• 조명전기설비학회논문지
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• 제29권7호
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• pp.80-86
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• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• KEPCO Journal on Electric Power and Energy
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• 제2권4호
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• pp.627-630
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• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• KEPCO Journal on Electric Power and Energy
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• 제1권1호
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• pp.163-168
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• 2015

In order to integrate high wind generation resources into power grid, it is an essential to predict power outputs of wind generating resources. As wind farm outputs depend on natural wind resources that vary over space and time, spatial modeling based on geographic information such as latitude and longitude is needed to estimate power outputs of wind generation resources. In this paper, we introduce the basic concept of spatial modeling and present the spatial prediction model based on Kriging techniques. The empirical data, wind farm power output in Texas, is considered to verify the proposed prediction model.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1615-1625
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• 2013

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio-temporal correlations using the theory of copula. The sampling approach captures the complex spatio-temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.87-88
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• 2015

본 논문에서는 제주풍력단지에 대한 측정 데이터(measured wind power outputs)를 기반으로 Fast Fourier Transform(FFT)을 실시하고, 이를 이용하여 각 풍력발전단지의 주파수 특성에 대해 분석해보고자 한다.

• 한국태양에너지학회 논문집
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• 제33권4호
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• pp.39-45
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• 2013

This paper presents the estimation of minimum BESS capacity for regulating the output of wind farms considering power grid operating condition in Jeju Island. To analyze the characteristics of wind farm outputs with a BESS, the real data of wind farms, Sung-San, Sam-dal and Hang-Won wind farm, located in the eastern part of Jeju island is considered. The wind farms are connected to Sung-san substation to transfer the electric power to Jeju power grid. Consequently, at PCC (Point of Common Coupling), it can see a huge wind farm connected to the substation and thus it can be expected that the smoothing effect is affected by not only the different wind speeds for each area but also the different mechanical inertia of wind turbines. In this paper, two kinds of simulation have been carried out. One is to analyze the real data of wind farm outputs during a winter season, and the other is to connect a virtual BESS to eliminate the unintended generating power changes by the uncontrolled wind farm outputs as shown in the former data. In the conclusion, two kinds of simulation results show that BESS installed in the substation is more efficient than each wind farms with BESS, respectively.

• KEPCO Journal on Electric Power and Energy
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• 제2권3호
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• pp.377-382
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• 2016

In 2013, a total capacity of 591.3 MW of installed wind power generation was achieved in Korea, with a total of 1,139 MWh of wind energy generated that year. More wind power plants will be installed in the coming years, and it is important to develop methods to reduce the output variability of these resources so as to provide stable power to the power grid of Korea. In this regard, this paper proposes the use of energy storage system (ESS) as a means to stabilize the output variability of wind power plants. Presented in this paper is a method that uses Discrete Fourier Transform (DFT) to determine the ESS capacity needed to provide a stable power output for ancillary services such as frequency regulation, economic dispatch, and emergency reserves. In the first step of the proposed method, four regions (namely, Samdal, Yeongdeok, Yeongyang, and Gangwon) in Korea that had the most wind power generation capacity were selected for analysis. In the second step, the individual and aggregated wind power outputs of the selected regions in 2013 were obtained This information was then used in the third step, where DFT analysis of the power outputs was used to drive the magnitudes of the output variation. And finally, the ESS capacity requirements needed to provide different ancillary services were determined based on the magnitudes of the output variation.

• 한국농공학회논문집
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• 제50권6호
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• pp.27-35
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• 2008

This study aims to estimate the optimal probability distribution of daily electricity generation by wind power, in order to contribute in rural green-village planning. Wind power generation is now being recognized as one of the most popular sources for renewable resources over the country. Although it is also being adapted to rural area for may reasons, it is important to estimate the magnitudes of power outputs with reliable statistical methodologies while applying historical daily wind data, for correct feasibility analysis. In this study, one of the well-known statistical methodology is employed to define the appropriate statistical distributions for monthly power outputs for specific rural areas. The results imply that the assumption of normal distributions for many cases may lead to incorrect decision-making and therefore lead to the unreliable feasibility analysis. Subjective methodology for testing goodness of fit for normal distributions on all the cases in this study, provides possibilities to consider the other various types of statistical distributions for more precise feasibility analysis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.165-166
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• 2015

본 논문에서는 제주풍력단지에 대한 측정 데이터(measured wind power outputs)를 기반으로 제주 실측 풍력데이터를 이용하여 공간적인 상관관계 분석을 수행하고, 상관관계 감쇠거리(CDD; Correlation Decay Distance)를 적용하여 새로운 풍력발전단지에 대한 공간모델 적용 시 기존 풍력발전단지의 포함여부(set 또는 subset)를 결정하는 기준(Threshold)으로 활용하고자 한다. 이를 통해 풍력발전출력 예측에 공간모델을 적용하고, 정확도를 향상시키는 방안을 연구하고자한다.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2006년도 춘계학술대회
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• pp.339-342
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• 2006

The power outputs of renewable energy such as wind turbines and solar energy powers depend on natural sources. Accordingly, the power outputs of renewable energy is different from capacity rate at the time of peak load. Because of this gap, long term electricity power plan can have over-estimated reserve margin. So, this paper suggests the chronological approach to calculate the reliability assessment of renewable energy.