Photovoltaic Power System Research Articles

Techno-Economic Analysis of Hybrid Pv-Battery-Diesel Power System for Rural Electrification: A Case Study of Ibudo Ora, Ogbomoso

Fossil-fueled generators and electrical grid extensions are the most popular energy sources for supplying electricity to rural areas. However, the high cost of running and maintenance, noise pollution, and a need for decarbonization necessitate the hybridization of different energy sources as a viable solution. Using the conventional technique for the optimal design of a Hybrid Power System (HPS), such as the Hybrid of Multiple Energy Resources (HOMER), is inefficient regarding electricity cost and carbon emission reduction. Hence, this research conducted a technical and economic analysis of an optimal photovoltaic (PV), battery, and diesel generator-based power system to electrify Ibudo Ora, a rural community in Ogbomoso. A feasibility study on electricity demand was conducted on the Ibudo Ora community by conducting an onsite survey, and the community load profile was estimated. Mathematical modeling of the HPS component was formulated. A multi-objective function was developed to minimize the Net Present Cost (NPC), Levelized Cost of Electricity (LCOE), and Total Carbon Emission (TCE) and maximize the System Reliability (SR) of the proposed HPS and optimized using the Energy Valley Optimizer (EVO). MATLAB R2021a was used to simulate the developed model. The performance of the developed EVO-based HPS was evaluated using NPC, LCOE, TCE, and SR as metrics and compared with HOMER, which was used for the same purpose. The NPC of the developed EVO-based HPS and HOMER-based HPS were $998702.87 and $1011984.27, respectively, while the LCOE were $0.4889 and $0.4954. The loss of power supply probabilities of the developed EVO-based and HOMER-based HPS was zero, while in terms of TCE, EVO-based HPS and HOMER-based HPS had 775958.15 kg and 832912.49 kg, respectively. The results showed an appreciable reduction in the NPC, LCOE, and TCE using EVO compared with HOMER for an optimized HPS. This research will assist the government, investors, and policymakers in making decisions on rural electrification using HPS.

State-of-Art Techniques for Photovoltaic (PV) Power Systems and their Impacts

State-of-Art Techniques for Photovoltaic (PV) Power Systems and their Impacts

Improving the efficiency of a non-ideal grid coupled to a photovoltaic system with a shunt active power filter using a self-tuning filter and a predictive current controller

Introduction. Recently, photovoltaic (PV) systems are increasingly favored for converting solar energy into electricity. PV power systems have successfully evolved from small, standalone installations to large-scale, grid-connected systems. When the nonlinear loads are connected to a grid-tied PV system, the power quality can deteriorate due to the active power supplied by the PV array, there’s a noticeable decline in the quality of power delivered to consumers. Its combination with the shunt active power filter (SAPF) enhances system efficiency. Consequently, this integrated system is adept at not only powering local loads but also at compensating for reactive power and filtering out harmonic currents from the main grid. The novelty of the work describes how an operation of a small scale PV system connected to the low voltage distribution system, and nonlinear load can be achieved, the investigation aims to analyze the system’s behavior and elucidate the advantages of employing various control algorithms. These proposed algorithms are designed to ensure a unity power factor for the utility grid while prioritizing high convergence speed and robustness against load power fluctuations across different levels of solar irradiation affecting the PV modules. The purpose of this work is to enhance the dynamic performance of the SAPF by cooperatively using a self-tuning filter (STF) based instantaneous active and reactive power method (PQ) with a novel predictive current control, enhance the system resilience, ensure optimal management of the total active power between the PV system, the electrical network and the non-linear load by integrating the functionalities of the SAPF under different levels of solar irradiation and maintain the DC-link capacitor voltage constant. Methods. A novel predictive current controller is designed to generate the switching signals piloted the three phase source voltage inverter, also a novel algorithm of instantaneous active and reactive power is developed, based on STF, to extract accurately the harmonic reference under non ideal grid voltage, also the perturb and observe algorithm is used to extract, under step change of solar irradiation, the maximum power point tracking of the PV module and the PI controller is used to maintain constant the DC-link capacitor voltage of the SAPF. Results. The efficacy of the proposed system is primarily centered on the grid side, and the performance evaluation of the control system is conducted using the STF based PQ algorithm and predictive current control. In addition, comprehensive testing encompasses all modes of operation, including scenarios involving distorted voltage sources, step changes in solar radiation, and variations in nonlinear loads. Results highlight superior performance in both transient and stable states, affirming the robustness and effectiveness of the proposed controllers. Practical value. The total harmonic distortion value of the grid current for all tests respects the IEEE Standard 519-1992. References 21, tables 7, figures 25.

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.

The added value of combining solar irradiance data and forecasts: A probabilistic benchmarking exercise

Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T1611International Energy Agency - Photovoltaic Power Systems - Solar Resource for High Penetration and Large Scale Applications. agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts.The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models.In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input.To ensure a fair evaluation of the different methods, we calculate a skill score that measures the performance of the proposed model relative to that of a trivial baseline model. The closer the skill score is to 100%, the more efficient the method is. Overall, skill scores of methods that use the blended point forecast ranges from 42% to 46% for the intra-hour scenario and 27% to 32% for the intra-day scenario. Conversely, methods that do not use the blended point forecast exhibit skill scores ranging from 33% to 43% for intra-hour forecasts and 8% to 16% for intra-day forecasts.These results suggest that using (a) blended point forecasts that optimally combine different sources of input data and (b) a post-processing with a statistical method to produce the quantile forecasts is an effective and consistent way to generate high-quality intra-hour or intra-day probabilistic forecasts.

CGAformer: Multi-scale feature Transformer with MLP architecture for short-term photovoltaic power forecasting

Accurately predicting the output power of photovoltaic (PV) systems is an effective means to ensure the reliable and economical operation of grid-connected PV systems. Aiming at the characteristics of PV power generation such as strong volatility, high intermittency and obvious periodicity, a hybrid model named CGAformer based on One-Dimensional Convolutional Neural Networks (CNN1D), Global Additive Attention (GADAttention), and Auto-Correlation is proposed for short-term PV power generation prediction. The model uses CNN1D to extract local features and obtains global weights by improving the GADAttetion obtained by additive attention. Auto-Correlation integrates local features and global weights and identifies repeated patterns in the sequence to obtain highly coupled multi-scale features, and finally generates the final prediction results through Multilayer Perceptron (MLP). In order to verify the effectiveness of the model, this paper uses a historical dataset from a PV system located in Uluru, Australia for sufficient experiments. In the comparative experiments, The overall average RMSE and MAE of CGAformer are improved by 6.82% and 20.46% respectively compared with long short-term memory (LSTM). In addition, ablation experiments and seasonal analysis are used to verify the effectiveness of the model and its excellent generalization ability for different seasons.

تصميم متحكم المنطق الضبابي لتتبع أقصى نقطة قدرة لنظام الطاقة الكهروضوئية

This research paper presents the method of fuzzy logic system to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a (PV) module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an (MPPT) system contains a (DC-DC) converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The (MPPT) finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Because of the nonlinear behavior of (PV) module current-voltage characteristics and the nonlinearity of (DC-DC) converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design andimplement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an (MPPT) system consisting of a (PV) module, a (DC-DC) converter, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck- boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that, the fuzzy logic controller is able to obtain the desired outcomes which are actual power generation of solar panels at different irradiance and temperature. Keywords: Fuzzy logic, PV, MPPT, DC-DC converter, MATLAB programming

4E Analysis of Solar Photovoltaic, Wind, and Hybrid Power Systems in Southern Pakistan: Energy, Exergy, Economic, and Environmental Perspectives

4E Analysis of Solar Photovoltaic, Wind, and Hybrid Power Systems in Southern Pakistan: Energy, Exergy, Economic, and Environmental Perspectives

Rapid assessment of solar PV micro-system energy generation in Poland based on freely pvlib-python library

Abstract Poland has experienced a remarkable growth in renewable energy adoption, notably in photovoltaic (PV) solar systems. The majority of installations are prosumer PV micro-installations, exceeding predicted capacity targets outlined in the Energy Policy of Poland until 2040. Despite the significant growth in installed PV capacity, there is still a lack of comprehensive research focusing on fast assessment of energy generation capacity for solar PV micro-systems. This study aims to address this gap by providing a comprehensive analysis of energy production potential across different configurations and locations in Poland. Using geocoding techniques, solar irradiation data from PVGIS database and pvlib-python library, a methodology was developed to rapidly estimate energy generation from 1 kWp solar PV systems . Results reveal spatial disparities in energy yield from solar PV micro installations in Poland, influenced by factors such as geographical location and panel orientation and inclination. Recognizing that the presented energy indicators provide valuable initial parameters for determining solar PV system power output, this data can serve as a critical reference point for stakeholders, assisting them in estimating potential energy generation capacities in different regions of Poland.

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Generating granular-scale surface albedo data is extremely important for solar photovoltaic site planning and to optimise renewable energy yield of bifacial panel installations. The albedo effect brings about a significant increase in power in bifacial photovoltaic systems, compared to their mono-facial counterparts, since the spectral response of bifacial solar panels correlates with the incident solar radiation wavelength on the back of the panel, to provide additional power generation capacity. Thus, harnessing the albedo data at relatively local scales is critical towards boosting solar power generation and providing greater power density in local electricity grids. This paper develops novel modelling approaches to produce high-resolution spectral albedo imagery across the Visible and Near Infrared (VNIR) bands, using the Wavelet-Fusion super-resolution model (i.e., Wavelet-FusionSR) trained with the Learned Gamma Correction approach by applying satellite image enhancement methodology. The proposed Wavelet-FusionSR model utilises the low-resolution moderate-resolution Imaging Spectroradiometer (MODIS) as well as high-resolution multi-spectral Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) satellite images, as critical inputs and ground-truth imagery, respectively, in order to perform sensor-to-sensor deep downscaling, without employing any synthetic or low-resolution satellite imagery data pairs. To augment the proposed deep learning algorithm across the decomposed sub-images of low-resolution inputs, we integrate local and global feature representation learning to train the proposed Wavelet-FusionSR model with Cauchy loss functions. In comparison with five competing benchmark models, the proposed Wavelet-FusionSR model demonstrates performance superiority using quantitative image downscaling metrics and visual assessments of the downscaled images for the visible band of solar radiation. The proposed Wavelet-FusionSR model yielded a Mean Square Error (MSE) of 0.00017, Signal-to-noise-ratio (PSNR) of 37.80, Structural Similarity Index (SSIM) of 0.999 and combined loss, MS-SSIMLoss, based on Multi Structural Similarity and Mean Absolute Error of 2.354 for the Visible Band images, and an MSE of 0.0014, PSNR of 28.43, SSIM of 0.999 and MS-SSIMLoss of 7.426 for the NIR spectral bands, demonstrating high efficacy of the proposed Wavelet-FusionSR method. The Wavelet-FusionSR method therefore attains high-resolution spectral albedo imagery outputs.

Performance analysis of IoT-enabled hydro-photovoltaic power systems considering electrical power mission chains

Synergistically and complementarily managing the operation of hydropower plants and other weather-dependent renewable energy generation plants (e.g., solar energy plants) provides an effective measure to improve the performance efficiency (PE) of renewable energy. However, environmental uncertainty and physical system unavailability pose challenges in assessing the PE of the power generation. This paper proposes an approach to synergistically and complementarily managing the operation of hydro-photovoltaic (HPV) power systems in IoT under uncertain environments and develops a mission chain-based PE model for quantifying the capacity of the fulfilment of a power supply mission to satisfy users’ demand for power. An importance measure-based restoration strategy is then proposed to enhance the PE of an HPV power system (PEPS). The approach is examined by taking a large-scale HPV in China in the case study. The results show that, in winters and in summers, the PEPS is higher than PE of a photovoltaic power system and a hydropower system, respectively, and the PEPS with the importance measure-based restoration strategy is higher than that under the random restoration strategy, respectively. The findings not only lay the foundation for the complementary operation of hybrid renewable energy power systems but also provide a reference for the restoration in case of power cuts.

Enhanced ammonia nitration by Bio-Electrochemical systems with constructed wetlands

The insufficient abundance of electron acceptors for ammonia during electron transfer in constructed wetlands (CWs) results in low nitrification rates. This study developed a green, low-carbon CWs enhanced by a bio-electrochemical systems (BESs-CWs) to achieve efficient ammonia (NH4+-N) removal. Electrode enhancement significantly promoted NH4+-N removal. Compared with traditional CWs, the average removal efficiency of NH4+-N in the BESs-CWs increased from 62.9 % to 90.6 %. The intermittent voltage driven by the photovoltaic power system caused minimal plant stress. However, electrode enhancement significantly affected microbial communities involved in short-path nitrification and denitrification within the biofilm. Specifically, the removal rate of NH4+-N by BESs-CWs under electrode enhancement was increased by 27.7 % compared to traditional CWs, enhancing the electron output of NH4+-N in the BESs-CWs. This system provides a method of ammonia nitration for CWs under poor electron acceptor conditions.

Research on the coupling mechanism of transient overvoltage in large-scale new energy station grid connection

Abstract The large-scale new energy station transmission system is relatively weak, and long-distance transmission is prone to transient overvoltage. The hysteresis of the control loop of the Static Var Generator (SVG) can also contribute to the increase of transient overvoltage. Therefore, this article first establishes a photovoltaic power generation model, SVG model, and photovoltaic grid-connected system. Secondly, the transient overvoltage coupling mechanism of photovoltaic grid-connected systems under various faults such as commutation failure, DC blocking, and DC restart was analyzed through PSCAD simulation. At the same time, the changes in transient overvoltage before and after the installation of SVG were compared, and SVG’s reactive power dynamic behavior was analyzed. Finally, the mechanism of SVG assisting in increasing transient overvoltage was summarized.

Enhancing the integration of PV and coal-fired power plant for low-carbon, low-cost, and reliable power supply through various energy storage systems

The integration of photovoltaic (PV) system and coal-fired power plants (CFPP) through various energy storage systems (ESS) presents a promising strategy for achieving a low-carbon, low-cost, and reliable power supply. This study proposes a low-carbon power system that deeply integrates PV and CFPP through battery, thermal, and ammonia energy storage system (BESS, TESS, AESS). Multi-objective optimizations were conducted to minimize both the levelized cost of electricity (LCOE) and specific CO2 emission while considering constraints related to power supply reliability. Additionally, the influence of technological advancements on reducing LCOE was explored, providing insights into the developmental pathways of these energy storage technologies. The results indicate that: (1) The optimal selection of ESS is related to CO2 emission reduction targets. For less restrictive targets, it is economical to abandon ESS and rely entirely on PV and CFPP. As emission reduction targets progress, TESS becomes economically optimal. In scenarios with strict targets, BESS becomes economically optimal. (2) The combination of PV+CFPP+MultiESS demonstrates superior cost-effectiveness under high-reliability constraints, exhibiting a 7.6 % lower LCOE compared to PV+CFPP+SingleESS at a reliability level of ≥ 99 %. (3) Compared to CFPP retrofitted with CCS, the PV+CFPP+ESS integrated system is more economically favorable for less restrictive CO2 emission reduction targets. However, as the CO2 emission limits become stricter, the CCS-retrofitted technology becomes more economically favorable. (4) To enhance cost-competitiveness, developments should be directed toward reducing the unit investment cost of storage for BESS, while reducing the unit investment cost of power conversion system for AESS. As for TESS, technological advancements across various aspects can effectively contribute to its cost-competitiveness.

A new method to identify optimal operating point for photovoltaic power systems based on completely mathematical model

A new method to identify optimal operating point for photovoltaic power systems based on completely mathematical model

Stochastic Optimization of Onboard Photovoltaic Hybrid Power System Considering Environmental Uncertainties

Environmental uncertainties present a significant challenge in the design of onboard photovoltaic hybrid power systems (PV-HPS), a pivotal decarbonization technology garnering widespread attention in the shipping industry. Neglecting environmental uncertainties associated with photovoltaic (PV) output and hull resistance can lead to suboptimal solutions. To address this issue, this paper proposes a stochastic optimization method for PV-HPS, aiming to minimize greenhouse gas (GHG) emissions and lifecycle costs. Copula functions are employed to establish joint distributions of uncertainties in solar irradiance, ambient temperature, significant wave height, and wave period. Monte Carlo simulation, the bi-bin method, and the multi-objective particle swarm optimization (MOPSO) algorithm are utilized for scenario generation, scenario reduction, and design space exploration. The efficacy of the proposed method is demonstrated through a case study involving an unmanned ship. Additionally, deterministic optimization and two partial stochastic optimizations are conducted to underscore the importance of simultaneously considering environmental uncertainties related to power sources and hull resistance. The results affirm the proposed approach’s capability to reduce GHG emissions and lifecycle costs. A sensitivity analysis of bin number is performed to investigate the tradeoff between optimality and computation time.

Short-Term Photovoltaic System Output Power Prediction Based on Integrated Deep Learning Algorithms in the Clean Energy Sector

Photovoltaic power generation system plays an important role in renewable energy. Therefore, accurately predicting the short-term output power of photovoltaic system has become a key challenge for real-time power grid management. This study focuses on Yingli's green energy photovoltaic system, and uses the convolution neural network and long-term and short-term memory network fusion model (CNN-LSTM) to predict the short-term power. The model integrates CNN's data feature extraction and LSTM's time series prediction ability, showing high accuracy and stability. The experimental results show that CNN-LSTM model has a low mean and variance of prediction error, and the prediction is stable and reliable, and it is consistent in different scenarios. This provides theoretical support for the output power prediction of photovoltaic system based on deep learning.

Thermal stability of magneto-hybridized silicon and aluminum oxides nanoparticle in C3H8O2-Williamson exothermic reactive fluid with thin radiation for perovskite solar power

The need to increase thermal power stability and energy conservation have spurred the interest in various renewable energies. A diverse range of fabrication techniques and architectures have been developed to meet the global energy demand. Perovskite solar power technologies are the next emerging generation of photovoltaic thermal power systems for an enhanced and stable power supply. Thus, this project examines the thin radiation thermal stability of combined magneto-hybrid silicon oxide (SiO2) and aluminum oxide (Al2O3) nanoparticles in exothermic propylene glycol (C3H8O2)-Williamson fluid for perovskite thermal cells improvement. Without particles agglomeration, the fluid flow is influenced by lower wall velocity, Joule heating and Williamson shear stress in a bounded domain. An invariant coupled differential model is obtained through the similarity transformation of the governing model. The solutions to the invariant model is provided using semi-discretized finite difference method. The outcomes revealed that nanoparticles thermal propagation for perovskite power generation is strengthened with rising Brinkman number, radiation, and Frank-Kamenetskii terms. Also, criticality is raised at the unstable thermal region but damped at the stable thermal regime.

Time-domain equivalent model for harmonic simulations of wind and photovoltaic plants

Equivalent models of wind power plants or photovoltaic systems reduce the computational burden relative to detailed models (DMs) during power quality studies. However, existing time-domain equivalent models are ineffective for predicting harmonic currents because they do not consider switching behavior from power converters. This paper proposes a novel time-domain equivalent model, the aggregated harmonic model (AHM), to perform harmonic current prediction in large plants. The AHM uses the National Renewable Energy Laboratory method to equalize the collector system and applies the superposition principle to include the dead time and switching effects. Simulations were executed using the DM and AHM of actual plants. Field measurements were performed to validate the AHM. The results show that the proposed model produced well-matched curves for current waveforms, frequency responses, and harmonic spectra. Consequently, this model is an accurate tool for transmission system operators.

Corrigendum: A comparison of several maximum power point tracking algorithms for a photovoltaic power system

Corrigendum: A comparison of several maximum power point tracking algorithms for a photovoltaic power system