• Journal of the Korean Solar Energy Society
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• v.29 no.4
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• pp.1-6
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• 2009

This paper estimates numerically cells the electrical characteristics of the PV module with environmental changes such as shunt resistance, series resistance, temperature, irradiance. Series resistance $R_s$ including diode characteristic resistance $r_d$ is derived from the p-n junction diode model. I-V characteristics of this model with series resistance $R_s$ are simulated on Matlab. Finally, theoretical I-V characteristics are compared with those of solar simulator. Those results agreed well within the manufacturer's maximum error range 3%

• Journal of the Korean Solar Energy Society
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• v.37 no.6
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• pp.69-77
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• 2017

The amount of solar irradiation obtained by a photovoltaic (PV) solar panel is the major factor determining the power generated by a PV system, and the array tilt angle is critical for maximizing panel radiation acquisition. There are three types of PV systems based on the manner of setting the array tilt angle: fixed, semi-fixed, and tracking systems. A fixed system cannot respond to seasonal solar altitude angle changes, and therefore cannot absorb the maximum available solar radiation. The tracking system continually adjusts the tilt angle to absorb the maximum available radiation, but requires additional cost for equipment, installation, operation, and maintenance. The semi-fixed system is only adjusted periodically (usually seasonally) to obtain more energy than a fixed system at an overall cost that is less than a tracking system. To maximize semi-fixed system efficiency, determining the optimal tilt angle adjustment schedule are required. In this research, we conducted a simulation to derive an optimal operation schedule for a semi-fixed system in Seoul, Korea (latitude $37.5^{\circ}$). We implemented a solar radiation acquisition model and PV genereation model on MATLAB. The optimal operation schedule was derived by changing the number of tilt angle adjustments throughout a year using a Dynamic Algorithm. The results show that adjusting the tilt angle 4 times a year was the most appropriate. and then, generation amount of PV system increased 2.80% compared with the fixed system. This corresponds to 99% compared to daily adjustment model. This increase would be quite valid as the PV system installation area increased.

• Journal of the Korean Solar Energy Society
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• v.37 no.3
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• pp.47-59
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• 2017

As the global warming threats to humanity, renewable energy is considered the key solution to overcome the climate change. In this circumstance, distributed PV systems are being expanded significantly its market share in the renewable energy industry. The performance of inverter is the most important component at PV system and numerous researches are focusing on it. In order to improve the inverter, PV simulator is an essential device to experiment under various load and conditions. This paper proposes the PV Power-Hardware-In-Loop simulator (PHILS) with real-time processing converted electrical and mathematical models to improve computation speed. Single-diode PV model is used in MATLAB/SIMULINK for the PV PHILS to boosting computation speed and dynamic model accuracy. In addition, control algorithms for sub-components such as DC amplifier, measurement device and several interface functions are implemented in the model. The proposed PV PHILS is validated by means of experiments with commercial PV module parameters.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.18-19
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• 2006

The recent interest in distributed generation (DG) due to the opening of the electricity market and the need for alternatives to conventional fossil fuel-based electricity generation has created renewed interest in grid-connected photovoltaic(PV) systems. Many studies are being performed at the power system level to examine the impacts of grid-connected PV systems and several models for PV arrays have been proposed in the literature. However, the complexity of these models and difficulties of implementing them in various software programs can be deterrents to their use. This paper proposes a robust and flexible PV device model suitable for dynamic and transient studies where the PV array's non-linear DC characteristics are important. The model's implementation in software is straightforward and it can even be constructed using standard software library components, as demonstrated using PSCAD/EMTDC.

• Journal of Environmental Science International
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• v.27 no.3
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• pp.227-231
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• 2018

We studied the warming effect induced by Photovoltaic(PV) power plants in rural areas during summer daytime using a simple analytical urban meteorological model. This analysis was based on observed meteorological elements and the capacity of the PV power plant was 10 MWp. The major axis length of the PV power plant was assumed to be 1km. Data of the necessary meteorological elements were obtained from a special meteorological observation campaign established for a over a PV power plant. We assumed that the wind flowed along the major axis of the PV power plant(1 km). As a result, the air temperature on the downwind side of the PV power plant was estimated to invrease by about $0.47^{\circ}C$.

• Journal of the Korean Solar Energy Society
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• v.28 no.4
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• pp.56-61
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• 2008

The modeling of PV (Photovoltaic) module is useful to perform detailed analysis of PV system performance for changing meteorological conditions, verify actual rated power of PV system sizing and determine the optimal design of PV system and components. This paper indicates a modeling approach of PV module performance in terms of meteorological conditions and identifies validity of this modeling method by comparing measured with simulated value of various PV modules using simulation model.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1489-1500
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• 2018

Partial shading analysis of large-scale photovoltaic (PV) arrays has recently become a theoretically and numerically challenging issue, and it is necessary for PV system designers. The main contributions of this study are the following: 1) A PSIM-based macro-model was employed because it is remarkably fast, has high precision, and has no convergence issues. 2) Three types of equivalent macro-models were developed for the transformation of a small PV sub-array with uniform irradiance to a new macro-model. 3) On the basis of the proposed new macro-model, a tearing method was established, which can divide a large-scale PV array into several small sub-arrays to significantly improve the efficiency improvement of a simulation. 4) Three platforms, namely, PSIM, PSpice, and MATLAB, were applied to evaluate the proposed tearing method. The proposed models and methods were validated, and the value of this research was highlighted using an actual large-scale PV array with 2420 PV modules. Numerical simulation demonstrated that the tearing method can remarkably improve the simulation efficiency by approximately thousands of times, and the method obtained a precision of nearly 6.5%. It can provide a useful tool to design the optimal configuration of a PV array with a given shading pattern as much as possible.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.461-470
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• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.164-168
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• 2015

Due to the uncertainty of weather, it is difficult to construct an accurate forecasting model for daily PV power generation. It is very important work to know PV power in next day to manage power system. In this paper, correlation analysis between weather and power generation was carried out and daily PV power forecasting models based on Extreme Learning Machine(ELM) was presented. Performance of district ELM model was compared with single ELM model. The proposed method has been tested using actual data set measured in 2014.