http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Modelling and Simulating the Spatio-Temporal Correlations of Clustered Wind Power Using Copula
Zhang, Ning,Kang, Chongqing,Xu, Qianyao,Jiang, Changming,Chen, Zhixu,Liu, Jun The Korean Institute of Electrical Engineers 2013 Journal of Electrical Engineering & Technology Vol.8 No.6
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.
Modelling and Simulating the Spatio-Temporal Correlations of Clustered Wind Power Using Copula
Ning Zhang,Chongqing Kang,Qianyao Xu,Changming Jiang,Zhixu Chen,Jun Liu 대한전기학회 2013 Journal of Electrical Engineering & Technology Vol.8 No.6
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 spatiotemporal correlations using the theory of copula. The sampling approach captures the complex spatiotemporal 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.
Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit
Wang, Yi,Zhang, Ning,Kang, Chongqing,Xu, Qianyao,Li, Hui,Xiao, Jinyu,Wang, Zhidong,Shi, Rui,Wang, Shuai The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.5
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.
Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit
Yi Wang,Ning Zhang,Chongqing Kang,Qianyao Xu,Hui Li,Jinyu Xiao,Zhidong Wang,Rui Shi,Shuai Wang 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.5
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.