This study evaluated the applicability of long-term datasets among third-generation reanalysis data CFSR, ERA-Interim, MERRA, and MERRA-2 to determine which dataset is more suitable when performing wind resource assessment for the ‘Southwest 2.5 GW Offshore Wind Power Project’, which is currently underway strategically in South Korea. The evaluation was performed by comparing the reanalyses with offshore, onshore, and island meteorological tower measurements obtained in and around the southwest offshore. In the pre-processing of the measurement data, the shading sectors due to a meteorological tower were excluded from all observation data, and the measurement heights at the offshore meteorological towers were corrected considering the sea level change caused by tidal difference. To reflect the orographic forcing by terrain features, the reanalysis data were transformed by using WindSim, a flow model based on computational fluid dynamics and statistical-dynamic downscaling. The comparison of the reanalyses with the measurement data showed the fitness in the following order in terms of coefficient of determination: MERRA-2 > CFSR = MERRA > ERA-Interim. Since the measurement data at the onshore meteorological towers strongly revealed a local wind system such as sea-land breeze, it is judged to be inappropriate for use as supplementary data for offshore wind resource assessment.

Although data-driven methods including deep neural network (DNN) were introduced, there was not enough assessment about spatial characteristics when using limited ground observation as reference. This work aimed to interpret the feasibility of several machine learning approaches to assess the spatial distribution of solar radiation on Earth based on the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) geostationary satellite. Four data-driven models were selected (artificial neural network (ANN), random forest (RF), support vector regression (SVR), and DNN), to compare their accuracy and spatial estimating performance. Moreover, we used a physical model to probe the ability of data-driven methods, implementing hold-out and k-fold cross-validation approaches based on pyranometers located in South Korea. The results of analysis showed the RF had the highest accuracy in predicting performance, although the difference between RF and the second-best technique (DNN) was insignificant. Temporal variations in root mean square error (RMSE) were dependent on the number of data samples, while the physical model showed relatively less sensitivity. Nevertheless, DNN and RF showed less variability in RMSE than the others. To examine spatial estimation performance, we mapped solar radiation over South Korea for each model. The data-driven models accurately simulated the observed cloud pattern spatially, whereas the physical model failed to do because of cloud mask errors. These exhibited different spatial retrieval performances according to their own training approaches. Overall analysis showed that deeper layers of networks approaches (RF and DNN), could best simulate the challenging spatial pattern of thin clouds when using satellite multispectral data.

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R² was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

Although the size of the wind turbine has become larger to improve the economic feasibility of wind power generation, whether increases in rotor diameter and hub height always lead to the optimization of energy cost remains to be seen. This paper proposes an algorithm that calculates the optimized hub height to minimize the cost of energy (COE) using the regional wind profile database. The optimized hub height was determined by identifying the minimum COE after calculating the annual energy production (AEP) and cost increase, according to hub height increase, by using the wind profiles of the wind resource map in South Korea and drawing the COE curve. The optimized hub altitude was calculated as 75~80 m in the inland plain but as 60~70 m in onshore or mountain sites, where the wind profile at the lower layer from the hub height showed relatively strong wind speed than that in inland plain. The AEP loss due to the decrease in hub height was compensated for by increasing the rotor diameter, in which case COE also decreased in the entire region of South Korea. The proposed algorithm of identifying the optimized hub height is expected to serve as a good guideline when determining the hub height according to different geographic regions.

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.