Photovoltaic Power Generation Research Articles

Research on energy storage capacity optimization of rural household photovoltaic system considering energy storage sharing.

With the promotion of the photovoltaic (PV) industry throughout the county, the scale of rural household PV continues to expand. However, due to the randomness of PV power generation, large-scale household PV grid connection has a serious impact on the safe and stable operation of the distribution network. Based on this background, this paper considers three typical scenarios, including household PV without energy storage, household PV with distributed energy storage, and household PV with centralized energy storage. Then, a calculation model for PV local consumption rate and annual net cost under different scenarios is constructed. Combined with a natural village in Shandong Province, the PV local consumption rate and annual net cost under three scenarios are compared and analyzed, and the potential of energy storage sharing in reducing storage capacity and improving PV local consumption is explored. The results show that configuring energy storage for household PV can significantly improve the power self-balancing capability. When meeting the same PV local consumption, household PV centralized energy storage can achieve smaller energy storage configuration and lower cost compared to household PV distributed energy storage. Finally, suggestions are proposed to further promote the development of household PV energy storage system. The research results can provide reference for improving the local consumption of rural household PV and accelerating the application of household PV energy storage system.

Optimal power flow method with consideration of uncertainty sources of renewable energy and demand response

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

Influence of high-resolution data on accurate curtailment loss estimation and optimal design of hybrid PV–wind power plants

Hybrid photovoltaic (PV) - wind power plants (HyPPs), i.e., where the PV and wind systems are co-located and share the Point of Interconnection (POI) with the grid, have recently attracted more attention. This trend is driven by the expected reduced capital and operational expenditures achieved through, e.g., shared land and POI infrastructure for HyPPs compared to two individual PV and wind installations. However, if the POI is underdimensioned relative to the PV and wind capacities, the generation from either or both of the assets must at times be curtailed, unless mitigated by solutions like energy storage. This curtailment might lead to income losses. Moreover, as HyPPs typically are designed using wind and PV generation data on hourly resolution, the actual curtailment losses can be underestimated. This might in turn lead to HyPP designs which are techno-economically sub-optimal.In this study, a comparative analysis is conducted to analyze how the curtailment and income loss estimations for HyPPs, as well as the techno-economic optimal HyPP topologies, are impacted by varying the input data time resolution. One year of site measured PV and wind power generation data on 5 s resolution from an existing HyPP located in Eastern Germany are used as basis for the study. For a HyPP topology with an undersized POI, where the installed capacities of the POI, PV, and wind systems are all equal, the curtailment losses are estimated to be 1.45%, 1.43% and 1.09% using the 5 s, 1 min and 1 h resolution datasets, respectively. Moreover, using the 1 h instead of the 1 min dataset leads to a 1.86% overestimation of the total Net Present Value (NPV) for this HyPP topology. As the shares of the PV and wind systems increase relative to the POI capacity, the differences in the estimation of the curtailment losses and NPVs between the high- and low-resolution datasets become more significant.

Predicting Solar Power Generation Utilized in Iraq Power Grid using Neural Network

The prediction of a photovoltaic (PV) energy production over time is considered the major challenge to integrate it with the utilize grid. This is because it is affected by many factors, including geographical locations and weather conditions. Hence, accurately forecasting PV generation is a crucial stage for ensuring a grid stability. In this paper, several studies are discussed between 2015 to 2023 based on various term forecasting conditions. Then, a neural network (NN) is employed to forecast a medium-term PV power generation by gathering meteorological data specific to the southern region of Iraq. The proposed NN model based on Python language is trained to find the PV power prediction for various seasons of the year using solar radiation and surrounding temperature of weather condition tests. While, the MATLAB Simulink model is designed to address the PV actual power for the same tests. The results show that the root mean square error of a PV generation test at autumn season provides the lowest percentage of 92.2119 W when compared with the other three seasons

A Rapidly Self-Healing Superhydrophobic Coating Made of Polydimethylsiloxane and N-nonadecane: Stability and Self-Healing Capabilities

Superhydrophobic surfaces have garnered significant attention in various industrial applications, such as photovoltaic power generation, anti-icing, and corrosion resistance, due to their exceptional water-repellent properties. However, the poor durability of conventional superhydrophobic coatings has severely impeded their practical implementation. To achieve the dual self-recovery of microscale and nanoscale surface structures and maintain low surface energy after damage to superhydrophobic coatings, thereby enhancing their durability, a rapidly self-healing superhydrophobic coating was developed using polydimethylsiloxane (PDMS) and n-nonadecane in this study. The coating surface demonstrated exceptional hydrophobic characteristics, as evidenced by a water contact angle (WCA) of 157.5° and a sliding angle (SA) of 4.2° achieved at optimized proportions. Through scanning electron microscopy, it was observed that the coating surface exhibited a rough structure at both the microscale and nanoscale. The stability test results showed that the WCA only decreases by 5.7° and the SA only increases by 3.6° after 100 instances of external friction. The stability test results demonstrated that the superhydrophobic coating maintains excellent hydrophobicity under mechanical external forces and in acidic and alkaline environments. The results of the self-healing capability test showed that the WCA rebounded to 151.5° and 149.5° after we subjected the samples to 20 MPa of vertical pressure damage and chloroform exposure for 4 h, respectively. The coating regained a robust hydrophobic state even after experiencing repeated mechanical and chemical damage. The above results indicate that the resulting coating demonstrates outstanding durability, including high resistance to friction, stability against acids and alkalis, and the ability to self-recover hydrophobicity after repeated damage.

Assessment of suitability for photovoltaic power generation in periurban seminatural grasslands: toward the coexistence of seminatural grasslands and photovoltaic power generation

Renewable energy has grown substantially in recent years due to its efficacy in mitigating climate change. The rapid proliferation of solar photovoltaic (PV) systems and subsequent alterations in land use have led to concerns about the impact on local ecosystems. Particularly in Japan, seminatural grasslands, which are valuable habitats, are being developed as solar PVs. Here, we focused on stilt-mounted agrivoltaic systems, capable of both photovoltaic power generation and plant growth beneath solar panels. By repurposing this technology, the coexistence of vegetation and solar PV systems can be possibly ensured. To assess the feasibility of this proposed approach, we initially examined the suitability of installing solar PV in seminatural grasslands. The suitability of seminatural grasslands, solar PVs, and random points for solar PV was evaluated in terms of electricity generation and construction costs. The environmental conditions of seminatural grasslands were found to be advantageous for electricity generation. On the other hand, in terms of construction costs, seminatural grasslands were comparable to randomly selected sites. These findings indicate that the suitability of existing seminatural grasslands for solar PV plant installations is not considerably lower than that of other land types. The idea of reconciling solar power generation with ecosystem conservation holds promise and warrants further investigation toward its realization.

Assessment of photovoltaic power generation and radiative cooling potentials in old residential districts: A case study of Shenzhen

Building-integrated photovoltaic (PV) and radiative cooling (RC) are promising technologies for attaining the zero-carbon target in building industry. The PV and RC materials can be utilized for energy-saving renovations in building envelopes of existing residential districts. Nowadays, it is common practice to add thermal insulation to old residential districts to reduce building energy consumption. Whereas the development of PV and RC technologies provide opportunities of renewable energy utilization on building envelops in old residential districts renovation. The space for installing PV and RC materials on the roofs and facades is restricted. Hence, assessing and contrasting the efficacy of PV and RC materials in different sections of building envelopes is important from both energy and economic aspects consideration.This study employed the Rhino-Grasshopper platform to assess the energy and economic performances of PV and RC technologies when installed on roofs and facades with varying orientations. A typical old residential district in Shenzhen was employed as an example. The objective was to enhance the allocation of PV and RC components to attain optimal energy and economic outcomes. The life-cycle equivalent energy saving was used as economic indicator. Results demonstrated that PV is more suitable on roofs application, whereas RC is more suitable for facades. It is profitable to apply RC on the north facade whereas PV can be profitable when applied to the other envelops. By applying PV and RC to all old residential districts in Shenzhen, the annual PV power generation and cooling energy saving from RC are as high as 5299 GWh and 277 GWh. This study demonstrated that PV and RC are promising technologies in energy-saving renovation of old residential districts. The developed methodology can be applied in renovation projects to achieve optimized designs.

Fault Condition Testing of Photovoltaic Inverter Controller Based on Active Support Technology using Hardware-In-the-Loop in RTDS

Abstract Photovoltaic power generation is an important component of sustainable power generation and has been widely utilized. Grid connected inverters are key equipment in photovoltaic integrated systems, which directly affect the stability of the system. In this paper, the transient model of photovoltaic inverter controller based on active support technology is derived. The transient mathematical model and corresponding self-closing loop control strategy are analyzed. Based on the RTDS, a hardware controller semi-physical simulation platform is established to simulate and test photovoltaic inverters under different fault and control methods. Finally, the simulation data obtained from the self-closing loop control method and the hardware control method are compared to verify the stability and reliability of the photovoltaic inverter hardware controller.

Hybrid model for intra-day probabilistic PV power forecast

Renewable generation forecasting has become an essential element of power system management because of the ever-increasing share of renewable power fed into the grid. Solar energy is one of the most common and well-known sources of energy in existing networks. Due to its intermittent and non-linear nature, accurate prediction of solar irradiance is essential for ensuring the reliable integration of photovoltaic (PV) plants with the grid system and for effectively managing supply and demand. The dispatching problem is typically addressed by generating a forecast for a few sample plants and scaling the result of these forecasts by the PV power generation capacity connected to the electric system. This research presents a comprehensive comparison of various methods for predicting PV power generation in fixed and single axis tracking PV systems at two different locations. These methods include time-series statistical regression, parametric physical approaches, machine learning (ML), and ensemble techniques, aimed at intra-day forecasts ranging from 1 to 6 h ahead. The study introduces a novel method for developing a parametric linear model for fixed PV systems and uses model output statistics (MOS) to improve forecast accuracy. Additionally, this study details various optimization algorithms designed to refine model parameters for creating a hybrid model. It also examines key factors influencing PV power forecasts and applies post-processing Kalman filter techniques. The study provides a detailed analysis of deterministic and probabilistic forecasting models, offering not just the most probable power production estimate, but also conveying the associated uncertainty. The results demonstrate a highly skilled ML based hybrid model to forecast PV power for different technologies at two different locations.

Wasserstein generative adversarial networks-based photovoltaic uncertainty in a smart home energy management system including battery storage devices

Rooftop photovoltaic (PV) power generation uncertainty is one of the prominent challenges in smart homes. Home Energy Management (HEM) systems are essential for appliance and Energy Storage System (ESS) scheduling in these homes, enabling efficient usage of the installed PV panels' power. In this context, effective solar power scenario generation is crucial for HEM load and ESS scheduling with the objective of electricity bill cost reduction. This paper proposes a two-step approach, where a machine learning technique, Wasserstein Generative Adversarial Networks (WGANs), is used for PV scenario generation. Then, the generated scenarios are used as input for the HEM system scheduler to achieve the goal of cost minimization. The generated solar energy scenarios are considered in a single household case study to test the presented method's effectiveness. The WGAN scenarios are evaluated using different metrics and are compared with the scenarios generated by Monte Carlo simulation. The results prove that WGANs generate realistic solar scenarios, which are then used as input to a Mixed Integer Linear Programming (MILP) problem aiming for electricity bill minimization. A 41.5% bill reduction is achieved in the presented case study after scheduling both the load and ESS, with PV fluctuations taken into account, compared to the case where no scheduling, PV, or ESS are considered.

Analysis of the Influence of Distributed Photovoltaic Access on the Operation Quality of Asymmetry Distribution Network

Abstract Distributed photovoltaic(PV) power generation, as a highly flexible renewable energy source, is currently developing rapidly and has been widely used. However, due to its uncertainty and randomness, large-scale distributed photovoltaic integration into the distribution network will change the network power flow distribution, which will have a greater impact on the operation quality of the distribution network operation. In order to study the influence of distributed PV access on the operation of asymmetry distribution network, this paper builds a three-phase power flow model of distributed photovoltaic access to distribution network. Based on the IEEE33 node three-phase calculation example data, the impact of distributed photovoltaic access location and capacity on a series of operation indicators such as voltage deviation, three-phase unbalance and network loss under asymmetry operation of the distribution network is studied. Finally, for the asymmetry distribution network operating condition, guidance suggestions for distributed photovoltaic access based on operation quality are given.

Defect recognition of photovoltaic panels based on adaptive dimensional feature aggregation convolutional neural network

Abstract Under the policy framework of achieving carbon neutrality goals and addressing climate change, the importance of photovoltaic power generation will become more prominent, therefore the detection of defects in photovoltaic panels will become increasingly important. In order to accurately detect defects in photovoltaic panels, this paper proposes an adaptive dimensional feature aggregation algorithm that combines Deformable Convolutional (DCN) and C3 convolutional layers to form C3_ DCN module enhances the generalization ability of convolutional kernels; By incorporating the Cross Modal Transformer Attention Mechanism (CoTAttention) and Context Enhancement Module (CAM) at the output end, the model has the ability to adaptively allocate computing resources and enhance the ability to detect small targets; Using Focal EIoU as the loss function reduces the slow convergence speed and inaccurate results of known box regression methods. The experimental results show that for the PVEL-AD industrial detection dataset, the improved model has an average accuracy of 91.8%, which is 1.5% higher than the original model.

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

A study on a low input current ripple high voltage gain cuk converter

Abstract In the realms of photovoltaic power generation and fuel cells, the application of traditional DC-DC boost converters faces significant constraints due to insufficient gain and pronounced input current ripple. This paper presents a design for a Cuk converter with low input current ripple and high voltage gain based on the conventional Cuk topology. The design incorporates a passive ripple circuit and a coat circuit. The operational principles of the converter are analyzed, followed by the construction of a simulation model in PSIM. Simulation results not only validate the theoretical analysis but also affirm the feasibility of the proposed converter.

Impact of photovoltaic power generation on poverty alleviation in Jiangsu, China

The photovoltaic poverty alleviation project, part of the “Ten Major Precise Poverty Alleviation Projects” implemented by the Poverty Alleviation Office of the State Council, significantly contributes to eradicating poverty and rural revitalization. A difference-in-differences model was utilized in this study to assess this project's impact on rural households. This analysis used tracking data from households both with photovoltaic equipment installed and without in “S Town,” Jiangsu Province, from 2017 to 2021. The results indicate that photovoltaic installations lead to an increase in per capita disposable income, hence reducing poverty. However, further analysis suggests that better health and work capacity in disadvantaged households correlate with lesser benefits from the photovoltaic project in terms of income. The policy implications of these findings include a necessity for developing innovative models in the photovoltaic sector to enhance conditions for farmers, reducing their dependence on government subsidies.

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

Design of rooftop photovoltaic power generation system

Design of rooftop photovoltaic power generation system

Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference.

Optimal cooperative scheduling strategy of energy storage and electric vehicle based on residential building integrated photovoltaic

With the development of photovoltaic (PV) power generation technology, more and more families have begun to use solar energy. However, the uncertainty of solar energy production and user electricity demand has brought huge challenges to the development of efficient energy scheduling strategies. To deal with the uncertainty of electricity behavior, Monte Carlo generates the electricity scenario sets based on real data. According to the principle of minimum average distribution error (ADE), the scene with 200 scenes is selected for scheduling. Then, the Transformer model with random error (RE-Trans) is used to predict solar radiation and outdoor temperature. Through verification, it is found that the Mean Absolute Error (MAE) of the former is 0.389 and the Root Mean Square Error (RMSE) is 0.514. The MAE of the latter is 0.212 and RMSE is 0.413. To make effective decisions in the agent with the environment, a Deep Reinforcement Learning model with self-attention mechanism (SAN-DRL) is designed. The model aims to achieve a dynamic balance between electricity costs, carbon emissions and electricity sales income. Through continuous interaction with the environment, the agent finally obtains the optimal disordered charging and discharging behavior of energy storage systems (ESS) and electric vehicle systems (EVs), which greatly improves the utilization of solar energy resources. Finally, through simulation experiments, the proposed method is compared with both the baseline model and the ideal scheduling. The results demonstrate that the proposed method can reduce electricity costs and carbon emissions by 83.72 % and 72.08 %, respectively. Additionally, it achieves a net income of 38.59 CNY.

Using Analytical Hierarchy Process to Select the Best Power Generation Technology in Libya

Selection of an appropriate power generation technology is a complex process. Because it is necessary to consider and take into account a wide range of parameters. The investment in the power generation field is too costly while resources are limited. This paper presents the determination of these parameters, which influence the selection of power generation technology in particular for developing countries. This paper answers the question of the best suitable technology for Libya because the investment in this field is so costly and making a wrong decision is not allowed, otherwise it will consume the big margin of the initial budget. Four main criteria are included, that is economic, environmental, technical and social. A sub-criteria are also included to support this decision. The performances of six technologies of a power generation are evaluated by Analytic Hierarchy Process (AHP) using expert choice software and the best technology is selected. The results showed that the solar photovoltaic power generation is the best technology. By means of the sensitivity analysis, it was found that the best alternative is not sensitive to a change in the weights estimated by the AHP.