Photovoltaic Generation Research Articles

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.

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.

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.

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.

Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning

Virtual power plants (VPPs) have emerged as an innovative solution for modern power systems, particularly for integrating renewable energy sources. This study proposes a novel prediction approach combining improved K-means clustering with Time Convolutional Networks (TCNs), a Bi-directional Gated Recurrent Unit (BiGRU), and an attention mechanism to enhance the forecasting accuracy of wind and photovoltaic power generation in VPPs. The proposed TCN-BiGRU-Attention model demonstrates superior predictive performance compared to traditional models, achieving high accuracy and robustness. These results provide a reliable basis for optimizing VPP operations and integrating renewable energy sources effectively.

Performance Enhancement of Microgrid Systems Using Backstepping Control for Grid Side Converter and MPPT Optimization

Microgrid networks represent a crucial model in the field of power transmission and distribution, integrating renewable energy sources with modern control technologies. This research focuses on enhancing the performance of a microgrid system connected to the main grid through a three-phase converter controlled using Voltage Oriented Control (VOC). The microgrid comprises a storage element and a photovoltaic (PV) generation system. To improve system efficiency and power quality, backstepping (BS) controllers are implemented and compared with traditional control methods. Three control systems are evaluated: a conventional system using Perturb and Observe (P&O) algorithm for Maximum Power Point Tracking (MPPT) and Proportional-Integral (PI) controllers for grid-side converter (GSC) control, a hybrid system with BS-modified P&O (BS-P&O) for MPPT and PI controllers for GSC, and an advanced system employing BS-P&O for MPPT and BS controllers for GSC. The research demonstrates that BS controllers exhibit superior dynamic performance, contributing to improved regulation of DC-link voltage, active and reactive powers. Additionally, they achieve lower Total Harmonic Distortion (THD) values and increase the overall efficiency of the PV system. The proposed advanced control strategy shows particular effectiveness in tracking speed, disturbance rejection, and power quality enhancement. This study underscores the potential of nonlinear control techniques in optimizing microgrid operations and facilitating the integration of renewable energy sources into the main grid.

Innovative Photovoltaic-Aeration Integration: Enhancing Energy Efficiency and Grid Stability in Wastewater Treatment

Abstract This paper presents a detailed investigation into enhancing the energy efficiency of wastewater treatment plants (WWTPs) by integrating photovoltaic (PV) systems, emphasizing power flow analysis and experimental validation. Recognizing the substantial energy demands of aeration processes in WWTPs, this study proposes an innovative integration of PV panels with aeration tanks. This approach generates renewable energy and optimizes energy use through the thermal interaction between the PV panels and the aeration tanks. Key findings demonstrate a 15% overall increase in energy efficiency and a 5% improvement in PV efficiency due to aeration-induced cooling, along with a reduction in voltage fluctuations by up to 30% during high-demand periods. Additionally, the integration offsets approximately 20% of the WWTP's total energy consumption. The research is structured into two main components: a comprehensive power flow study using DIgSILENT PowerFactory and a laboratory experiment to validate the integration's effectiveness. The power flow analysis evaluates the electrical impact of PV integration on the WWTP's power grid, focusing on scenarios such as load fluctuations, grid disturbances, and the synchronization of PV generation with plant energy needs. The simulation results indicate that the integration significantly enhances the stability and efficiency of the plant's electrical system, reducing reliance on traditional energy sources. Concurrently, a laboratory experiment explored the practical effects of integrating PV systems with aeration tanks. The experiment demonstrated that the cooling effect provided by the aeration tanks leads to increased PV efficiency and notable energy savings. These experimental results align with the simulation findings, confirming the efficacy of this integrated approach. This study introduces a novel methodology for integrating renewable energy technologies into industrial processes, showcasing the potential for significant energy savings and improved operational efficiency in WWTPs. Future research will focus on scaling this integration strategy and assessing its long-term impacts on energy efficiency and wastewater treatment effectiveness.

Optimal control of grid-connected overvoltage of distributed photovoltaic power generation based on cluster division

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

A New Methodology for Estimating the Potential for Photovoltaic Electricity Generation on Urban Building Rooftops for Self-Consumption Applications

As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments.

Deep-RNN based model for short-time forecasting photovoltaic power generation using IoT

Global warming is one of the most significant issues of the century due to climate change caused by increased carbon emissions resulting from the exploitation of fossil fuels. Consequently, renewable energies are considered an alternative that promotes cleaner production and offers a substantial reduction in carbon emissions. Therefore, accurately forecasting photovoltaic (PV) power generation is crucial for controlling and distributing electrical inventory and ensuring the stability and reliability of power systems. In this paper, we develop a model for forecasting short-term PV power generation based on deep Recurrent Neural Networks (deep-RNNs). To improve efficiency, our model uses weather and PV generation dataset on-site collected in real-time using IoT technology. Specifically, by leveraging deep-RNN, particularly the long short-term memory network (LSTM) and gated recurrent units (GRU), which excel at capturing long-term dependencies in time series data, this article proposes a combination of LSTM and GRU models to take advantage of both in different weather conditions. The results of the experiments show that the LSTM-GRU model that has been proposed performs better in PV power forecasting than both the LSTM and GRU models together.

Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

This paper presents a planning strategy for integrating renewable distributed generation (DG) units into a distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing a novel meta-heuristic algorithm, the Blood-Sucking Leech Optimizer (BSLO), the study addresses a multi-objective optimization problem aimed at determining the optimal placement and sizing of DG units, as well as the most effective network topology. This approach seeks to minimize active power losses, improve voltage profiles, reduce installation costs, and lower greenhouse gas emissions. The model accounts for variable load demands, climatic factors (such as ambient temperature, solar irradiation, and wind speed), and fluctuating energy prices, reflecting realistic operating conditions. Tested on the IEEE 69-bus distribution network, the BSLO algorithm demonstrated rapid convergence to the global optimum by effectively balancing exploration and exploitation phases. Compared to other meta-heuristic methods, such as the Grey Wolf Optimizer, Gorilla Troops Optimizer, Walrus Optimization Algorithm, and Artificial Hummingbird Algorithm, the BSLO consistently achieved superior accuracy and faster convergence, resulting in higher precision and optimization efficiency. The optimal deployment of two PV generators and two wind turbines, combined with selective line switch openings, resulted in an 87.66% reduction in active power losses, a 73.30% decrease in voltage deviation, a 51.91% reduction in overall system costs, and a 62.74% decrease in greenhouse gas emissions compared to the base case.

Tradeoff optimization of urban roof systems oriented to food-water-energy nexus

Urban roof development serve as a form of urban tinkering that could provide favorable conditions to meet wicked food, water, and energy challenges. In this research, three urban roof systems are designed featuring food production, rainwater collection, building energy saving, and photovoltaic power generation, which are named the bare roof system, green roof system, and open-air farming roof system. Here we establish a generalizable framework to reveal the multifaceted tradeoffs of urban roof schemes integrating geographic information system, life cycle assessment, and multi-objective optimization and decision-making. Testing the framework in a typical compact city Shenzhen, China, results show that the rooftop suitability for food-water-energy function development within the city ranges from 26.21 % to 42.83 %. When implementing a city-wide roof system upscaling, the harvested rainwater on rooftops would contribute to 82.5 % of urban recycled water utilization target in 2035 and exhibit the fewest economic costs, but comes at the cost of life cycle carbon emissions and land occupation with 1850 kt and 140 km2. City-wide roof farming scenario shows potential to achieve the local self-sufficiency of vegetables, and stands out as the avoided transboundary energy footprints which are 4.5 times greater than life cycle energy consumption. Our research can foster the multiple tradeoff understanding between upscaling roof systems and urban food-water-energy nexus. The findings will assist in multi-objective decision-making for roof planning and sustainable transition in cities.

Ground-Based Cloud Image Segmentation Method Based on Improved U-Net

Cloud image segmentation is a technique that divides images captured by meteorological satellites or ground-based observations into different regions or categories. By extracting the distribution, shape, and dynamic features of clouds, it provides precise data support for the meteorological and environmental fields, significantly influencing photovoltaic (PV) power generation forecasting, astronomical telescope observatory site selection, and weather forecasting. A ground-based cloud image segmentation model based on an improved U-Net is proposed, which adopts an overall encoder–decoder structure. In the encoder phase, this paper constructs a dilated convolution–atrous spatial pyramid pooling (ASPP)–dilated convolution structure to enhance early cloud feature extraction. Dilated convolution is a novel type of convolution that expands the receptive field by inserting holes into standard convolution, thereby capturing a larger range of contextual information. ASPP maintains high resolution while paying attention to both local details and global structures of the image. In the decoder stage, the bicubic interpolation method is used for up-sampling to restore the feature map resolution and improve the clarity of the segmented image. The bicubic interpolation method refers to the use of cubic polynomial functions to interpolate the pixel values of the input image. In addition, this paper designs a novel skip connection layer structure between the encoder and decoder, composed of a depthwise separable path (DS path) and an improved channel spatial attention module (Im-CSAM) connected in sequence. The DS path combines depthwise separable convolutions and residual structures to facilitate information exchange between high-level and low-level features. The Im-CSAM is a modular attention mechanism that focuses on important cloud features in spatial and channel dimensions to enhance segmentation accuracy. Experiments show that compared to the traditional U-Net, the accuracy, precision, and MIoU of this model improved by 2.2%, 4.1%, and 5.0%, respectively, in the SWINySEG dataset, and by 3.2%, 3.6%, and 5.8%, respectively, in the TCDD dataset, proving that the improved method has a better generalization ability and segmentation performance.

Nonlinear Backstepping Control for Three-Phase Single-Stage Grid-Tied Photovoltaic Systems via an LCL-Filter

This paper examines the modeling and nonlinear control of a Three-Phase Single-Stage Grid-Tied Photovoltaic System (TPSS-GTPS). The system structure is relatively simple, comprising a Photovoltaic (PV) generator connected to the grid through a three-phase Voltage Source Inverter (VSI) and an LCL filter designed to reduce harmonics in the grid current. The primary objective of the control system is to maximize the extraction of power from the Photovoltaic Generator (PVG) and deliver it to the utility grid with a Unity Power Factor (UPF), while ensuring the asymptotic stability of the closed-loop system. In order to achieve these objectives, a novel nonlinear controller was developed in the synchronous dq-frame following the backstepping approach. Evaluating the effectiveness of the designed controller, simulations were performed in the MATLAB/Simulink environment under various scenarios to consider the effects of irradiance and temperature on the PVG. The simulation results demonstrated that the controller successfully achieved all the specified objectives. Additionally, this study highlights the effectiveness of the LCL filter in reducing Total Harmonic Distortion (THD).

Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configurations of PV generators, inverters/chargers, and batteries were determined, focusing on maximising the net present value (NPV). Both DC- and AC-coupled systems were explored. The performance of each configuration was simulated over a 25-year lifespan, considering varying pricing, solar resources, battery ageing, and PV degradation. Constraints included investment costs, capacity factors, and land use. A case study conducted in Wiesenthal, Germany, was followed by sensitivity analyses, revealing that a 75% reduction in battery costs is needed to make AC-coupled PV-plus-battery systems as profitable as PV-only systems. Further analysis shows that changes in electricity and FCR pricing as well as limits on FCR charging can significantly impact NPV. The study confirms that integrating arbitrage and FCR services can optimize system profitability.

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.

99.6% efficiency DC-DC coupling for green hydrogen production using PEM electrolyzer, photovoltaic generation and battery storage operating in an off-grid area

This paper presents a novel connection and control strategy for a hydrogen generation system using a proton exchange membrane electrolyzer powered by solar energy in an off-grid area without network backup. Given that, the proposed architecture is based on the indirect control of the Photovoltaic plant (achieved by removing a power-converter), the presented solution is more efficient and more reliable than the traditional scheme. Furthermore, with this innovation one can reach the same hydrogen production with smaller electrolyzers.The methodology includes detailed models of the photovoltaic panel and the electrolyzer, along with a control strategy that considers the degradation mechanisms of the electrolyzer to ensure reliable and prolonged operation. The results show that the proposed strategy keeps the operating power of the electrolyzer constant, even under variations in irradiance, thanks to energy storage in batteries. It is demonstrated that the proposed system offers efficiency above 99.6% during the analyzed period, with a 100% utilization rate of the electrolyzer, avoiding periods of inactivity and high current peaks. The study also includes simulations and experimental tests that confirm the feasibility and effectiveness of the presented solution, highlighting its advantages in terms of efficiency and investment costs compared to direct connections and other existing methods.