Simulation Of Photovoltaic System Research Articles

An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

Accurate, reliable, and efficient extraction of photovoltaic (PV) model parameters is an essential step towards PV system simulation, control, and optimization. Nevertheless, this problem is still facing great challenges because of its intrinsic nonlinear, multivariate, and multimodal properties. In this paper, a new variant of cuckoo search (CS), adaptive operator selection CS (AOSCS), is advanced for the PV model parameter extraction problems. AOSCS includes two major improvements: (1) an adaptive operator selection mechanism is developed to automatically assign the workloads of exploration and exploitation operators, and (2) the exploration and exploitation operators used in the original CS are modified to promote the exploration capability and reduce the blindness of search, respectively. The performance of AOSCS is firstly validated on CEC 2017 test suite and then it is utilized to solve the parameter extraction problems of five PV models. Moreover, further experiments on two commercial PV modules under distinct irradiance and temperature levels are also conducted to evaluate the practicality of the proposed algorithm. It is manifested that the results yielded by AOSCS are very competitive relative to other parameter extraction approaches. Accordingly, the proposed AOSCS is able to be served as an up-and-coming candidate algorithm for PV model parameter extraction problems.

Simulation of solar photovoltaic system integrated with TEG in presence of hybrid nanomaterial

Simulation of solar photovoltaic system integrated with TEG in presence of hybrid nanomaterial

LCL-T Module Reduction of Offshore Photovoltaic Power Generation System Based on Current Feedback-routh Approximation Method

The rapid development of offshore photovoltaic power generation has attracted more and more attention. Aiming at the problem of poor output power quality caused by the randomness and intermittentness of offshore photovoltaic power generation, LCL filters and isolation transformers are widely used on the AC side of photovoltaic power generation systems to improve the power quality of Grid-connected. Although this method has a good suppression effect on harmonics and DC components, its higher order will result in a larger amount of calculation, especially in the simulation of large-scale photovoltaic grid-connected systems, the operating speed will be too slow. In order to increase the simulation rate while ensuring the quality of the output power, the high order LCL-T (LCL filter and isolation transformer) module of the photovoltaic power generation system is reduced in order. When the transformer is not saturated, the LCL-T module reduction method based on the weighted current feedback method is adopted, and the purpose of simplification is achieved through the zero-pole cancellation of the transfer function; When the transformer is saturated, the LCL-T module reduction method based on the Routh approximation method is adopted, Use advanced mathematics formulas to reduce the order of high-order system equivalent processing. The PSCAD/EMTDC simulation software verifies the off-grid characteristics and simulation rate of the reduced-order model.

Asynchronous co-simulation of photovoltaic power generation system based on FPGA-CPU

In order to realize the real-time electromagnetic transient simulation of grid-connected photovoltaic power generation system with small time step, a heterogeneous multiplexed parallel real-time simulation method based on field programmable gate array (FPGA) and central processor is studied. According to the simulation accuracy requirements of the high and low frequency decoupling module, a differentiated simulation step size is used to solve the problem, and a 1us/50us FPGA-CPU multi-rate real-time simulation platform is constructed, in which the FPGA simulation part meets the real-time requirements of small-step transient simulation through the optimization of the response matching method and the EMTP algorithm, the offline simulation results are compared with Simulink, and the model before and after optimization is analyzed, the real-time electromagnetic transient simulation results are compared with Simulink, and the model before and after optimization is analyzed. By comparing the offline simulation results with Simulink and analyzing the FPGA resource consumption before and after model optimization, the accuracy and real-time performance of the co-simulation method for real-time simulation of grid-connected photovoltaic systems are verified.

An economic sustainability analysis of hybrid solar-biomass system for sugar industries in India

Renewable energy systems have been gaining more importance in developing countries not only due to the increase in concern towards environmental sustainability but also resolving the looming energy crisis that bewilders economic development. Till date, inadequate attention has been paid to the integration of Renewable Energy (RE) sources in the industrial sectors of TamilNadu, India. Since industries have been key drivers of electricity consumption in the past decade, this paper aims at the simulation and analysis of hybrid Biomass-Solar Photovoltaic system for electrification of the process industries, especially sugar industries. The main objective of the paper is to minimize the cost of energy and Green House Gas (GHG) emissions leading to sustainable development. The economic feasibility analysis is performed using Hybrid Optimization Model for Electric Renewables (HOMER) software and the sustainability analysis with the help of Clean Development Mechanism that can be adopted for the sugar industry. The simulation results support the effectiveness of the proposed grid connected solar-biomass hybrid system for the sugar industry with the net present cost of 16 million dollars (M$) combined with emission reductions of 6,266 tons/year.

Enhancing Photovoltaic Cell Parameters Extraction through Grey Wolf Optimizer

Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerical, and metaheuristic algorithms aimed at this task in recent years, the extraction of parameters remains a formidable obstacle. This study employs the Grey Wolf Optimizer (GWO) to extract the five key parameters of the RTC France solar cell. The GWO’s performance is systematically compared with metaheuristic algorithms such as Enhanced Chaotic JAYA (CJAYA) and Performance-Guided JAYA (PGJAYA). The study showcases the prowess of GWO in optimizing PV parameters, marking a significant stride forward in the realm of optimization techniques for PV cell modeling. Through meticulous analysis using MATLAB-SIMULINK, the research unveils the profound effectiveness of GWO in navigating the intricate landscape of parameter extraction within PV systems.

Parameter identification and generality analysis of photovoltaic module dual-diode model based on artificial hummingbird algorithm

Abstract The aim of this study is to propose a photovoltaic (PV) module simulation model with high accuracy under practical working conditions and strong applicability in the engineering field to meet various PV system simulation needs. Unlike previous model-building methods, this study combines the advantages of analytical and metaheuristic algorithms. First, the applicability of various metaheuristic algorithms is comprehensively compared and the seven parameters of the PV cell under standard test conditions are extracted using the double diode model, which verifies that the artificial hummingbird algorithm has higher accuracy than other algorithms. Then, the seven parameters under different conditions are corrected using the analytical method. In terms of the correction method, the ideal factor correction is added on the basis of previous methods to solve the deviation between simulated data and measured data in the non-linear section. Finally, the root mean squared error between the simulated current data and the measured current data of the proposed model under three different temperatures and irradiance is 0.0697, 0.0570 and 0.0289 A, respectively.

Simulation of photovoltaic thermal solar system with new technique for improving electrical performance

Simulation of photovoltaic thermal solar system with new technique for improving electrical performance

Comparison of ground-based and floating solar photovoltaic systems performance based on monofacial and bifacial modules in Ghana

This research aims to compare the energy output potential of land-based and floating bifacial photovoltaic (PV) systems of 50 MW and 400 kW with an existing land-based and floating monofacial PV system of the same dimensions and design in Bui, Ghana. The study uses ground weather files and PV system configurations to compare the energy yield from simulations of the different PV systems based on capacity factor, performance ratio, and monthly, final/annual energy yield. The results show that the bifacial gain of land-based and floating bifacial PV systems is 2.51% and 4.57%, respectively, and the capacity factor and performance ratio of the bifacial PV system are higher than the monofacial PV system. However, the additional energy generated by the bifacial PV system is not significant enough to justify the installation of a new system. Therefore, the study recommends optimizing system parameters such as albedo, tilt angle, and ground cover ratio to 0.5, 15°, and 0.3 respectively to enhance the performance and increase the bifacial gain of the PV system. Overall, this study provides valuable insights into the potential of bifacial PV systems in Bui, Ghana, and highlights the importance of optimizing system parameters for maximum energy output.

Hourly global solar radiation prediction based on seasonal and stochastic feature

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.

A feasibility analysis of PV-based off-grid rural electrification for a pastoral settlement in Ethiopia

Ethiopia’s electric grid relies mostly on hydropower for electricity generation. Compared to metropolitan regions, rural areas have only 5% access to power, and 83% of remote areas rely on traditional biomass energy for lighting and cooking. Close to 60% of the land area in Ethiopia is pastoral, and electrifying from the main grid is a major challenge due to economic, technical, and nomadic reasons. This paper explores the feasibility analysis, design, and simulation of an off-grid solar Photovoltaic system in addition to discussing the complete engagement of national energy policy and a strategic plan for electrifying a pastoral Communities’ in Borena. Using solar radiation base data from the National Aeronautics and Space Administration, a case study is performed at Moyale, Yabelo, and Dire, which have 454 households, 367 households, and 379 households, respectively. Through the HOMER software simulation, the optimization determines the inverter, battery size and number, and solar array’s capacity. The daily energy usage and peak power demand in the pastoral communities of Moyale, Yabelo, and Dire were 498.102 kWh/day, 447.114 kWh/day, and 454.02 kWh/day, or 36.89 kW, 35.41 kW, and 35.68 kW, respectively. Due to the country’s subsidizing of all clean energy costs, off-grid solar Photovoltaic systems are more economically feasible than diesel generators, which have a level cost of electricity of 0.4 US$/kWh. National energy strategic plans and policies ultimately support the full involvement of off-grid solar Photovoltaic electrification at remote sites.

Improvement of power quality of a 200 kW grid-connected PV system

<span lang="EN-US">In order to enhance electricity quality, a grid-connected photovoltaic (PV) system simulation is presented in this study. A 200 kW photovoltaic system is integrated to a utility grid and loads. A 25 kV 3-level insulated gate bipolar transistor (IGBT) bridge converter is used. Through this system integration of a renewable energy source with a non-renewable source is achieved. This system also wins over the intermittent nature of the renewable source (solar energy) and non-reliability of conventional sources (utility grid). Power and signal quality for various signals such as ripple factor and total harmonic distortion has improved. The suggested system is investigated using MATLAB/Simulink environment. Total harmonic distortion (THD) in voltage and current, ripple factor correction is also incorporate in the MATLAB model.</span>

Modeling, Simulation, and Experimental Analysis of a Photovoltaic and Biogas Hybrid Renewable Energy System for Electrification of Rural Community

This research is a numerical study, with simulation and experimental investigation of a novel photovoltaic and biogas hybrid renewable energy system. PVSYST simulation software is used to evaluate the energy generation of photovoltaic (PV) systems installed at optimum tilt angle and interrow spacing, as well as measure the performance ratio and system losses. The results obtained from simulations show that annual energy production from designed PV system is 16 654.80 kWh year−1 and its annual specific energy production is 1556.5 kWh kWp year−1. The average annual performance ratio is about 84.3%, normalized array production is 3.23 kWh kWp day−1, normalized system losses are 0.07 kWh kWp day−1, and normalized system production is 3.16 kWh kWp day−1. With SuperPro Designer software, a model is developed for simulating anaerobic digestion for treating animal manure to produce biogas. The simulation yields 88.13% of CH4 and 6.49% of CO2. The performance of the installed experimental setup is examined for 30 d. The maximum load demand of all houses is 46.902 kWh, whereas maximum energy produced by a hybrid PV/biogas system is 61.06 kWh. The payback period for a hybrid photovoltaic/biogas system is estimated to be 4.47 years and levelized cost of energy is 0.0186 USD kWh−1. The hybrid PV/biogas system results in net CO2 mitigation of 20.45 tCO2(e).

A Feasible MPPT Algorithm for the DC/DC Boost Converter

One of the most promising forms of renewable energy is solar energy. However, efficient exploitation of this energy form is a topic of great interest, especially in obtaining the maximum amount of power from the solar photovoltaic (PV) system under changing environmental conditions. To solve this problem, it is necessary to propose an optimal algorithm. Therefore, this paper presents a feasible maximum power point tracking (MPPT) technique for DC/DC boost converters applied in load-connected stand-alone PV systems to extract the maximum available power. This proposed method is based on the combination of the modified perturb and observe (P&O) and fractional open circuit voltage (FOCV) algorithms. The effectiveness of the proposed technique is verified via time-domain simulation of the load-connected stand-alone PV system using PSIM software. The simulation results show a tracking efficiency with an average value of 99.85%, 99.87%, and 99.96% for tracking the MPP under varying loads, irradiation, and simultaneously varying temperature, load, and irradiation, respectively. In addition, tracking time is always stable at 0.02 sec for changing weather conditions in the large range. Therefore, the results of the proposed method indicate advantages compared to the conventional method.

Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module

One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell’s precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E−03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E−03 and 4.7571E−05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.

Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Distributed generation represents a paradigm shift from the traditional electric grid to localized generation of electric power along with the capability of incorporating renewable energy (RE) sources into the energy mix. Responding to the need for sufficient analysis, simulation, and study of feasibility of distributed generation, this study aims to design a hybrid energy system for a university building and analyze its economic benefits. The viability of existing load forecasting methodologies for energy systems is also presented in this paper. The energy system design was determined through predictive modeling of the load profile of the building using historical data and optimization using machine learning methods, namely auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM). Simulations were run in HOMER Pro. Results show that a grid-connected solar photovoltaic (PV) system installed on the roof coupled with an energy storage system (ESS) will provide the most economic benefits because it yields a reduced cost of energy (COE) per kilowatt-hour (kWh) for the building. This study shows that in line with efforts to transition towards clean energy, hybrid energy systems using RE not only have economic benefits, but can also ensure energy security and environmental sustainability.

Optimization of fractional order PI controller to regulate grid voltage connected photovoltaic system based on slap swarm algorithm

Problems with voltage and stability have arisen as a result of the dramatic development in renewable energy generating units, notably solar energy systems linked to low and medium voltage networks, and the influence of active loads that vary rapidly from time to time during the day. Because reactive power is directly proportional to voltage, its use to renewable energy producing units can only improve their efficiency. Better performance for these controllers is possible via the usage of several controller types. In this paper, we use a salp swarm optimization algorithm (SSA) to design fractional order proportional-integral (FO-PI) controllers, whose job it is to regulate the active and reactive power of solar inverters by compensating for the overvoltage and undervoltage presented by the inverters' ability to absorb and produce reactive power. After that, we compared the FO-PI controller's results to those of the standard PI controller. Grid-connected photovoltaic (PV) system modeling and simulation were performed using the MATLAB/ Simulink modeling and simulation tools. After that, the full PV system was simulated in the most likely situations across a range of grid and weather conditions. We may infer from the simulation results that this model is credible, reliable, and applicable to the analysis of grid-connected PV systems.

Agricultural Grid Connected Photovoltaic System Design and Simulation in Egypt by using PVSYST Software

Agricultural Photovoltaic Systems are a key technology to achieve sustainable development goals by reducing competition between land for food and electricity. In addition, Agricultural Photovoltaic Systems are at the heart of the link between power generation, crop production and irrigation water conservation. The main ecophysiological constraint on crop production under photovoltaics is the reduction of light. It is difficult to recommend shade tolerance for some plant varieties due to insufficient information on shading conditions for most plants. The use of shading panels (photovoltaic panels) requires more crop-specific research to determine the optimal percentage of panels and their placement that will not reduce agricultural yields. Crop yield variation versus field shading and availability to maximize the system require extensive research. This study aims to develop a standard procedure for designing an agricultural grid-connected photovoltaic power generation system for solar power generation in an agricultural area in Bahteem, Egypt. The technical and annual performance of the grid-connected PV system was simulated using PV Syst software. The paper started with a pre-feasibility study of a grid-connected photovoltaic system using PV Syst. Software with an extensive database of meteorological data, including global daily horizontal solar irradiance, and a database of various renewable energy system components from different manufacturers. In this work, a comprehensive literature review of agricultural solar photovoltaic systems is conducted, with a particular focus on grid-connected systems, followed by a design procedure for grid-connected solar photovoltaic systems. The planned photovoltaic system will generate a total of 400 KWp of electricity. This generated electricity can drive down electricity prices by exporting excess electricity to the national grid. In addition, solar power systems are fuel-efficient and have a low environmental impact.

Switch-Averaged Frequency Domain Simulation of Photovoltaic Systems

This paper pushes forward frequency domain (FD) modeling of switched networks aimed at transient simulation, with particular interest in photovoltaic (PV) systems. The PV system simulation is performed via the numerical Laplace transform (NLT) in a sequential (partitioned-time) fashion by using a set of time-windows. The proposed technique enhances existing FD PV models by a) averaging switching functions and b) using sample overlapping to alleviate numerical oscillations due to rise-time phenomenon at time-window interfaces. The proposed enhancements provide a more efficient dynamic simulation compared to both classical single-window full-sample NLT implementation and non-averaged FD PV models. Verification is performed via prevalent electromagnetic transient (EMT) software tools.

Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem

The accurate estimation of model parameters is significant for the simulation, evaluation, control, and optimization of photovoltaic systems. Recently, meta-heuristic algorithms(MHAs) have been proposed to solve the photovoltaic parameter identification problem. However, extracting the accurate and reliable parameters of PV models is still a great challenge, and many HMAs may present unsatisfactory performance due to their premature or slow convergence. Therefore, how to develop algorithms efficiently balancing the exploration and exploitation to improve the accuracy and reliability of the algorithm is extremely important. This paper proposes a Hybrid multi-group stochastic cooperative optimization algorithm (HMSCPSO). In the proposed algorithm, we designed a multi-group cooperation search mechanism to enhance the global search capability: Each group utilized different strategies. The first group used classic velocity and position updates, the second group employed the chaos strategy, and the third group utilized the lévy flight strategy. Through cooperation search between groups to increase the diversity of the population and reduce the possibility of falling into local optimum, but also concentrate some individuals to explore the current global optimum to improve the accuracy of the solution. HMSCPSO and its variants were tested on 27 benchmark functions to verify the proposed algorithm’s effectiveness. Then, the HMSCPSO is applied to solve four PV parameter identification problems of different photovoltaic models. Statistical experiment results demonstrate that the proposed algorithm has excellent advantages compared with other meta-heuristic algorithms in terms of accuracy, reliability, and convergence speed. Therefore, HMSCPSO is expected to be an effective parameter identification method for solar cells and photovoltaic modules.