Modeling Of Photovoltaic System Research Articles

A Novel Combination Algorithm of Different Methods of Maximum Power Point Tracking for Grid-Connected Photovoltaic Systems

Abstract This paper proposes a novel algorithm to combine two or more techniques of maximum power point tracking (MPPT) to increase the output dc power and the efficiency of photovoltaic (PV) arrays. Different MPPT methods have dissimilar responses for the same environmental circumstances. The combination of multiple methods has the advantage of ever acquiring maximum power as the environmental conditions change. The proposed algorithm is used to enhance the selection of the most suitable duty cycle of the combined methods, based on the power-voltage characteristics, to get the best tracking response. Multiple classical and/or artificial intelligence (AI)-based MPPT methods can be combined based on this selection algorithm. To demonstrate its effectiveness, two examples are illustrated. The first one is the combination of two classical MPPT methods, which are the incremental conductance (INC) and the perturb-and-observe (P&O). The second example is to combine two AI-based MPPT, which are the artificial neural network (ANN) and the fuzzy logic control (FLC). The simulation results of the application of the proposed algorithm to a grid-connected PV system model justify its capability to acquire better static and dynamic responses than the responses of individual MPPT methods.

A dynamic energy management system using smart metering

Smart grid technologies are a catalyst for the modernisation of the electrical system whilst satisfying all electrical power stakeholders. The application of intelligent systems results in more flexibility and reliability. This paper presents a dynamic energy management system for a microgrid connected to a grid for residential application. The system models a smart metering system to collect data from different components of the electrical system. A grid-tied photovoltaic and energy storage system model is optimally designed. The model uses the framework of a smart grid based on demand response and energy pricing to coordinate the energy flow of a home. Three optimal control scenarios are formulated, where the opportunity energy is considered to be injected to the main grid. These scenarios are two linear methods (open and closed-loop models) and a quadratic approach based on model predictive control. It was observed that the energy storage system plays an essential role in the context of energy-saving and gain from the demand side. The models provide benefits in terms of energy-saving and energy cost. The performance of dynamic modelling is validated with the experimental data from the smart metering system.

Optimization and comparative analysis for a stand-alone hybrid model of PV, wind turbine, and natural gas generator system in remote area – A case study in Belu

This paper proposes a hybrid model of photovoltaic (PV) and wind power generation based on the off-grid system for the rural area. The off-grid system is considered more suitable to be applied in the remote area since it has a lack of access to the closest grid network. Belu (9°10.5‘N and 124°59.4'E), Indonesia, is chosen particularly as the model due to its high potential resources of renewable energy. The data on wind and solar resources are obtained from Meteonorm. The system schematic consists of the wind turbine, photovoltaic (PV), battery, converter and electrical load. A small-size natural gas generator is used as the back-up. The data is processed by using HOMER Optimizer based on the derivative-free algorithm. The total Net Present Cost (NPC) and the Levelized Cost of Energy (LCOE) for the optimized system configuration are $95,828 and $0.106, respectively. The performance of the system is represented by the percentage value of the excess electricity and unmet electric load which are 13% and 0.39%, respectively. The carbon dioxide produced is only 10,070 kg/year. In general, the optimized system is much better compared to the generator-only system based on the economical, electrical, and environmental aspects.

Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data

This study provides several models for accurately computing the annual optimum tilt angle for fixed solar photovoltaic arrays or solar collectors, in any location of the world. The optimum tilt angle that maximizes the annual energy yield can therefore be easily calculated in the absence of meteorological data and simulation software tools. The proposed models are calculated using global horizontal radiation data collected from 2551 sites across the world. In the process, well-established submodels have been selected to estimate the hourly irradiance on any possible inclined surface, and its corresponding annual energy yield. After selecting the optimum angle for each location, through a regression analysis, a mathematical model that calculates annual optimum angles as a function of latitude has been developed. Furthermore, regression techniques such as neural networks and decision trees have been compared with the polynomial models. Finally, the results are compared to those obtained from high-quality 1-min measured irradiance data obtained at 52 research-class stations from the World Radiation Monitoring Center–Baseline Surface Radiation Network, providing a remarkably high number of validation data points. The results are analyzed, validated, and compared with previous research proposals proving the good performance of the proposed models.

Comparative analysis of monofacial and bifacial photovoltaic modules for floating power plants

The aim of this study is to create usable commercial simulation software tools for photovoltaic (PV) systems even in the case of floating applications. Using the experimental data of a plant installed at the “Enel Innovation Lab” in Catania (IT), adequate heat exchange coefficients of the software's thermal models were found, which allowed us to take into account the thermal effects for these types of installations. An optimization of the sizes for mono- and bifacial floating systems was carried out. The simulated data for a ground system were compared with the floating data through performance indices. In addition, monofacial models were compared with bifacial. The optimized variables are the tilt angle and the pitch, whereas for the bifacial systems, the module elevation is also considered. The albedo is a sensitive factor mainly for the bifacial modules, so it was considered a parameter in the different design solutions. The simulations were performed by two specialized commercial software programs, where different PV system models are implemented. The normalized annual energy yield was chosen as a meaningful parameter with which to compare the different solutions. As the analysis of PV systems is highly site dependent, the study was developed for two locations, characterized by different diffuse and albedo solar irradiance components, specifically at high latitudes (Frankfurt, DE) and at intermediate latitudes (Catania, IT).

Effect of head and PV array configurations on solar water pumping system

Nowadays, the photovoltaic (PV) to power the water pumps is a newly accepted technology with few challenges associated with high initial cost and lower system efficiency. The literature suggests the majorly of commercial pumps at present, underperform, due to poor system design, low efficiency and mismatch of components. In this paper Matlab Simulink model of a solar photovoltaic (PV) water pumping system (SPVWPS) has been created. Such a model; demonstrates the system characteristics under different operating parameters. This model can successfully and completely check the feasibility, size and optimize the system for its complete utilization. The effect of various parameters such as operating head of the pump, radiation, PV array configurations on the performance of the SPVWPS has been discussed and presented in the paper.

Multiswarm spiral leader particle swarm optimisation algorithm for PV parameter identification

The ambition for more photovoltaic (PV) systems, the concern for optimal utilisation, and the uncertainty associated with its energy production have led to an accelerated research in the PV field. The precise modelling of PV systems under any operating condition is necessary to obtain accurate and reliable estimates of PV model parameters. In this paper, a novel multiswarm spiral leader particle swarm optimisation (M-SLPSO) algorithm is proposed to solve the PV parameter identification problem. The proposed M-SLPSO uses several swarms with different search mechanisms: each swarm is guided by a leader with a different spiral trajectory. In addition, the swarms can exchange search mechanisms and agents can migrate between swarms, thereby enabling a good balance between exploration and exploitation mechanisms. This algorithm maintains a diversity of exploratory trajectories throughout the entire search process when constructing new solutions, mitigating population stagnation, and premature convergence. Furthermore, it adapts to the optimisation problem that is being solved and simultaneously explores different regions of the search space. The performance of the proposed M-SLPSO was evaluated and compared with other state-of-the-art metaheuristics by applying it to some benchmark functions and to the PV parameter identification problem, which considered two case studies: one using a standard dataset and the other using eight experimental datasets (under different operating conditions). Comparative and statistical results comprehensively indicate that the proposed M-SLPSO has an extremely competitive performance and can determine highly accurate and reliable solutions.

Modelling of Photovoltaic, Fuel Cell and Battery Hybrid System Connected to Non-Linear Load

Modelling of Photovoltaic, Fuel Cell and Battery Hybrid System Connected to Non-Linear Load

Modelado, diseño y simulación de sistemas de micro-generación solar fotovoltaicos utilizando MATLAB

La investigación tuvo por objetivo realizar un modelado, diseño y simulación de sistemas de micro-generación solar fotovoltaicos utilizando MATLAB como aporte de la Universidad Laica Eloy Alfaro de Manabí, a la conformación de alternativas energéticas viables en beneficio de la población ecuatoriana. Se desarrolló en función del enfoque cuantitativo, tipo descriptivo con apoyo de modalidad proyecto factible, se procedió a construir el modelado, diseño y simulación, el cual se presenta en una fase descriptiva matemática y otra de simulación, desplegadas en el apartado de propuesta tecnológica. Mediante el estudio por separado de cada elemento del sistema solar fotovoltaico aislado, se ha profundizado en su comportamiento dinámico. Además, ha permitido el diseño de una herramienta o aplicación parametrizable según las características técnicas de cada elemento del sistema, permitiendo simular sistemas solar fotovoltaicos incorporando datos reales de radicación solar y temperatura del sitio físico donde se realiza el estudio.

Load stability analysis of Kazakhstan photovoltaic power system based on data visualization

Traditional methods can not track the load migration of Kazakhstan photovoltaic power system dynamically, and the analysis results have limitations. Therefore, this paper introduces the data visualization method to analyze the load stability of Kazakhstan photovoltaic power system. The simulation model of photovoltaic power system is built to set the model parameters. The system stability node is obtained by using the principle of power flow calculation. The load stability analysis of the system is realized by controlling the output impedance of boost converter. In order to verify the effectiveness of the proposed method, a comparative experiment was designed. The results show that this method can control the operation state of Kazakhstan photovoltaic power system, and has a good effect on the load stability analysis of the system.

Implementing Discrete Model of Photovoltaic System on the Embedded Platform for Real-Time Simulation

This paper presents the development of a discrete model of a photovoltaic (PV) system consisting of a PV panel, Maximum Power Point Tracking (MPPT), a dual-axis solar tracker, and a buck converter. The discrete model is implemented on a 32-bit embedded system. The goal of the developed discrete PV model is to provide an efficient way for evaluating several algorithms and models used by the PV system in real-time fashion. The proposed discrete model perfectly matches the continuous and discrete model simulated with MATLAB-SIMULINK. The real-time performance is tested by running the model to simulate the PV system, where the fastest time sampling of 1 ms is achieved by the buck converter model, while the longest time sampling of 100 ms is achieved by the solar tracker model. Moreover, a novel method is proposed to optimize the net energy, which is calculated by subtracting the energy consumed by the tracker from the PV energy generated. The proposed net energy optimization method varies the operation time interval of the solar tracker under high and low solar irradiation conditions. Based on the real-time simulation of the discrete model, our approach increases the net energy by 29.05% compared to the system without the solar tracking and achieves an increase of 1.08% compared to the existing method.

Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs

The utilization of multi-junction solar cells with high efficiency is still not widespread for terrestrial power applications. These solar cells, composed by several material layers, reach high efficiencies but its cost is expensive, more of 100 higher than classical silicon solar cells. Thus, one solution consists to use reduced sizing solar cells associated with optics mounted on solar tracker to concentrate the solar beam. Numerous meteorological parameters such as beam solar irradiance, ambient temperature and air mass and especially spectral characteristics of sun radiation are involved in the conversion process and are generally used as inputs in power models. Several models from literature, different by their form and by the number and type of input variables, are presented; based on this state-of-art, some similar models are selected and tested on two experimental CPV systems located on two different sites, Ajaccio and Le Bourget du Lac. Then, an operational model of electrical power using inputs easily measured and available for a solar CPV plant operator is developed. It could be used as a decision-aided tool for investors in providing an estimation of the energy production capacity of the CPV systems on the future implantation site. This established model based on data measured on the CPV system in Ajaccio estimates the produced power with a root mean square error of about 5% on the two sites using only a reduced number of inputs.

Store-on grid scheme model for grid-tied solar photovoltaic systems for industrial sector application: Costs analysis

A store-on grid scheme model is proposed to facilitate grid-tied rooftop solar photovoltaic (PV) systems implementation in developing countries. Using Uganda as a case study, the proposed model’s costs analysis results were compared to time of use rating model for energy consumption results of an industry by considering solar PV system performance and daily energy demand. The proposed model’s results revealed that the prosumer should be charged a fee of 5.14 ¢/kWh by the grid operator for storing solar PV generated energy on battery energy storage system. Also, the utility operator could sellback energy to the prosumer at 17.03 ¢/kWh under the proposed model. Comparison of the models’ results revealed that the proposed model reduces the energy costs of the prosumer by about 12.5% compared to under time of use rating model. Also, implementation of rooftop solar PV by an industry could guarantee energy security by meeting about 84% of the annual energy demand by using solar PV energy generated. Also, under the proposed model, the prosumer could feed about 20% of total annual generated solar PV energy to the grid. The proposed SoG model is replicable and applicable to any location worldwide with the utilization of the appropriate datasets.

High-economic PV power compensation algorithm to mitigate voltage rise with minimal curtailment

Increasing photovoltaic (PV) penetration has caused overvoltage problem in distribution systems. Power curtailment that includes power absorption of PV inverter and charge of battery energy storage is usually used to improve the voltage profile. Most methods prefer to use an excessive local power curtailment to regulate voltage. This inevitably reduces PV generation economy. Hence, a coordinated power compensation algorithm (PCA) is proposed in this paper to regulate bus voltage, aimed at reducing the total power curtailment while serving requisite load demand. Selecting primary control nodes and optimal PV compensation orders based on sensitivity sequence, our PCA achieves an effective voltage regulation with the minimal power curtailment. Final results on a tested PV system model verify the effectiveness.

A Six Parameter Single Diode Model for Photovoltaic Modules

AbstractThe modeling of photovoltaic (PV) systems is substantial for the estimation of energy production and efficiency analysis in the PV systems under the changing environmental conditions. A PV model mathematically expresses the electrical characteristic of the PV modules according to temperature and irradiance. The most popular electrical circuit models are the single-diode model (SDM) and the double-diode model (DDM). Considering accuracy and complexity, SDM was used in this paper. In the equivalent circuit model used to estimate the electrical behavior of the PV modules, the parameter estimating has become an optimization problem. In recent studies, it is seen that metaheuristic algorithms are often employed in solving this optimization problem. In this paper, a new six-parameter PV model is proposed to improve the accuracy of the five-parameter SDM, taking into account the temperature dependence of the series resistance. Particle swarm optimization (PSO) and a couple of metaheuristic algorithms have been executed to estimate six unknown parameters of the proposed model under standard test conditions (STC: 25 °C, 1000 W/m2, AM1.5) using current–voltage (I–V) data of PV module. In order to evaluate the performance of the proposed method under the changing environmental conditions, it was compared with the three methods commonly used in the literature. Accuracy of the proposed model has been indicated by the root mean square error (RMSE) within the range of current data and the model current values. Simulation results demonstrate that the proposed model can predict the I–V curve for the PV modules with high accuracy.

Modeling and Parameter Optimization of Grid-Connected Photovoltaic Systems Considering the Low Voltage Ride-through Control

The asymmetric faults often cause the power grid current imbalance and power grid oscillation, which brings great instability risk to the power grid. To address this problem, this paper presented a modeling and parameter optimization method of grid-connected photovoltaic (PV) systems, considering the low voltage ride-through (LVRT) control. The harmonics of the grid current under asymmetric faults were analyzed based on the negative-sequence voltage feedforward control method. The notch filter was added to the voltage loop to filter out the harmonic components of the DC bus voltage and reduce the harmonic contents of the given grid current value. The proportional resonant (PR) controller was added to the current loop. The combination of these two components could reduce the 3rd, 5th, and 7th harmonics of the grid current and the output power fluctuation. Then, the parameters of the inverter controller were identified by the adaptive differential evolution (ADE) algorithm based on the sensitivity analysis. The effectiveness of the proposed method was compared with two other strategies under the asymmetric grid faults. The suppression of DC bus voltage fluctuation, power fluctuation, and low-order harmonics of the grid current all had better results, ensuring the safe and stable operation of the PV plant under grid faults.

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy.

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.

State Estimation for Situational Awareness of Active Distribution System With Photovoltaic Power Plants

The real-time monitoring of the grid-connected renewable energy generations is of great significance to the operation and control of active distribution systems (ADS). Considering the detailed photovoltaic (PV) system model and ambient influencing factors, this paper develops a state estimation framework for the ADS with integrated PV power plants. Firstly, suitable models of PV arrays and power converters for steady-state state estimation are developed. Based on these models, the state variables of a PV generation system are selected, and the measurement equations are derived. Finally, combined with the state estimation model of distribution networks, a framework for state estimation and bad data processing of an ADS with PV power plants is presented. A parameter estimation approach for PV power plants based on multiple measurement scans is also developed in order to address the practical situation of unknown model parameters. Simulation results show that the proposed framework can effectively capture the operating state of the distribution network and PV power plants. In addition, it enhances the estimation accuracy and bad data processing capability compared to conventional state estimation frameworks.

Equivalent Model of a Three Phase System of Photovoltaic Generation to Analyze Voltage Variations in a Radial Distribution System

In this paper, the modelling of a three-phase photovoltaic system, for analyzing voltage variation in a radial distribution system, is presented. The radial distribution system is represented by a benchmark which is widely used in the analysis of distribution systems with distributed generation, and electrical microgrids. The parameters estimation of this model is performed by selecting the aerial distribution of conductors and then calculating the sequence components. Moreover, a model of a three-phase photovoltaic generation system for analyzing voltage variations is proposed. The model represents an array of photovoltaic panels, a dc/dc converter with its control system, and a three-phase inverter. The software MATLAB/Simulink is chosen to simulate both the distribution and the photovoltaic systems. All the components of the three-phase photovoltaic system are parametrized with information of commercial equipment. To facilitate the implementation of the system model in the analysis program, reduced models of its components are selected. Finally, the proposed model of the three-phase photovoltaic system is validated by simulating single-phase faults along the feeder and changes of irradiance over the photovoltaic generators and observing the voltage behavior in one node of the distribution system. The results show that irradiance changes and single-phase faults affect the voltage behavior depending on the photovoltaic penetration level and the generators location.

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models