Modeling Of Photovoltaic System Research Articles

Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Coordinated control strategy of photovoltaic energy storage power station based on adaptive dynamic programming

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range.

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.

Investigating the power and efficiency of the 3D model of the concentrated photovoltaic thermoelectric hybrid system and estimating the power consumption of the water pump for cooling

Solar energy is one of the most useful types of renewable energy. Using solar cells is very common to convert solar energy into electricity. By focusing the radiation on the cells, more energy is radiated in a smaller area of the cell, which is called concentrated photovoltaic (CPV). Due to the concentration of radiation, the temperature of the cell rises which can be converted into electricity through thermoelectric generators (TEG). Present study is a hybrid system of solar cell, thermoelectric generator and heat exchanger (CPV-TEG). By conducting this study, it was revealed that the combined power produced by CPV-TEG reaches its maximum value at a concentration ratio of 40. This increase continues with the increase in fluid flow rate, but according to the power consumption of the pump, it was observed that in all types of heat exchangers, an increase in flow rate of more than 8 liters per hour reduces the output power of the system. The replacement of the secondary heat exchanger with the primary one illustrated that the increase of the channels in the exchanger, despite the increase in the power consumption of the pump, causes a very small increase in the output power and efficiency of the system. Also, replacing the tertiary heat exchanger instead of the secondary exchanger showed that changing the direction of fluid output increases the power consumption of pump in high flow rates.

Marine Predators Algorithm with Deep Learning based Solar Photovoltaic System Modelling and Optimization of Green Hydrogen Production

Green hydrogen production depends on solar energy contains employing renewable solar power to purpose the electrolysis of water, causing the separation of hydrogen and oxygen molecules. This method contains a high potential to address the energy transition challenges caused by weather changes and vital carbon-neutral alternatives. Photovoltaic (PV) based combined energy systems performance as a potential new technological solution for clean and affordable green hydrogen production. Optimizing solar PV systems for the effectual generation of green hydrogen includes maximizing the energy output of solar panels and increasing the entire hydrogen production method. Several researchers and scientists are paid attention to the optimizer and modelling of many blocks developing the PV electrolysis method to acquire the optimum solution. With this motivation, the study presents a new Marine Predator Algorithm with Deep Learning-based Modelling and Optimization of the Green Hydrogen Production (MPADL-MOGHP) technique. The MPADL-MOGHP technique can be employed for determining the optimum operational variables of the water electrolysis procedure relevant to hydrogen (H2) production. Catalyst amount (μg), electrolysis time (min), and electric voltage (V) are the three controlling factors that should be properly detected to increase hydrogen production. The MPADL-MOGHP technique comprises two major stages of operations such as modelling and optimization. Primarily, the DBN model is applied for simulating the water electrolysis procedure designed based on electric voltage, quantity of catalyst, and electrolysis time. Next, the MPA is applied for determining the optimum parameters of the water electrolysis process to maximize the generation rate of the hydrogen. The quantity of catalyst, the electrolysis time, and the electric voltage are applied as decision parameters during the optimization process. The performances of the MPADL-MOGHP system are tested on different aspects. The experimental values highlighted the promising results of the MPADL-MOGHP method over other existing techniques.

A novel economic and technical dispatch model for household photovoltaic system considering energy storage system in “Duhok” City/Iraq as a case study

Photovoltaic (PV) systems harnessing solar power to generate electricity have gained widespread adoption worldwide due to clean innovations. The geographic position of the Kurdistan region, north of Iraq, is very favourable for the photovoltaic power generation system. In Duhok City, the average daily hours of sunshine per year are 8.5 h; this is a good point, which makes installing photovoltaic systems more interesting because the performance of PV systems depends mainly on solar radiation. This study analyses the design of a PV system that supplies electricity to households in Duhok City during the period when there is no general electricity considering the economic and technical dispatch aspects. The system is to be designed to power the household for 10 h (Grid-connected PV system) since it is assumed that the general electricity supply only lasts for 10 h per day. The system will be an alternative source of electricity to the local generators. The full design of the proposed system is implemented, so that the data on the electrical load and site radiations of a specific household at the site under consideration are collected during the design stages. Further, the technician's and economic evaluations are considered as well during each step of analysis and selection for system components. The cost estimate was based on the following data: the electrical load of the house is 6239.1 Wh/day, the electricity cost is $0.005 per Wh and the total investment amount is $3475. The ultimate outcomes, when juxtaposed with the local expenses of diesel generators and PV solar systems, indicate that within 4.5 years, PV solar systems will recoup their initial costs, providing a clean and noise-free energy source thereafter.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

Global MPPT controllers for enhancing dynamic performance of photovoltaic systems under partial shading condition

This research focuses on the development and evaluation of global maximum power point tracking MPPT controllers for enhancing the dynamic performance of Photovoltaic (PV) systems under partial shading conditions. Three different controllers, namely Proportional-Integral (PI), Fractional-Order PI (FOPI), and Fuzzy Logic Controllers (FLC), are studied in conjunction with a modified Incremental Conductance (IC) and Fractional Open-Circuit Voltage (FOCV) algorithms. The proposed controllers with a novel optimization algorithm called Atom Search Optimization Algorithm (ASOA) are compared with classical control. The objective is to investigate the effectiveness of the global MPPT controllers in achieving higher tracking accuracy, faster convergence, and improved stability under varying shading conditions. The study involves extensive simulations using a representative PV system model subjected to different shading scenarios. The results demonstrate that the proposed global MPPT controllers with the ASOA algorithm, when integrated with the modified MPPT algorithms, outperform the classical control techniques in terms of dynamic performance and response to partial shading conditions. The suggested ASOA-based fuzzy IC MPPT controllers can reach the MPP more quickly with a shorter settling time and a better tracking response. Furthermore, with a reduced settling time of about 1.9 % and an average power harvesting efficiency of over 97.9 %, ASOA-based fuzzy IC MPPT controllers offer the best harvesting characteristics. The controller exhibits superior tracking accuracy and stability, mitigating the effects of shading-induced multiple peaks in the P-V curve.

Hybrid Solar PV and WECS Power Quality Improvement by STATCOM

The increasing integration of off-grid energy sources in the utility load has ended up resulting in elevated standards regarding power quality, voltage stabilisation purposes, and efficient energy use. The electrical network Wind and solar energy have been considered to be among the most reliable renewable energy sources. However, the self-sufficient operation of either photovoltaic or wind energy systems does not offer a highly consistent source of electricity production owing to the unpredictability of the wind and solar irradiance availability. As a result of this, a variety of solar and wind power generation systems have the ability to produce a very reliable and promising electrical supply. In the present study, a hybrid wind and photovoltaic panels system model has been provided. This specific type of technology possesses plenty of possibilities for its users from afar. This particular kind of technology is highly beneficial in inaccessible or offshore locations where integrating with the grid is not very cost-effective. Nevertheless, integrating power electronics to dissipated generation (DG) systems brings substantial issues with power quality, which include reactive power adjustment and harmonic development, which throws off the system for power distribution. The present study proposes a simulation framework for a hybrid wind-photovoltaic generation system. The system's efficiency in gridconnected mode is assessed. Calculations of total harmonic distortion (THD) at various speeds of wind were used for assessing the wind-SPV hybrid system's power quality. This hybrid system's power quality has been enhanced owing to its employing of STATCOM.

Novel approach to remote rural heating: Direct coupled photovoltaic electric heater underfloor heating system with phase change materials

Limited access to grids and the unavailability of traditional energy sources present significant challenges to effective heating in remote rural. This paper proposes a direct coupled photovoltaic electric heater underfloor heating system with phase change materials (direct coupled PVEH-UFHs), which eliminates the reliance on the power grid or conventional energy sources for heating in remote areas. The model of the direct coupled photovoltaic electric heating system (direct coupled PVEHs) and the building model with PCM floor were created and the accuracy of the direct coupled PVEHs model was verified experimentally. The study proposes using the total heating achievement rate (THAR) as the evaluation index and identifies the critical parameters affecting the system performance through sensitivity analysis. The results indicate that the photovoltaic capacity factor (PVCF), PCMs thickness and melting temperature are the key parameters affecting the heating performance of the system. The recommended 1.4 PVCF and PCM melting temperature of 294 K. The minimum payback time for system configuration that achieves 90 % THAR is 16.2 years. In summary, this paper proposes a design method and evaluation criteria for direct coupled PVEH-UFHs suitable for energy-efficient heating in remote areas.

Enhanced FOPID Controller Based Cascaded Inverter for Grid-Connected Photovoltaic System

An enhanced controller is proposed to investigate grid-connected photovoltaic systems employing cascaded two-level inverters. The primary focus is on optimizing power output, achieved through the development, modeling, and testing of photovoltaic systems using the suggested upgraded controller. This advanced controller, specifically a Fractional Order PID (FOPID) controller, incorporates the Ant-Lion Optimizer (ALO) algorithm for enhanced performance compared to conventional controllers. The FOPID controller’s reliability is emphasized, and further improvements are pursued by optimizing gain parameters through the prescribed method. Power supply under both schemes is examined, and the controller’s performance in extracting maximum power under specified operating conditions is deemed satisfactory. To validate the proposed approach, practical implementation is conducted using the MATLAB/Simulink platform. The effectiveness of proposed and conventional methodologies for analyzing the id and iq currents is assessed. A comparison is drawn between the suggested approach and current methods such as the base controller, GSA, and ABC methodologies. In comparison to the existing methods, the suggested FOPID controller demonstrates superior performance in maintaining the DC link voltage, achieving an efficiency of 94%, while the GSA method reaches 91.5%, the ABC method achieves 91%, and the base controller achieves 90%.

Novel Multi-Port Converter for Distributed MPPT Operation in Solar PV System

Solar photovoltaic (PV) systems continue to be the most prevalent renewable energy resource despite the presence of numerous limitations. A power discrepancy between PV modules on a large scale may result in power dissipation throughout the entire PV system. This particular paper proposes an efficient multi-port converter for distributed maximum power point tracking operation (D-MPPT) for a solar PV system. The operation details of the proposed multi-port converter along with analytical waveforms are presented in this paper. To implement the D-MPPT approach in the proposed multi-port converter, a detailed analysis of mathematical modeling of solar PV systems with a mismatch of PV power and voltage stabilization approach is done. In addition, the proposed approach eliminates the need for additional current sensors and semiconductor components to overcome the effect of mismatched power in the PV system. To validate this, the prototype has been built and integrated with the real environment of the solar PV system. To verify the operation, a detailed simulation study and experimental investigation have been carried out and presented in this paper. After a detailed investigation and discussion of measured results and analysis, it is concluded that the proposed multi-port DC-DC converter is the most suitable solution for solar PV applications.

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

A modified particle swarm optimization rat search algorithm and its engineering application.

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm's accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population's diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.

Sustainable solar energy development in Zakho, Iraq: a techno-economic and environmental assessment

This study presents a solar energy roadmap aimed at attracting investors to capitalize on the abundant solar resources in Zakho, Iraq, for clean energy technology. The objective is to mitigate global warming effects caused by fossil fuel combustion and promote sustainable technological development. To end this, the study employs RETScreen Expert software to validate the techno-economic and environmental viability of a grid-connected solar photovoltaic system utilizing climatic data from the Astronaut Office Information System (NASA) Database. Additionally, the Photovoltaic Geographical Information System (PVGIS) modeling method determines optimal angles for installing residential rooftop Photovoltaic (PV) systems across selected locations. Results indicate that all selected locations are suitable for solar photovoltaic projects, with Rezgari identified as the optimal district due to its highest annual solar radiation of 1863.117 kWh/m2 and the highest annual electricity production at 7905.38 kWh. The capacity factor (CF) values fall within the range of 17.395-17.574%. The study reveals an annual greenhouse gas (GHG) emissions reduction ranging from 6.9876 to 7.1123 (tCO2) across four districts. Considering the financial and environmental indicators, Rooftop Photovoltaic Systems emerge as a sustainable and efficient solution to enhance the environment and prove economically viable in Zakho City.

Particle swarm optimization for enhanced maximum power point tracking: design and implementation in Proteus

This study introduces a photovoltaic (PV) system model tailored for PV design, incorporating a particle swarm optimization (PSO) MPPT technique to achieve optimal efficiency, swift responsiveness, and cost-effectiveness. To initiate, a PV module model is formulated within Proteus using SPICE coding. Subsequently, an experimental test setup is deployed to authenticate and validate the model. Following this, a PSO-based MPPT algorithm is proposed, which overcomes the limitations of conventional perturb and observe (P&O) and incremental conductance MPPT methods, notably reducing the reliance on mathematical divisions. To substantiate the effectiveness of the proposed approach, both methodologies are implemented on an affordable Arduino Uno platform utilizing the simulated PV module model. The outcomes highlight that the PSO-based MPPT algorithm excels in terms of rapid response (0.09 s), minimal steady-state oscillation, and an impressive 99 percent efficiency.

Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning

Power forecasting in large-scale electrical systems, comprising photovoltaic (PV), solar, and wind power, faces challenges due to geographical diffusion and temporal variations. Despite numerous studies, the disparity between predicted and actual generation remains a significant issue. This study utilizes historical power and atmospheric data from diverse plants, employing preprocessing techniques to enhance quality and reduce noise. K-Means clustering is applied to the dataset, optimizing deep learning training periods and increasing accuracy. A resilient hybrid deep learning model is proposed for microgrid (MG) power forecasting, encompassing preprocessing, model training, and assessment stages. Mathematical models for PV systems, battery storage, and wind systems, along with a K-means clustering algorithm, contribute to accurate forecasting. The recurrent neural network based on gated recurrent unit architecture outperforms traditional algorithms, demonstrating superior accuracy, and reduced errors in extensive experimental analyses. Pearson coefficients reveal associations between different power production forms, emphasizing the potential of hybrid renewable energy clusters to enhance forecasting. Case studies illustrate the partial controllability of concentrated solar power production, reducing overall renewable energy cluster unpredictability. The proposed method showcases the efficacy of the hybrid model in addressing challenges and improving accuracy in large-scale power forecasting.

Quantifying spectral albedo effects on bifacial photovoltaic module measurements and system model predictions

AbstractWe provide a comprehensive analysis of the effect of spectral albedo on photovoltaic (PV) module measurements and system model predictions. We demonstrate how to account for albedo in indoor bifacial device measurements by adjusting the applied irradiance using the scaled rear irradiance method, exemplified on fabricated silicon heterojunction (SHJ) modules. System model performance is studied using a detailed 3D finite‐element model, DUET, for fixed‐tilt and horizontal single‐axis tracked (SAT) arrays between 15 and 75°N. Spectral effects cause variations in measured SHJ module short‐circuit current up to 2% and efficiency variation up to 0.3% abs. We further demonstrate that rear‐side spectral mismatch factors (SMMs) resulting from including or omitting spectral albedo in PV system modeling vary between ±13%, while total (front+rear) SMMs vary up to 3%, depending on the deployment configuration and latitude. SAT array SMMs are weakly correlated with latitude, while fixed‐tilt array SMMs increase with latitude, driven by an increasing proportion of ground‐reflected light on the front‐side of modules. Ground‐reflections can constitute between 2% and 32% of total incident module irradiance, with notably high (>10%) contributions for fixed‐tilt arrays at high latitude. Effects of spectral albedo are most significant for: (1) fixed‐tilt deployments at high latitudes, (2) wide bandgap technologies such as perovskite and cadmium telluride cells, (3) albedos which vary steeply over the technology's absorption range, and (4) high albedo ground covers. Overall, we demonstrate that omitting spectral albedo effects can result in PV measurement and system‐level modeling uncertainties on the order of several percent in these cases.

Energy based techno-economic and environmental feasibility study on PV/T and PV/T heat pump system with phase change material-a numerical comparative study.

A sustainable, affordable, and eco-friendly solution has been proposed to address water heating, electricity generation, space cooling, and photovoltaic (PV) cooling requirements in scorching climates. The photovoltaic thermal system (PV/T) and the direct expansion PV/T heat pump (PV/T DXHP) were numerically studied using MATLAB. A butterfly serpentine flow collector (BSFC) and phase change material (PCM) were assimilated in the PV system and MATLAB model was developed to evaluate the economic and enviroeconomic performance of the PV/T water system (PV/T-W), PV/T PCM water system (PV/T PCM-W), the PV/T DXHP system, and the PV/T PCM heat pump system (PV/T-PCM-DXHP). In this study, annual energy production, socioeconomic factors, enviro-economic indicators, and environmental characteristics are assessed and compared. Also, an economic, environmental, and enviro-economic analysis was conducted to assess the commercial viability of the suggested system. The PV/T PCM-DXHP demonstrated the highest electrical performance of 53.69%, which is comparatively higher than the other three configurations. The discounted levelized cost of energy (DLCOE) and payback period (DPP) of the PV/T PCM-DXHP were ₹2.87 per kW-h and 3-4years, respectively, resulting in a total savings of ₹67,7403 over its lifetime. Furthermore, installing this system mitigated 280.72 tonnes of CO2 emissions and saved the mitigation cost by ₹329,700 throughout its operational lifecycle.

New Model of Photovoltaic System Adapted by a Digital MPPT Control and Radiation Predictions Using Deep Learning

Forecasting solar radiation is one of the most useful impacts that can give us a deep vision on maintaining the integrity of solar systems. The availability and ease of use of the data make this process simpler. Predictions may be produced using various data sources. In fact, there are two different forms that can be identified. The first one was the use of historical solar radiation data, while the second one was the use of other meteorological parameters. The availability and choice of the data source can have an effect on the choice of the model and methods used. Our proposed article aims to take research as an example to review the solar radiation situation in Morocco and outline the methods of predicting solar radiation using different machine learning and deep learning methods like ANN, MLP, BPNN, DNN, and LSTM, which are used in different regions in Morocco.