell materials, load mismatch, maintenance and cleaning schedule, spectral mismatch loss, and cable loss. The direct environmental factors that have the biggest impact on efficiency are the effects of sun irradiance and temperature. This empirical investigation examined the impact of optical filters on the electrical efficiency of a 40W polycrystalline solar PV panel in India while considering real-time ambient conditions. Through the utilization of an optical filter, the solar PV panel's temperature is diminished, hence enhancing the output power of the solar panel. Based on the experimental findings, the utilization of a transparent plexiglass sheet equipped with a coolant system installed on the solar panels leads to a drop in the average operating temperature of the solar PV module by up to 6°C. Additionally, the output power is enhanced by up to 10W in comparison to the reference solar panel’s output performance.</p>

Preface

Available on the latest research progress of clean energy and power generation technology, this paper volume gathers a bunch of papers collected from 2023 8th International Conference on Clean Energy and Power Generation Technology (CEPGT 2023), held via virtual form in Zhuhai, China during December 15th to 17th, 2023.The Conference was attended by about 100 delegates around the globe. In the opening ceremony speech, all academicians, experts and scholars were welcomed to attend the Conference and to share insightful speeches and presentations. Then in the keynote speech part, Prof. Marc A. Rosen (Ontario Tech University, Canada), Prof. Haiping Yang (Huazhong University of Science and Technology, China), Prof. Qinmin Yang (Zhejiang University, China), Prof. Qiang Lu (North China Electric Power University, China) and other professors made wonderful academic reports on international frontier hot spots. Apart from the keynote speeches, oral and poster presentation parts were also held and displayed by various scholars, leading to a warm atmosphere of academic discussion.We received a number of submissions from the Conference. After rigorous review and review rebuttal process by related top experts, various excellent papers were accepted and included in this paper volume. These papers cover many topics of the Conference theme, including Energy Security and Clean Use, Energy Conservation and Emission Reduction, Biomass Energy Engineering, Hydroelectric Power Generation, Solar Photovoltaic Power Generation System, etc. The works of this volume can promote the development of clean energy and power generation technology, and thereby enhance scientific information interchange among scholars from top universities, research centers and high-tech enterprises working all around the world.Featuring the most cutting-edge research directions and achievements related to clean energy and power generation technology, this Conference provided the most comprehensive research in related fields and a more comprehensive understanding of the latest results of cross researches in the fields. Meanwhile, it also helped researchers and engineers understand the research frontier, as well as discover the solutions to potential problems.We would like to acknowledge the authors for their contributions and the reviewers for their time to review the submissions. We are thankful to all the committee members and advisors of this volume. Finally, our appreciation also goes to the editors and members of Journal of Physics: Conference Series for their efforts in publishing this volume.We hope that it will serve as a reference for researchers and practitioners in academia and industry in the areas related to clean energy and power generation technology.The Committee of CEPGT 2023List of Committee Member is available in this pdf.

Fault diagnosis study of ship solar photovoltaic power generation system

In this paper, a fault diagnosis method of ship photovoltaic (PV) power generation system based on a convolutional neural network (CNN) is proposed to ensure the smooth navigation of ships. Firstly, to understand the normal operation and fault state of the generation system, a simulation model of the photovoltaic power generation system is constructed using MATLAB / Simulink. Then, the model is simulated to extract the fault data under different fault conditions, and the eigenvalue extraction of the fault data is carried out by using the fast Fourier transform method. Following that, a fault diagnosis model is built using the convolutional neural network and trained accordingly. The final diagnostic results show that the established simulation model and convolutional neural network can provide support for the fault diagnosis of ship photovoltaic power generation system, and can autonomously detect the type of faults and locate the location of the faults after the faults occur in the generation system, to ensure the stable and safe operation of ship power generation system.

Green energy-sourced AI-controlled multilevel UPQC parameter selection using football game optimization

The power quality (PQ) has been significantly affected by the integration of intermittent non-conventional sources (NCS) into the local distribution system in addition to the adoption of power electronic technologies to regulate non-linear loads. This article combines the H-bridge cascade five-level unified power quality conditioner (5L-UPQC) with the wind power generation system (WPGS), solar photovoltaic power generation system (SPVGS), and battery storage system (BSS) as an effective approach to address PQ problems. The utilization of the Levenberg–Marquardt backpropagation (LMBP)-trained Artificial neural network controller (ANNC) in the UPQC is recommended for generating appropriate reference signals for the converters. This eliminates the requirement for conventional complex conversions, such as abc, dq0, and αβ. Moreover, the artificial neuro-fuzzy interface system (ANFIS) is recommended for achieving a DC-link balance. Football game optimization (FBGO) is utilized to determine the optimal shunt and series filter characteristics. The major objectives of the proposed system are to reduce the current waveform irregularities, resulting in a decrease in the total harmonic distortion (THD), an enhancement in the power factor (PF), the mitigation of supply voltage imbalances and disturbances, and the maintenance of a steady direct-current link capacitor voltage (DLCV), despite the variations in the load, solar irradiation, and wind velocity. The efficiency of the suggested strategy is assessed using four case studies that involve different loads, variable wind velocities, and source voltage balancing conditions. Based on the simulation studies and obtained results, the suggested method significantly decreases the THD to values of 2.91%, 3.63%, 3.75%, and 3.50%. Additionally, it achieves a power factor of unity, which is considerably lower compared to other multilevel schemes that use the traditional symmetrical reference frame (SRF) and instantaneous reactive power (pq) methods. This design has been executed using the MATLAB/Simulink program.

Scheme Design and Energy-Saving Optimization of Cold and Heat Energy Supply System for Substation Main Control Building in Cold Area

In the context of global climate change, the implementation of building energy conservation and carbon reduction, as well as the realization of zero-energy buildings, is a key measure to cope with climate change and resource depletion. A substation is an indispensable building in the process of urbanization construction. However, in existing cold areas, the heating form of substations generally adopts electric heating, which consumes a large amount of energy. This paper optimizes the existing HVAC form of substations through the rational utilization of surrounding environmental resources and puts forward reasonable building energy-saving and carbon-reduction methods. It demonstrates the feasibility of combining solar photovoltaic power generation systems, air source heat pumps, and natural ventilation to optimize energy savings and carbon reduction in the main control building of a substation in a cold area. The computational fluid dynamics (CFD) method is used to demonstrate the feasibility of natural ventilation during the summer and transition seasons. The data indicate that the installation of a solar photovoltaic power generation system results in an average annual power generation of 18.75 MWh. Additionally, using an air source heat pump can save 44.5% of electricity compared to electric heating. When both a solar photovoltaic power generation system and an air source heat pump are used to provide a building with cold and heat sources, the annual emissions of CO2 can be reduced by 4.90 tons compared to a traditional electric heating system.

Analysis Of Silicon Crystal Materials: Meeting Growing Demands Across Industries

The demand for silicon crystal materials is poised for continued growth across diverse sectors in the upcoming years. In the realm of computers and communication, silicon single crystal wafers and chips are in high demand, fueled by the increasing adoption of communication equipment and the essential requirement for memory and control chips in information processing systems. Beyond the electronics domain, silicon crystals find applications in various industries, including environmental protection, new energy, and construction. In the field of environmental protection, silicon crystals play a vital role in denitrification and dust removal devices. In the new energy sector, they contribute to solar photovoltaic power generation systems and geothermal power generation systems. In construction, silicon crystals find their place in the production of building glass and semiconductor building materials. As China's economy continues to evolve and environmental concerns gain prominence, the demand for semiconductor materials, including silicon crystal materials, is anticipated to rise. This underscores a bright market outlook for silicon crystal materials in the foreseeable future.

Application analysis based on solar grid-connected photovoltaic power generation and intermittent energy storage system

Solar photovoltaic power generation as a building electrical professional in the energy use of important technical means, through the realisation of photovoltaic conversion, can be a large amount of light energy transmission to the building application, once the electrical energy surplus is too much, but also can be transmitted to the power grid use. Based on this, the article takes the solar photovoltaic power generation system as the basis, comprehensively analyses the operation principle and basic characteristics of solar photovoltaic power generation, summarizes the main influencing factors of grid-connected solar photovoltaic power generation, as well as the impact of grid-connected solar photovoltaic power generation on the grid, and researches on applying automation technology to solar photovoltaic power generation, such as photovoltaic buildings, DC inverters, reactive power compensation, etc., so as to comprehensively improve the grid-connected solar photovoltaic power generation efficiency and provide a constant source of energy for buildings. Solar thermal power generation technology is another kind of solar power generation technology besides photovoltaic power generation. It is a renewable energy generation method that integrates solar thermal conversion power generation, large-scale heat storage and the characteristics of power grid synchronmachine. According to the influence of different intermittent heat load fluctuation characteristics on the design of solar heat collection and heat storage system, the design method of heat collection and heat storage of intermittent solar heating system is established. The operation characteristics of solar energy system in three typical intermittent heating modes are compared and analyzed. It provides the design method and operation optimization strategy for the efficient utilization of solar heating system.

Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer.

Deep Learning for Variable Renewable Energy: A Systematic Review

In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.

Assessing the Photovoltaic Power Generation Potential of Highway Slopes

The solar photovoltaic (PV) power generation system (PGS) is a viable alternative to fossil fuels for the provision of power for infrastructure and vehicles, reducing greenhouse gas emissions and enhancing the sustainability of road transport systems. A highway slope is generally an idle public area with high accessibility, which is the ideal application scenario for a PV PGS. The assessment of PV power generation potential (PGP) is key for the planning and design of PV PGS projects. Previous approaches to potential assessments are mainly based on digital maps and image processing techniques, which do not fully consider the impacts of the highway orientation, the slope geometric characteristics, and the PV panel placement scheme on the evaluation results. Therefore, this study proposes an assessment method for the PV PGP on highway slopes using the design or calculated highway and slope geometric parameters and the solar radiation received by PV panels under the desirable placement scheme. Highway segmentation and geometric parameter calculation methods were established, and the optimal PV array placement schemes for typical slope orientations were determined by simulating the PV power generation in the software PVsyst (version 7.2). Afterwards, the theoretical PGP could be calculated using the received solar radiation and the available slope area. By subtracting the energy loss caused by temperature changes, the operation of inverters, and the PV modules’ performance decay, the actual PV PGP could be obtained. Finally, a case study of the solar PGP assessment of a 1.97 km long highway section is provided, and the feasibility of the proposed method is verified.

Preface

This Proceedings contains the selected papers of the 2023 3rd International Conference on Energy Engineering, New Energy Materials and Devices (NEMD 2023), which was held via virtual form in Guangzhou, China from March 24th to 26th, 2023.NEMD 2023 is aimed at the academic and scientific community and the productive sectors of the region, with the aim of disseminating academic and research work; moreover, promotes the exchange of experiences between researchers as well as the participation of the productive sectors in research, extension, technological development, and innovation activities, contributing to the strengthening the relationship between universities, enterprises and institutions.This Conference was articulated with different topics around the themes of Energy Engineering, New Energy Materials and Devices to discuss on the research findings, their possibilities and challenges. The relevance of the NEMD 2023 conference theme to a wide range of disciplines is reflected in the papers that have been submitted for publication. The topics covered in the Proceedings includes: Solar Photovoltaic Power Generation System, Principle and Technology of Solar Cell, Distributed Energy System Technology, Energy Conversion and Application, Green Energy Materials, etc.. All the papers, being reviewed through a double-blind review process, are original contributions to the topics and shown the research works that are doing in the field of the described theme.We had about 60 participants all around the world. The scientific program of the Conference was composed of keynote speech, oral presentation, and academic investigation. Assoc. Prof. Jing Guo from Xi’an Jiaotong University, China, who has published more than 80 academic publications, over 4600 SCI citations, with an H-factor of 40 (Google Academic), made a keynote speech on Ceramic-Polymer Composites Prepared via Cold Sintering Process. Ceramicpolymer composites have attracted much attention due to the excellent combined physical and chemical properties. However, most of the ceramics are sintered at high temperatures, typically higher than 1000 °C, which are not compatible with the processing temperatures of polymers. Thus, it is challenging to integrate ceramics with polymers in a onestep sintering process. Cold sintering process proposed in 2016 provides a route for the densification of ceramics at temperatures lower than 350 °C. He discussed the recent development of cold sintered electronic materials based on ceramic-polymer composites. The brilliant speeches of each speaker provided a feast of knowledge for all the participants, allowing them to communicate with each other and get some academic advice or suggestion.Our sincere thanks and gratitude to all the participants for making such a comprehensive NEMD Conference and Proceedings, which contribute to the enhancement and development in related fields. We are grateful to the Technical Program Committee for their valuable time and advice. Special thanks to the members of the Journal of Physics: Conference Series for making this Proceedings published. We appreciate the effort from those involved and look forward to a more successful conference next year.The Committee of NEMD 2023Committee Member are available in the pdf

An intelligent converter and controller for electric vehicle drives utilizing grid and stand-alone solar photovoltaic power generation systems

In this study, a battery energy management system for electric vehicle (EV) applications is proposed with a standalone photovoltaic (PV) source and controlled based on the availability of grid, PV source, load consumption, and energy stored in the battery. This paper proposes a single-ended primary-inductance converter (SEPIC) DC-DC converter for charging the battery through the utility and PV source that provides good load regulation. The bidirectional nature of the proposed DC-DC converter provides the charging and discharging of the EV battery in the succeeding modes of operation, i) grid-tied charging, ii) PV-tied charging, iii) discharging to the load in the absence of utility and PV source, and iv) regenerative braking. An improved perturb and observe-based maximum power point tracking (MPPT) algorithm is proposed to track the maximum power from the PV source. In addition, to handle the four modes of operation, a dedicated controller is also proposed. Firstly, the proposed system is validated using MATLAB/Simulink software by considering different operating conditions, and the performance is compared with the traditional MPPT algorithms. Finally, the effectiveness of the suggested system is validated through an experimental prototype. The result proved the superiority of the converter and controller over the traditional systems.<div align="center"> </div>

A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and optimized dispatch and reduces operating costs. Solar PV power generation depends on the weather conditions, such as temperature, relative humidity, rainfall (precipitation), global solar radiation, wind speed, etc., and it is prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility, and intermittency. Recently, the demand for further investigation into the uncertainty of short-term solar PV power generation prediction and its effective use in many applications in renewable energy sources has increased. In order to improve the predictive accuracy of the output power of solar PV power generation and develop a precise predictive model, the authors used predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is an important aspect of optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. The contributions, advantages, and disadvantages of the predictive probabilistic methods are compared. Finally, future studies on short-term solar PV power forecasting are proposed.

Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm

With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved Aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved Aquila optimization algorithm (IAO) is used to optimize the parameters of the LSTM neural network to establish a model for predicting the actual photovoltaic power. The experimental results show that the proposed IAO-LSTM photovoltaic power prediction model has less error, and its overall quality and performance are significantly improved compared with the previously proposed AO-CNN model.

Research on combined solar fiber lighting and photovoltaic power generation system based on the spectral splitting technology

Modern buildings tend to be high-rise and dense, and indoor lightings are extensively depending on electricity even on sunny days currently. In addition, for those existing solar lighting technologies in development, only the visible light of solar radiation has been used, with the extra spectral energy dissipated by waste heat. A solar fiber optic lighting and photovoltaic power generation system based on spectral splitting technology (SSLP) is proposed and tested in this study. The sunlight is divided into different wave bands through a spectral beam splitter, where the visible light is used for optical fiber illumination, and the near-infrared radiation is used for photovoltaic power generation. The designed SSLP system is simulated with the Monte Carlo ray tracing method, and then experimentally tested. The effects of structural parameters and tracking accuracy on the optical performances of the system are examined. The experimental results show that the sunlight transmitted to the room using optical fiber is bright and comfortable, with an average lighting efficiency of 15.1 %; meanwhile, the average power generation efficiency of the system is about 6.1 %, reaching one-third of the value of conventional PV modules. When the output luminous flux is converted by the luminous efficiency of fluorescent lamps, the overall average efficiency of the system can reach to 29.5 %; when converted by LED lamps, the overall average efficiency can reach to 25.4 %, which are both more efficient than the conventional PV power generation. Taking the solar radiation conditions in Beijing as an example, when the sunlight collection area of the system is 1 m2, it can provide 8 h of sunlight illumination of 500 lx per day for a room of 19.7 m2, while the daily generated electricity can be provided to the LED lamps to extend the lighting time by 2.5 h.