Photovoltaic System Output Research Articles

Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles

Cloud transients cause rapid fluctuations in the output of photovoltaic (PV) systems, which can significantly affect the voltage levels in a low-voltage (LV) grid with high penetration of PV systems. These voltage fluctuations may lead to violation of the existing power quality standards. This study estimates the impact of rapid PV output fluctuations on the power quality in an existing LV grid by performing load flow analyses for scenarios in the years 2017, 2030 and 2050 using PV data with 20-second resolution. In this study, we propose a system for the mitigation of PV output fluctuations by altering the charging processes of electric vehicles (EVs) and we assess the effectiveness of the proposed system. Results indicate that PV output fluctuations have minor impact on the voltage levels in the year 2030, but PV output fluctuations induce considerable voltage fluctuations in the year 2050. The magnitude of the voltage fluctuations is dependent on the location in the grid, the installed PV capacity and the grid configuration. These voltage fluctuations can induce visible and annoying light flicker for a significant part of the day in the year 2050. Implementing the proposed system shows that EV technology can contribute in reducing the amount of visible and annoying light flicker considerably, however at the expense of increased charging costs for EV owners.

Impact of harmonic limits on PV penetration levels in unbalanced distribution networks considering load and irradiance uncertainty

Photovoltaic (PV) system is one of the most important technologies among the renewable energy resources. With the increasing penetration of PV systems in distribution networks, harmonic distortions also rise due to the inevitable effects of PV inverters. Therefore, determination of the optimal PV penetration level is a vital issue in terms of preventing power quality problems. In this paper, probabilistic and deterministic approaches are proposed to determine the optimal penetration levels of PV systems in unbalanced distorted distribution networks by taking into account the uncertainty of load profile and the intermittent characteristic of PV system output power due to changes in solar irradiance. The Interior Point (IP) method is applied to solve the optimization problem in conjunction with the Monte Carlo Simulation (MCS) and K – means clustering, respectively. The methodology is based on the dependence of the harmonic spectrum of PV injected current on solar irradiance. The harmonic power quality parameters are calculated by using loop frame of reference based three phase harmonic power flow method in the unbalanced distribution network, where the nonlinear loads and PV systems are interfaced. The allowable PV penetration levels are determined based on power quality parameters comprising of total harmonic voltage and individual harmonic voltage distortions, and RMS bus voltage limits.

Genetic algorithm to improve power output of photovoltaic system under partial shaded condition

The Partial Shaded Condition (PSC) is a process of non-optimal power capture in photovoltaic (PV) system; it will happen when one or all the PV solar cells get shaded by external factors. This phenomenon makes a sudden change in cell irradiance and lead to non-optimal power capture and reduces the generated power in PV systems. Cell under PSC generates one global peak (GP) and many local peaks (LPs), but the tracking of GP under (PSCs) effectively are still a big challenge to the researchers. In this paper, Fuzzy Logic Control (FLC) and optimized FLC algorithms were developed to track the PV power during the PSCs. The configuration of both algorithms are designed, simulated and evaluated using MATLAB/Simulink and their performance was compared with other literature techniques to study their capability to track the MPPT under PSCs. Proposed GA-FLC achieved 0.7 S as tracking time to find and track the Global Peak of Maximum power Point (GMPP) under PSCs and the usage of buck/boost converter in this technique achieved 98.5% of tracking accuracy. GA-FLC shows good dynamic performance in terms of tracking accuracy and complexity under PSCs compared to other techniques.

Performance evaluation of roof top solar photovoltaic systems in Tamilnadu

This manuscript reports the monitored performance results of roof top solar photovoltaic (PV) power plants in different parts of Tamilnadu, India. In this work, PV plants of capacities 84 kWp and 18 kWp located at Tirunelveli and Ranipet respectively in Tamilnadu are considered. During an eight month period, of September 2014 to April 2015, these plants had generated 43.99 MWh and 15.55 MWh units of electricity respectively. The average electricity production per day for the considered period of these plants is 181.74 kWh and 62.81 kWh respectively. The performance ratio (PR) of these plants PV1 and PV2 is found to be 0.52 and 0.86 respectively. The characteristics of poly crystalline PV modules and the performance of employed photovoltaic inverters are also analyzed.It is observed that external conditions like climate and bad weather significantly reduces the PV system output, whereas it reduces marginally due to inverter failure as observed from the values of energy yield and performance ratio of these plants. Online monitoring of PV plant with DC/AC line and phase voltages and current waveforms observed for the given day are also presented.

A new IR and sprinkler based embedded controller directed robotic arm for automatic cleaning of solar panel

Purpose The purpose of this paper is to design and develop an IR and sprinkler based embedded controller operated robotic arm for automatic dust removal system to mitigate the dust effect on the solar panel surface, since dust accumulation normally affected by real weather conditions is one of the serious concern for the deterioration of photovoltaic (PV) system output. Design/methodology/approach The system is a wet cleaning device which provides a cheap silicon rubber-based wiping operation controlled by the pulse width modulation-operated motors of robotic arm. The IEEE 1149.1-compliant mixed signal-embedded platform of C8051F226DK is involved to command the complete system. Findings A prototype of 30 WP system is capable of producing an inspiring average value of 11.26 per cent in energy increase, 13.63 per cent in PV module efficiency and 85.20 per cent in performance ratio of the system after 73 days of cleaning in summer season. In addition, a total of 1,617.93 W power; 1,0516.55 Wh energy; and 350.55 KWh/KWP final yield was found during the entire cleaning period. Originality/value A novel technique of the implementation of IR sensor and sprinkler in dust mitigation is proposed in this paper. The IR sensor is used as a versatile object which can manage the robotic arm setting and control the automatic switching between cleaning and charging, as well as identify the thermal condition of solar panel for overheating.

The effect of snowfall and icing on the sustainability of the power output of agrid-connected photovoltaic system in Konya, Turkey

When the module surface is covered with various factors such as snow and icing which prevent the solar irradiance from reaching the photovoltaic cells, the power production of the system and its performance decrease. The purpose of this study is to determine the energy production losses of a grid-connected photovoltaic plant due to snowfall and icing. The effect of snowfall and icing has been examined on a photovoltaic system consisting of a hybrid inverter with two separate maximum power point tracking inputs and 36 monocrystalline modules, which are mounted on the supporting system horizontally in the south direction and at a constant tilt angle of 30$^{\circ}$. The plant area is located in a priority and snowy region (Konya,Turkey) where large-scale photovoltaic system installations are carried out. In order to evaluate the effect of snowfall, the minute resolution data of the hybrid inverter which provides connection to the grid is used. The change over time of the power generated by the two arrays of the plant was examined comparatively. For comparison, one of the arrays was continuously cleared. The recorded data was used to determine the expected energy output of the array covered with snow. Besides, the solar irradiance and ambient temperature data obtained from the meteorological station were used to accurately identify and evaluate the effects of snowfall with digital images recorded in the site area. The results showed that surface clearing of modules had a significant positive effect on the power output of the system. In the array entirely covered with snow, the daily energy loss exceeds 93 %. In months of heavy snowfall, the monthly energy loss is 18 % depending on time of being covered with snow of the modules. When the production data of 2017 and 2018 is evaluated, it is seen that the total energy loss of the plant varies between 1 % and 2 %.

Multiconcept Methods to Enhance Photovoltaic System Efficiency

The main goal of this paper is to review the most important methods previously developed to enhance the efficiency and increase the lifetime of photovoltaic panels. The methods to increase the solar radiation incident on photovoltaic panels, as well as the cooling and the maximum power point tracker methods, are concisely presented in this paper. The pros and cons analysis reveals that the methods to enhance the power generated by the photovoltaic panels are strongly dependent on geographical location, climatic conditions, and the materials used. This review paper is also of interest for engineers who attempt to identify the most adequate solutions to maximize the energy output of photovoltaic systems for each location.

Active power control to mitigate voltage and frequency deviations for the smart grid using smart PV inverters

Large-scale deployments of variable renewable energy sources, such as photovoltaic (PV) systems, are increasing around the world. PV systems are a potential source of clean energy but they experience power intermittency and lack controllability. However, smart PV inverters provide grid-friendly functionalities that control the power output of PV systems. Power intermittency of PV systems causes major problems such as voltage fluctuations and frequency deviations in an electric power grid. Together with varying loads and other renewable distributed generations, the grid frequency and voltage become difficult to manage. A smart PV inverter allows for active power curtailment, volt-watt, and frequency-watt control systems to mitigate the voltage and frequency deviations for a smart power grid. Therefore, smart PV inverters can reduce the installation of voltage and frequency control devices in an electric power grid, which in turn can reduce installation and maintenance costs. Real-time analyses of active power curtailment, volt-watt control, frequency-watt control using smart PV inverters and their effects on voltage and frequency are presented in this paper. Power curtailment operations were executed in low and high PV penetration areas. From the experimental analyses, distributed voltage deviations can be mitigated using active power curtailment and volt-watt control systems, and the over-frequency of an electric power grid can be reduced using frequency-watt control method. Controller performances of smart PV inverters are also evaluated in this paper. The experimental analyses were executed in the Maui Advanced Solar Initiative Project.

Fine-Tuning of Multijunction Solar Cells: An In-Depth Evaluation

Multijunction (MJ) solar cells are currently seen as the most promising technology toward achieving incomparable solar to electricity conversion efficiency, largely surpassing the best conventional single-junction solar cells. It was recently shown that futuristic cell architectures encompassing increasing number of subcells will likely show increased sensitivity to the spectral distribution of sunlight, giving rise to dramatic variations in the energy output of MJ-based Photovoltaic (PV) systems from one location to the other. Here, we investigate the extent to which MJ cells fine-tuned to the spectral distribution of any particular site are likely to outperform conventionally designed solar cells (tailored to AM1.5 reference solar spectrum). We first tackle the question of the extent to which fine-tuning may improve the performance of MJ cells showing deviations in the values of the main atmospheric parameters, relative to the reference solar spectrum. We then evaluate the potential of fine-tuning in improving the energy output of MJ cell-based PV systems for several locations selected for being representative of the broad diversity of atmospheric and climatic conditions. We demonstrate that the improvement in the energy output one can expect from fine-tuned PV cells strongly depends on how the mean annual spectrum deviates from the reference AM1.5 solar spectrum, with values ranging from $\approx$ 0 to more than 30%, depending on the location and the cell architecture considered. Implications for future generations of MJ solar cells are finally discussed.

A novel MPPT technique to increase accuracy in photovoltaic systems under variable atmospheric conditions using Fuzzy Gain scheduling

ABSTRACT Photovoltaic (PV) system has gradually become a research focus in the field of renewable energy power generation, and the output efficiency of PV system is the major concern of researchers. There are obvious non-linear characteristics in the output of PV system, and it will be greatly affected by the external environment. For achieving the maximum output power, PV system must operate under the guidance of maximum power point tracking (MPPT) methods. The tracking time and accuracy of these methods need to be improved. Therefore, this study contributes to increasing the output efficiency of PV system by improving the tracking time and accuracy with using Fuzzy Gain Scheduling of Proportional-Integral-Derivative (FGS-PID) regulator in the grid-connected mode. The suggested adaptive FGS-PID scheme is implemented using two-level control system architecture, combining the benefits of fuzzy logic as well as conventional PID regulator. Ziegler–Nichols tuning method is employed to select the initial values of PID gains. In the simulations of PV system under the varying irradiance and temperature, the tracking speed and tracking accuracy of FGS-PID (scaling factors) were higher than those of ANN, ANFIS, Fuzzy, INC, P&O, PID, and FGS-PID methods. In summary, FGS-PID (scaling factors) has better performance in tracking time and accuracy than other comparison algorithms. It can improve the output efficiency of PV system in practical application.

Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile

Santiago de Chile frequently suffers from atmospheric pollution that contributes to the decrease of solar irradiance on the surface, leading to losses in the energy output of photovoltaic systems. In this study, a simple model is used to estimate the effect of aerosols on the solar irradiance over the city throughout the year, using as input AERONET sunphotometer data and other in-situ measurements. The results show reductions of 3.5% and 14.1% for global horizontal and direct normal irradiance respectively and an increase of 35.4% for diffuse horizontal irradiance between the actual condition in Santiago and a hypothetical atmosphere free of aerosols. These effects translate approximately to an annual difference in the energy output of −7.2% and −8.7% for monocrystalline and amorphous silicon PV technologies respectively, and an annual difference of −16.4% for a CPV technology, showing that aerosols can have a significant effect on the photovoltaic energy production.

Solar photovoltaic power output forecasting using machine learning technique

Photovoltaic (PV) systems are used around the world to generate solar power. Solar power sources are irregular in nature due to the output power of PV systems being intermittent and depending greatly on environmental factors. These factors include, but are not limited to, irradiance, humidity, PV surface temperature, speed of the wind. Due to uncertainties in the photovoltaic generation, it is critical to precisely envisage the solar power generation. Solar power forecasting is necessary for supply and demand planning in an electric grid. This prediction is highly complex and challenging as solar power generation is weather-dependent and uncontrollable. This paper describes the effects of various environmental parameters on the PV system output. Prediction models based on Artificial Neural Networks (ANN) and regression models are evaluated for selective factors. The selection is done by using the correlation-based feature selection (CSF) and ReliefF techniques. The ANN model outperforms all other techniques that were discussed.

Gauss polynomials based modelling of grid-connected PV system output power in high-altitude area

Complicated ordering management and improper providing electricity are a problem encountered during the PV system generate electricity. Accurately predict the output power is an effective approach for minimizing these problems. In this paper, electric power exchange relationship of each link is analysed, according to the topology of the photovoltaic (PV) power stations. We propose the under prediction model of output power, based on the theory model of output PV current in the high-altitude environment. The composite error could be calculated by the difference between the under prediction power and the measure power. Comprehensive error expression is obtained by using the high-order polynomial fitting method. An improved prediction model, which adapts to the high-altitude environment, is established, while compensate the original model with the composite error. Compared with the actual output power of the PV power station, the results show that the curves of the two are highly consistent, the correlation between the two is more than 0.8 and the relative error between the predicted and measured power is less than that of the under-predicted power model. The results show that the improved predictive power model has a good tracking performance.

Grid Connected PV Modules in Distributed Generation System during Irradiance and Temperature Variation

Now a days, photovoltaic (PV) system finds extreme interest as alternative source of energy and they are integrated with grids forming distributed generation (DG) systems. But, the output of PV system is dependent of irradiance and temperature. As these parameters changes location to location in a very unpredictable way due to weather and shading and also for some other reasons, it effects the overall performance of DG system. Here such three PV systems of each 255 KW was considered to be located in different locations. These PV systems feed power to the grid and acts like a DG system. But what if the insolation and temperature change in random behavior in these different locations and what will be their impact on the total system. The model was designed in PSCAD and the possible scenarios are considered and also the effect of it is analyzed. The comparison study is done with normal condition and varying condition. The study reveals the effect on PV, inverter and transformer output. The stability of overall system is affected and also harmonics are injected and also the differences in phase and amplitude are observed which degrades the system performance.

A recursive ensemble model for forecasting the power output of photovoltaic systems

Solar power provides a clean and renewable energy source. However, unlike many conventional sources, Photovoltaic (PV) power generation is of high volatility and uncertainty in short terms, which creates great challenges to forecasting and balancing electricity generation with demand. This study investigates the effects of PV solar power variability and proposes a data-driven ensemble modeling technique to improve the prediction accuracy of PV power generation. Three different types of models are integrated within a recursive arithmetic average model on their stand-alone predictions. The proposed methodology is later demonstrated to be of higher accuracy by comparing its prediction performance with each stand-alone forecasting model. Several different training and testing samples have been analyzed with the proposed model. The results show that the ensemble model performs better than the other stand-alone forecasting techniques in general.

Direct Connection of Supercapacitor–Battery Hybrid Storage System to the Grid-Tied Photovoltaic System

Penetration rate of grid-connected photovoltaic (PV) generation to the existing utility grid is rapidly increasing over the years. Since the power generated from PV systems fluctuates according to the weather condition, e.g., cloud passing, this can significantly disturb the stability of a weak utility grid. The integration of energy storage devices and its ramp-rate control technique are required to reduce the impact of PV systems output fluctuations and augment the stability of the utility grid. In this paper, ramp-rate control is applied to the direct connection of energy storage devices in PV generation system configuration. The direct connection of supercapacitors string and battery combination scheme is proposed to reduce the number of power converters so that the efficiency of the system is increased. In this work, the PV system output is controlled by directly controlling the energy storage system (ESS) to limit the changing rate of the PV output to a desired ramp-rate value, hence reducing the battery charge/discharge cycles and extending the expected lifetime of the ESS. The performance of the proposed direct connection scheme of the ESS and its ramp-rate control strategy is verified using a 1-kW PV system prototype.

Short-Term Photovoltaic Generation Forecasting Based on LVQ-PSO-BP Neural Network and Markov Chain Method

With the rapid development of solar photovoltaic generation, the effective prediction of photovoltaic is of great significance to mitigate its impact on power system. According to the analysis of main factors which affect power output of photovoltaic system, a short-term power forecasting model based on back propagation(BP) neutral network and LVQ-PSO-BP neural network and Markov chain method was established. The weather is clustered and distinguished by using learning vector quantization(LVQ) and the particle swarm optimization(PSO) is used to optimize BP neural network weights and thresholds, improving forecasting network training speed. Finally, daily predictive value is corrected by Markov chain method to improve short-term photovoltaic generation forecasting precision. The simulation results indicate that the proposed method can accelerate the speed of searching optimums, improving the classification accuracy of weather types and the precision of the photovoltaic generation output effectively.

Research on local voltage control strategy based on high‐penetration distributed PV systems

With the high-penetration distributed photovoltaic (PV) systems accessed to a distribution network, the peak active power output of PV systems is always greater than the total local load demands, which might lead to the issues of overvoltage and reverse power flow in a distribution system. In this paper, based on the reactive power output capability of distributed PV inverters, a distributed PV reactive power local control strategy is proposed. Three types of voltage reactive power local control modes of PV inverter are analysed, respectively; they are voltage-var control mode (Q(V) mode), adjustable power factor mode (PF(P) mode) and active power curtailment mode (APC mode). The applicable conditions of different control modes are analysed. A simulation example based on IEEE 33-node system is studied, where the method of determining the optimal control mode under different operating scenarios of distribution system with PV is given, and the validity of the three local control modes is verified.

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

Mathematical Modeling and Simulation of the Photovoltaic System's Output Power depends on Temperature and Irradiance

Fotovoltaik (PV) sistemlerin cikis gucu, gunes isinimi ve sicaklik ile ilgilidir. Bu makalede, PV sisteminin cikis gucunun sicaklik ve gunes isinimina bagli degisimi incelenmistir. Ayrica, bu calismada PV panellerinin yaninda PV dizininin calisma prensibi ozetlemistir. Off-grid PV sistemi Matlab/simulasyon programi kullanilarak modellenmis ve bu calismada detayli calismasi gerceklestirilmistir. Ayrica, farkli sicaklik ve isinim degerleri icin PV sisteminin cikis gucu analiz edilmistir. Sonuc olarak, PV sisteminin cikis gucunun analitik ifadesi, Sosyal Bilimler Icin Istatistik Programi (SPSS) kullanilarak bulunmustur.