Photovoltaic Generation System Research Articles

Enhancing Low Voltage Ride-Through Resilience using Empowering Solar Energy Strategies

The rapid advancement of Low Voltage Ride-Through (LVRT) technology has been highlighted by the exponential rise of photovoltaic (PV) generation systems. An extensive analysis of LVRT methods specifically designed for PV generating systems is presented in this research. Important topics like main causes, current control schemes, power balance and overall solutions are the main focus of the review. Moreover, it provides comprehensive insights and elaborates on integrated solutions intended for large-scale PV systems. The objective of this research is to improve the resilience and reliability of renewable energy sources, especially solar photovoltaic systems, against grid disruptions and voltage changes by investigating LVRT approaches in solar power systems. Enhancing the integration of solar power systems into current electrical grids, reducing downtime, and optimizing system performance are the objectives in order to accelerate the shift to a more resilient and sustainable energy infrastructure.

Nonlinear control of three level NPC inverter used in PV/grid system: comparison of topologies and control methods

With the passage of time, the importance of using renewable energy systems to overcome energy consumption and improve the quality of the grid has emerged through the use of nonlinear control techniques and reliance on advanced types of inverters such as multi-level inverters. This research is focused on comparing two grid-connected converter topologies in a photovoltaic (PV) generation system connected to a three-phase grid that serves a non-linear load. Additionally, the study explores two different control techniques applied to this converter, evaluating their effects on the total harmonic distortion coefficient. A comparison has been made between the traditional inverter and the three-level inverter type neutral point clamped (NPC) inverter, with the use of integral backstepping (IBS) technique which was also compared with the proportional integral (PI) controller. The simulation results in MATLAB/Simulink are presented illustrating the performances and the strong effectiveness of the three-level NPC inverter controlled by the proposed technique (IBS).

Energy exchange optimization for a building-to-building grid: A non-cooperative game-theory method

The growing demand for green energy solutions has led to an increase in innovative distributed energy generation (DEG) architectures, such as buildings with photovoltaic (PV) generation and energy storage systems (ESS). In these distributed grids, buildings with renewable energy generation capabilities can exchange surplus energy to optimize overall system efficiency, increase their profit, and reduce reliance on the main grid. This paper proposes a game-theory method to optimize energy exchange within a building-to-building (B2B) grid. The proposed method formulates the energy exchange problem as a non-cooperative game, where each building owner aims to maximize their welfare by strategically determining their energy trading decisions, including pricing. The game-theory approach considers energy supply and demand, ESS capacity, and cost considerations, including ESS degradation cost. The proposed approach is demonstrated through simulations, showing significant improvements in cost savings compared to building-to-grid (B2Grid) alternatives. Three cases are compared: (1) B2B grid without considering ESS, (2) B2B grid with ESS without considering the ESS degradation cost (ideal case), and (3) B2B grid with ESS considering the ESS degradation cost. The results show the potential of the proposed game-theory in optimizing energy exchange within the B2B grid with daily and operational hours saving of 2.12−6.37 % and 4.57−165.93 %, respectively.

Effects of Adding the Antiparallel Diodes in a Model of Solar Photovoltaic Cell: Theory and Pspice Simulations

Background: The photovoltaic (PV) array consisting of modules is considered as the fundamental electrical power conversion unit of the PV generator system. The PV array has nonlinear characteristics with the current-voltage (I-V) characteristic in the exponential form introduced by diodes. In this context, the key objective of the research carried out in the present study is to propose a novel PV cell, with anti-parallel diodes introduced in the circuit model in place of the single diode. Objective: The objective of this research is to compare the effects, magnitudes of current and power generated by the PV cell, with antiparallel diodes to those with single diodes. Methods: The model circuit containing two antiparallel diodes is first designed, and then analyzed through the circuit Equationuations found using the Kirchhoff’s laws. The I-V characteristics are modified in the hyperbolic sine form, and the model Equationuation is solved analytically and numerically using the MATLAB software. The analytical and numerical investigations are then confirmed by Pspice simulation, which is made possible with the introduction of additional bloc necessary to make the output signal voltage tunable in the opened circuit. Results: The I-V characteristic is plotted showing that when the voltage is lower than a particular, the model containing the antiparallel diode has the increasing of current, while that with single diode has constant current. Additionally the current generated by the PV cell oscillates increasingly when the supplied signal voltage is negative. We also found that the maximum power increases with the temperature. The expression of the cutoff signal voltage leading to zero current was also found and plotted, the increasing function of the number Ns of series diodes. As application, the PV circuit designed is used in the charging of the battery of electrical vehicle. Conclusion: The PV cell with antiparallel diodes generates currents for large band of signal voltage, and the power generated increases with temperature.

A Bi-level optimization dispatch for hybrid shipboard microgrid considering electricity-gas-heat coupling

With the increasingly severe problem of air pollution and energy crisis, new energy power generation technology in ship has quickly become the focus of attention. Compared with traditional ships, hybrid shipboard microgrid systems can achieve pollution-free, renewable and high use value. However, the integration of electricity-gas-heat in hybrid energy shipboard microgrid system also poses challenges to current optimization methods. Therefore, this paper develops a bi-level optimization dispatch model for hybrid shipboard microgrid system based on multi-objective particle swarm optimization algorithm. Taking the diesel generators, photovoltaic generation system, energy storage system (ESS) and thermal energy storage equipment into account, a hybrid shipboard microgrid system model considering electricity-gas-heat coupling is constructed. Based on this, a bi-level optimization dispatch model is established to reduce total cost, GHG (GHG) emissions and lifespan loss of ESS. The upper-level model achieves the optimization dispatch of power generation equipment and loads; a lower-level optimization model with the goal of reducing the lifespan loss of ESS is constructed. The improved multi-objective and single-objective particle swarm optimization algorithms are introduced to find the optimal dispatch solutions for bi-level optimization dispatch model. Finally, simulation results show that the proposed optimization method can not only reduce the cost and GHG emissions by 8.7% and 10.9%, but also improve the cycle life of ESS by 9.2%.

Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm Optimization.

This study introduces an improved quantum-behavior particle swarm optimization (IQPSO), tailored for the task of maximum power point tracking (MPPT) within photovoltaic generation systems (PGSs). The power stage of the MPPT system comprises a series of buck-boost converters, while the control stage contains a microprocessor executing the biomimetic algorithm. Leveraging the series buck-boost converter, the MPPT system achieves optimal operation at the maximum power point under both ideal ambient conditions and partial shade conditions (PSCs). The proposed IQPSO addresses the premature convergence issue of QPSO, enhancing tracking accuracy and reducing tracking time by estimating the maximum power point and adjusting the probability distribution. Employing exponential decay, IQPSO facilitates a reduction in tracking time, consequently enhancing convergence efficiency and search capability. Through single-peak experiments, multi-peak experiments, irradiance-changing experiments, and full-day experiments, it is demonstrated that the tracking accuracy and tracking time of IQPSO outperform existing biomimetic algorithms, such as the QPSO, firefly algorithm (FA), and PSO.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

Modeling and Simulation of Power Cell Capacity Configuration and Large-Scale Echelon Utilization in Photovoltaic Energy Storage Power Station

The muscle element is an part and parcel of the photovoltaic stored energy substation. In an effort to upgrade the availability of the muscle element and the availability of the muscle generation of the photovoltaic stored energy substation, the muscle element volume arrange and large-scale cascade utilization pattern of the photovoltaic stored energy substation are studied. On the basis of the structure of the photovoltaic stored energy substation, the capabilities exchange policy of the photovoltaic stored capabilities substation is analyzed. Based on the correlation between the muscle element volume retention proportion and the number of circulates, the Monte Carlo simulation method is selected to calculate the muscle element cascade utilization volume. On the basis of the volume count outcomes, the muscle element volume arrange based on large-scale cascade utilization is designed. Combined with the muscle element volume arrange based on the enterprise environmental protection index and the muscle element volume arrange based on capabilities loss, the multi-objective function of volume arrange is constructed, and the goal function is solved using the glowworm algorithm. The experimental outcomes show that the cascade utilization volume of muscle batteries becomes stable with the extension of service years, and some batteries in the stored capabilities system can use degraded muscle batteries, thereby improving the availability of muscle batteries; The monthly average availability of photovoltaic muscle generation system increased from 55% to more than 85%.

Decentralized utilization of distributed energy storage resources for simultaneous frequency regulation in a microgrid

A microgrid has emerged for the transformation from centralized into localized electricity generation, which increases the opportunity for renewable-based distributed generators (DGs) applications such as photovoltaic (PV) and wind as well as small-scale conventional generation units. PV and wind integration provides environmental, technical, and economic benefits. However, the low inertia of the PV system and decoupling due to the presence of the power electronics converter in the wind generation system led to a significant declination in the microgrid's inertia, which causes an excessive frequency nadir and a high rate of change of frequency (ROCOF) in the presence of disturbances. The microgrid's inertia can be enhanced by storing or releasing energy within the microgrid. The wind generation system has intrinsic kinetic energy stored in the rotating parts, while the PV system's sorted energy is improved by introducing an external supercapacitor on the DC link. The aforementioned energy storage elements provide limited energy resources, which should be employed in a proper manner. This article proposes a control scheme to boost the cooperation of wind and PV generation systems for the enhancement of inertia and frequency support. Therefore, the proposed scheme prevents excessive stress on individual generators and improves the utilization of available energy resources. For example, at load increase by 0.5 MW, with frequency support using PV only or wind only, the frequency deviation is enhanced by 44.4 % and 29.5 %, respectively. While for both PV and wind support the frequency simultaneously, the frequency deviation is enhanced by 53.3 %. In addition, with the cooperation of PV and wind, the required energy from PV and wind is reduced by 29.5 % and 13.7 % respectively and the rate of change of frequency is enhanced by 61.9 %. The energy share among PV and wind generation systems is investigated and verified by simulations; tuning formulas are also developed to attain predefined energy support with equal energy sharing among different resources. For instance, with a non-equal energy share strategy and load increases by 0.5 MW, the wind delivers almost 1.6 times the energy delivered from the PV. Hence, more stress occurs on the wind generation compared to PV. While, using equal energy share strategy, the required energy from wind is about 1.03 times the energy delivered from the PV. The proposed control scheme is applied to an AC microgrid that comprises a thermal generation plant, a PV generation system, and a doubly-feed induction generator-based wind generation system. To verify the effectiveness of the proposed scheme, different simulation cases considering load change, wind speed variation, irradiance variations, and weak grid integration are implemented. When compared to individual frequency support, the proposed scheme provides a superior response.

Power Optimization of Multi-Type Mixed-Connection Photovoltaic Generation System for Recreational Vehicles

The utilization of photovoltaic (PV) generation to charge storage batteries in recreational vehicles (RVs) is becoming increasingly prevalent. However, the performance of PV generation systems is hindered by the mismatch caused by different module types and varying environmental conditions. This discrepancy negatively impacts the output performance of PV modules, resulting in reduced system efficiency. To address this issue, this paper explored the series–parallel output characteristics of different types of PV modules and summarized the methods for configuring PV modules in a mixed-structure PV generation system for RV energy supplementation. Building upon this foundation, a novel equalization scheme based on extremum-seeking control (ESC) is introduced. The scheme initially employs a forward–flyback converter (FFC) to equalize the current among series-connected PV modules, followed by matching the voltage between parallel-connected PV module strings. Finally, the ESC is utilized to optimize the real-time output power of the PV generation system, thereby enhancing overall system efficiency. Through simulation experiments conducted on a PV generation system with four types of mixed-connection PV modules employing the PLECS simulation platform, simulated results demonstrate the effectiveness of the proposed scheme in improving PV module output performance and maximum power tracking efficiency. The simulation data reveal that the proposed scheme achieves an impressive average tracking efficiency of 99.15%, surpassing the efficiency of the global maximum power point tracking scheme based on an enhanced perturb and observe algorithm.

CVaR Quantitative Uncertainty-Based Optimal Dispatch for Flexible Traction Power Supply System

The flexible traction power supply system (FTPSS) integrating a photovoltaic (PV) generation system, an energy storage system (ESS), and a railway power conditioner (RPC) is a promising solution for future railways. However, the volatilities and random uncertainties of PV and traction load negatively impact the ability of FTPSS to operate safely and economically. A dispatch method combining conditional value-at-risk (CVaR) and probabilistic scenarios is proposed for FTPSS to quantify uncertainties into the risk cost. To reconcile economy and robustness, a flexible optimization dispatch model that considers the economic expense and risk cost is built. Finally, this optimization problem is formulated as a mixed integer quadratically constrained programming (MIQCP) model and solved by the GUROBI solver. Based on a genuine instance in China, the method’s validity has been demonstrated. The validity of the method is verified based on a real case in China. The essence of the proposed method is to mitigate the risks related to PV and traction load changes to some extent during the day-ahead dispatch stage by taking into account potential fluctuations in the real-time operating stage. The proposed method can help dispatch decision-makers create effective dispatch strategies and is conducive to FTPSS energy savings and risk avoidance.

Bağımsız Bir Hibrit Güç Sisteminin Tasarımı ve Akıllı MPPT Algoritmaları ile Optimizasyon Kontrolü

In this paper, a stand-alone hybrid power system has been proposed using both: photovoltaic generator (PVG) and permanent magnets synchronous generator (PMSG), integrated with a lithium-type battery connected to the DC bus. The improvement of performance and reliability of the overall system are guaranteed. For this aim, the efficiency of the PVG system can be enhanced firstly by using two MPPT's techniques, such as the Perturb and Observe (P&O) method and the Fuzzy Logic Control (FLC) approach. The Genetic Algorithms (GA’s), using the principles of evolution, natural selection, and genetic mutation, are then introduced to address difficulties in the adjustment of the FLC gains. A lithium-ion battery model is presented to ensure stability and energy storage. Furthermore, the model of the wind energy conversion system (WECS) based on PMSG is presented. Hence, the control by the gradient method, allowing designing an MPPT based on the delivered power and mechanical speed of the generator. Both sources are connected to the grid via a two-level voltage source inverter controlled by Space Vector Modulation (SVM) technique. Finally, simulation results obtained using MATLAB / SIMULINK software proved the effectiveness of proposed control strategies with high performance which is manifested clearly when the suggested controllers employed and shed light on the performance of the stand-alone hybrid system.

Comparison of economic viability between photovoltaic generation on rural properties and soybean production

The main objective of this study was to conduct a comparative analysis of the feasibility of implementing rural photovoltaic farms in relation to soybean cultivation in different states of Brazil. The study aims to assist rural producers in making decisions regarding the occupation of rural areas with photovoltaic generation systems, considering that these areas could otherwise be used for crop production. Given the agricultural vocation and the need for food supply, these areas would be unavailable for crop production during the lifespan of the photovoltaic system. Therefore, data on soybean production costs and costs for implementing photovoltaic systems were collected. This allowed for the calculation of the profitability of each activity by subtracting the production costs from the revenues obtained through the sale of inputs. Based on these data, cash flows were elaborated, and the Net Present Value (NPV) was calculated for each activity. Photovoltaic energy generation resulted in the highest NPV in all states, with Paraná standing out at NPV R$ 4,340,184.85. Regarding soybean production, the state of Goiás achieved the best result for the activity, with NPV R$ 75,166.01. The analyzed activities demonstrated financial viability as they resulted in NPVs greater than zero.

Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement

In recent years, the government has attached great importance to promoting innovation in new energy technologies, particularly in the area of solar power generation. Photovoltaic (PV) power generation has become an essential factor in the promotion of renewable energy due to its inherent safety, environmental sustainability and abundant solar energy resources. The efficiency and stability of PV generation systems are closely related to the accurate prediction of real-time global horizontal irradiance (GHI). However, some existing research methods have made some progress in hourly resolution, but the multi-step prediction accuracy and model generalization ability in high time resolution are still unsatisfactory. Therefore, this paper proposes a novel minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement scheme. In this scheme, the advantages of both LSTM and InformerStack models are combined to further enhance the global and local features, which can better capture the short-term time series patterns and long-term dependencies and improve the accuracy of the time series. The experimental validation results show that the performance of this prediction scheme is improved by 11.2%, and 26.0% for nRMSE and R2, respectively, and the 24-step prediction can be improved up to 26.1% vs. the persistent model. Meanwhile, the prediction scheme of this paper is also expected to provide a reference in future multi-step time series accurate prediction with higher resolution.

Efficient DC Voltage Balancing of Cascaded Photovoltaic System Based on Predictive Modulation Strategy

This paper presents a predictive modulation strategy for dc voltage balancing of cascaded H-bridge-based photovoltaic generation systems (CHB-PV). It is intended for improving the efficiency of CHB-PV by reducing the number of switching events. The strategy enables the charge/ voltage estimation and the design of a real-time discharge-order exchange, ensuring discrete dc voltage balancing. The conventional loss-reduced level-shifted-pulse-width-modulation (LS-PWM) is embedded and used to predict the discharge values and the target dc voltage value. The real-time discharge-order exchange is used to make the individual voltages converge to the target value at intervals of maximum 1/4 fundamental cycle. The voltage spread range introduced by the discrete voltage balancing is also considered and well suppressed by a simple adaptive method. Balancing of the dc voltages is finally achieved in a more discrete way, which makes reduced switching events compared with only using the loss-reduced LS-PWM. Experiment results based on a 2.1 kW/311 V setup are presented to demonstrate the effectiveness of the proposed technologies.

Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model

Abstract This article discusses the significance and obstacles of short-term power prediction in photovoltaic systems and introduces a hybrid model for photovoltaic short-term power prediction technology based on variational mode decomposition (VMD), convolutional neural network (CNN), improved particle swarm optimization (IPSO) and least squares support vector machine (LSSVM). In the initial stage, the photovoltaic generation signal is decomposed into multiple Intrinsic mode functions (IMFs) using VMD to enhance the extraction of signal time–frequency characteristics. Subsequently, CNN is utilized for feature learning and extraction of each IMF, modeling the nonlinear and non-stationary features. Following this, the IPSO-LSSVM optimization algorithm is employed to establish and optimize multiple LSSVM models, predicting power fluctuations at different time scales. Finally, the predictions from each model are synthesized to obtain the final photovoltaic short-term power forecast. Through validation with actual photovoltaic generation data, this hybrid model demonstrates high accuracy and reliability in short-term power prediction, showing an average relative error and root mean square error reduction of 15.23 and 53.60%, respectively, compared to a certain comparative model. This proposed method based on VMD-CNN-IPSO-LSSVM hybrid model for photovoltaic power prediction holds promising prospects and practical value in the operation and scheduling of photovoltaic generation systems.

Estimation and monitoring of grid connected PV system generation

This paper presents a new method to identify the operating performance parameters of a photovoltaic (PV) generator and a grid connected inverter. This method has been applied to the behavior modeling and simulation of the grid connected PV system at the Centre de Developpement des Energies Renouvelables (CDER) in Algeria. The PV module (PVM) parameters has been identified in static with outdoor measurements of I-V curves in order to model the PV array generation. Using the Linear Reoriented Coordinates Method (LRCM), the prediction of maximum power point (MPP) of the PV array has been performed without using complicate approximations or Taylor series. In order to estimate the ac output power, a PV inverter performance model based on empirical relationship was used. However, an accurate identification of the inverter performance parameters is performed using an optimization algorithm, named Levenberg-Marquardt (LM) algorithm, and a moutput current and voltage. The simulation results were achieved through MATLAB/Simulink environment, for the setting and the testing of the models. The results show a good agreement between the measured and estimated results based on the identified parameters. The integration of the proposed method and the presented model present a simple way to estimate the expected system performance with a high degree of accuracy. This solution allows a development of automatic supervision, energy generation prediction and fault detection procedure.

Sensorless MPPT considering Power Loss Factors for DPP Structure in Photovoltaic Generation System

Sensorless MPPT considering Power Loss Factors for DPP Structure in Photovoltaic Generation System

Remote monitoring and analysis of productivity indicators of photovoltaic energy generation systems

Remote monitoring and analysis of productivity indicators of photovoltaic energy generation systems

Energy Management of a Battery Storage System Considering Variable Load and Controllable Renewable Generation (Solar Study Case) to Keep the Grid’s Frequency Stability

This paper presents the research of analytical functions related to the energy generation of photovoltaic systems and the residential and commercial load demanded by end users, concerning a statistical function. To test this model, a linear cost function was considered to compute its overestimation and underestimation due to its maximum and minimum production limits, where energy consumption is obtained at each instant of time, within the established production ranges, through the analytical equations that determine solar energy generation and demand load. The result obtained by applying the Uncertainty Quantification (UCF) theory in these equations, in the same way through the Monte Carlo (MC) simulation for comparison, is the expected value of energy for a hypothetical storage system E (Cu, Co). Better accuracy of results via this model can be improved upon when the energy generation parameters are structured as analytical functions each instant of time associated with probability distributions based on the uncertainty costs of controllable sources, instead of statistical functions.