• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.357-360
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• 2007

Any investment analysis has to deal with the uncertainty that arises over the course of operating the invested project. When it comes to an wind power, such analysis gets even more complicated, as the wind resource or the current is inherently unstable and unpredictable. Different from predecessors in the field of analyzing wind power economics, this paper proposes a stochastic methodology of analyzing the economic efficiency of an investment in wind power to explicitly address those uncertainties or risks. A probability distribution is assigned to each variable to generate a probability distribution of the economic value of an investment through a Monte-Carlo simulation

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.42-46
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• 2005

In spite of fast growing of prediction codes, there is still not negligible uncertainty in their results. This uncertainty affects on the turbine structural design and power production prediction. With the growing size of wind turbine, reducing this uncertainty is becoming one of critical issues for high performance and efficient wind turbine design. In this respect, there are international efforts to evaluate and tune prediction codes of wind turbine. As the reference data for this purpose, field test data is not appropriate because of its uncontrollable wind characteristics and its inherent uncertainty. Wind tunnel can provide controllable wind. For this reason, NREL has done the full scale test of the 10m turbine at NASA-Ames. With this reference data, a blind comparison has been done with participation of 18 organizations with 19 modeling tools. The results were not favorable. In Europe, a similar project is going on. Nine organizations from five countries are participating in the MEXICO project to do full scale wind tunnel tests and calculation with prediction codes. In this study. these two projects were reviewed in respect of wind tunnel test and its contribution. As a conclusion, it is suggested that scale model wind tunnel tests can be a complementary tool to calculation codes which were evaluated worse than expected.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.607-617
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• 2019

Global warming and climate change are increasing the intensity of typhoons and hurricanes and thus increasing the risk effects of typhoon and hurricane hazards on nuclear power plants (NPPs). To reflect these changes, a new NPP should be designed to endure design-basis hurricane wind speeds corresponding to an exceedance frequency of $10^{-7}/yr$. However, the short typhoon and hurricane observation records and uncertainties included in the inputs for an estimation cause significant uncertainty in the estimated wind speeds for return periods of longer than 100,000 years. A logic-tree framework is introduced to handle the epistemic uncertainty when estimating wind speeds. Three key parameters of a typhoon wind field model, i.e., the central pressure difference, pressure profile parameter, and radius to maximum wind, are used for constructing logic tree branches. The wind speeds of the simulated typhoons and the probable maximum wind speeds are estimated using Monte Carlo simulations, and wind hazard curves are derived as a function of the annual exceedance probability or return period. A logic tree decreases the epistemic uncertainty included in the wind intensity models and provides reasonably acceptable wind speeds.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.149-157
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• 2005

The power performance monitoring system for a small class of wind turbine is established. The wind turbine power performance characteristics are determined by measured power curve and the estimated annual energy production (AEP). The measured power curve is determined by collecting simultaneous measurements of wind speed and power output at the test site under varying wind conditions. In order to determine the power performance characteristics of the wind turbine accurately, the data are of sufficient quantity and quality shall be corrected according to defined criteria. In this study, the 6kW wind turbine made by Germany Inventus GmbH is examined.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.158-164
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• 2018

Wind power forecast is critical for efficient power system operation. However, wind power has high forecasting errors due to uncertainty caused by the climate change. These forecasting errors can have an adverse impact on the power system operation. In order to mitigate the issues caused by the wind power forecasting error, wind power curtailment and energy storage system (ESS) can be introduced in the power system. These methods can affect the economics of wind power resources. Therefore, it is necessary to evaluate the economics of the methods for mitigating the wind power forecasting error. This paper attempts to analyze the economics of wind power curtailment and ESS operation for mitigating wind power forecasting error. Numerical simulation results are presented to show the economic impact of wind power curtailment and ESS operation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.457-466
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• 2013

Wind energy is rapidly becoming significant generating technologies in electricity markets. As probabilistic nature of wind energy creates many uncertainties in the short-term scheduling, additional actions for reliable market operation should be taken. This paper presents a novel approach to evaluate ramping capability requirement for changes in imbalance energy between day-ahead market and real-time market due to uncertainty of wind generation as well as system load. Dynamic ramp rate model has been applied for realistic solution in unit commitment problem, which is implemented in day-ahead market. Probabilistic optimal power flow has been used to verify ramping capability determined by the proposed method is reasonable in economic and reliable aspects. This approach was tested on six-bus system and IEEE 118-bus system with a wind farm. The results show that the proposed approach provides ramping capability information to meet both forecasted variability and desired confidence level of anticipated uncertainty.

• New & Renewable Energy
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• v.10 no.2
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• pp.19-28
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• 2014

Wind power generation of 5 MW wind turbine was predicted by using wind measurement data from HeMOSU-1 which is at south west coast of Korea. Time histories of turbulent wind was generated from 10-min mean wind speed and then they were used as input to Bladed to estimated electric power. Those estimated powers are used in both polynominal regression and neural network training. They were compared with each other for daily production and yearly production. Effect of mean wind speed and turbulence intensity were quantitatively analyzed and discussed. This technique further can be used to assess lifetime power of wind turbine.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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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.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.