• 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.10 no.5
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• pp.1930-1939
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• 2015

Wind power planning aims to locate and size wind farms optimally. Traditionally, wind power planners tend to choose the wind farms with the richest wind resources to maximize the energy benefit. However, the capacity benefit of wind power should also be considered in large-scale clustered wind farm planning because the correlation among the wind farms exerts an obvious influence on the capacity benefit brought about by the combined wind power. This paper proposes a planning model considering both the energy and the capacity benefit of the wind farms. The capacity benefit is evaluated by the wind power capacity credit. The Ordinal Optimization (OO) Theory, capable of handling problems with non-analytical forms, is applied to address the model. To verify the feasibility and advantages of the model, the proposed model is compared with a widely used genetic algorithm (GA) via a modified IEEE RTS-79 system and the real world case of Ningxia, China. The results show that the diversity of the wind farm enhances the capacity credit of wind power.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1773-1779
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• 2015

Shore-to-sea visible light communication using color clustered multiple-input and multiple-output (MIMO) is presented. The proposed maritime visible light communication (MVLC) offers a low-cost, high-speed wireless link for shore-to-sea maritime communications. Each color cluster is comprised of 50 red, green and blue (RGB) light emitting diodes (LEDs) and is modulated using on-off-keying (OOK). Selection combining is performed at the receiver, producing diversity effect within that color cluster. In this paper, we employ sea states (wave height, wind speed, etc.) data from both Pierson-Moskowitz and JONSWAP spectrum models under atmospheric turbulence conditions. Based on the simulation model, the maritime link quality is analysed in terms of coverage distance and bit error rate performance. The results show that the proposed system provides an efficient MVLC, while satisfying International Association of Lighthouse Authorities (IALA) requirements for maritime buoyage system and also offering sufficient illumination from high power LEDs.

• Journal of Environmental Policy
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• v.10 no.1
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• pp.93-127
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• 2011

Investment on technology to obtain green energy is prevailing all over the world. The technology development project is more likely to involve multiple sub-projects, each of which is related to develop elementary technology when the project is larger and nation-wide. However, the methodologies identifying optimal combination of elementary technologies among the candidates have been very few. Hence, the purpose of this paper is to propose a novel methodology which provides an optimal combination of green energy technologies. To do so, to-be developed technologies are clustered with multiple categories. Among the technologies, based on Delphi method, the experts select a representative technology, which is indispensible to the green energy system and has the highest connectivity with other elementary technologies. Then the methodology selects an elementary technology from each technology category based on two metrics: Relatedness with representative technology and project risk. To show the feasibility of the proposed methodology, we applied the methodology to an actual windmill development project.