scholarly journals Solar Resource Potentials and Annual Capacity Factor Based on the Korean Solar Irradiance Datasets Derived by the Satellite Imagery from 1996 to 2019

2021 ◽  
Vol 13 (17) ◽  
pp. 3422
Author(s):  
Chang Ki Kim ◽  
Hyun-Goo Kim ◽  
Yong-Heack Kang ◽  
Chang-Yeol Yun ◽  
Boyoung Kim ◽  
...  

The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite–Korea Institute of Energy Research (UASIBS–KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, from 1996 to 2019. During the investigation period, the monthly mean of daily total irradiance was in a good agreement with the in situ measurements at 18 ground stations; the mean absolute error is also normalized to 9.4%. It is observed that the irradiance estimates in the datasets have been gradually increasing at a rate of 0.019 kWh m−2 d−1 per year. The monthly variation in solar irradiance indicates that the meteorological conditions in the spring season dominate the annual solar insolation. In addition, the local distribution of solar irradiance is primarily affected by the geographical environment; higher solar insolation is observed in the southern part of Korea, but lower solar insolation is observed in the mountainous range in Korea. The annual capacity factor is the secondary output from the Korean solar irradiance datasets. The reliability of the estimate of this factor is proven by the high correlation coefficient of 0.912. Thus, in accordance with the results from the spatial distribution of solar irradiance, the southern part of Korea is an appropriate region for establishing solar power plants exhibiting a higher annual capacity factor than the other regions.

2019 ◽  
Vol 9 (6) ◽  
pp. 1131 ◽  
Author(s):  
Luis Valentín ◽  
Manuel Peña-Cruz ◽  
Daniela Moctezuma ◽  
Cesar Peña-Martínez ◽  
Carlos Pineda-Arellano ◽  
...  

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.


Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4402
Author(s):  
Julián Urrego-Ortiz ◽  
J. Alejandro Martínez ◽  
Paola A. Arias ◽  
Álvaro Jaramillo-Duque

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%–~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.


Author(s):  
Rodrigo Alonso-Suarez ◽  
Franco Marchesoni ◽  
Liber Dovat ◽  
Agustin Laguarda

Author(s):  
Huifang Deng ◽  
Robert F. Boehm

The southwestern US is an ideal location for solar power plants due to its abundant solar resource, while there is a difficulty in implementing wet cooling systems due to the shortage of water in this region. Dry cooling could be an excellent solution for this, if it could achieve a high efficiency and low cost as wet cooling. Some dry cooling systems are currently in operation, and investigations of their performance have been reported in the literature. This paper looks into the limits to the power production implicit in dry cooling, assuming that improvements might be made to the system components. Use of higher performance heat transfer surfaces is one such possible improvement. We have developed a model of a fairly typical, but simplified, solar trough plant, and simulated thermodynamic performance of this with the software Gatecycle. We have examined the power generation and cycle efficiency of the plant for the Las Vegas vicinity with conventional wet cooling and conventional dry cooling cases considered separately using this software. TMY2 data are used for this location for this purpose. Similarly, the same studies are carried out for “ideal” cooling systems as a comparison. We assumed that in the ideal dry cooling system, the condensing temperature is the ambient dry bulb temperature, and in the ideal wet cooling system, it is the ambient wet bulb temperature. It turned out that the ideal dry cooling system would significantly outperform the conventional wet cooling system, indicating the possibility of the dry cooling system being able to achieve increased performance levels with component improvements.


2020 ◽  
Vol 12 (7) ◽  
pp. 1212 ◽  
Author(s):  
Román Mondragón ◽  
Joaquín Alonso-Montesinos ◽  
David Riveros-Rosas ◽  
Mauro Valdés ◽  
Héctor Estévez ◽  
...  

Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant’s operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types.


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