Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system production simulation. Existing power system probabilistic production simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic production simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.

[1]  B. Klockl Multivariate Time Series Models Applied to the Assessment of Energy Storage in Power Systems , 2008, Proceedings of the 10th International Conference on Probablistic Methods Applied to Power Systems.

[2]  Shijie Cheng,et al.  Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling , 2013, IEEE Transactions on Sustainable Energy.

[3]  G. Papaefthymiou,et al.  Multivariate time series models for studies on stochastic generators in power systems , 2010 .

[4]  Liisa Haarla,et al.  Assessment of large scale wind power generation with new generation locations without measurement data , 2015 .

[5]  N.S. Rau,et al.  Expected Energy Production Costs by the Method of Moments , 1980, IEEE Transactions on Power Apparatus and Systems.

[6]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[7]  Xiang Niande IMPLEMENT OF PROBABILISTIC PRODUCTION COST SIMULATION ALGORITHM BASED ON SEQUENCE OPERATION THEORY , 2002 .

[8]  Yao Liang-zhong,et al.  Power System Probabilistic Production Simulation Including Wind Farms , 2009 .

[9]  C. Singh,et al.  A Wind Farm Reliability Model Considering Both Wind Variability and Turbine Forced Outages , 2017, IEEE Transactions on Sustainable Energy.

[10]  Gregory Levitin,et al.  The Universal Generating Function in Reliability Analysis and Optimization , 2005 .

[11]  B. O. Anyaka Power System Simulation Model Based on Probability Analysis , 2013 .

[12]  Mattia Marinelli,et al.  Wind and Photovoltaic Large-Scale Regional Models for Hourly Production Evaluation , 2015, IEEE Transactions on Sustainable Energy.

[13]  R. Billinton,et al.  The IEEE Reliability Test System???Extensions to and Evaluation of the Generating System , 1986, IEEE Power Engineering Review.

[14]  Gregory Levitin A Universal Generating Function in the Analysis of Multi-state Systems , 2008 .

[15]  Gregory Levitin,et al.  A universal generating function approach for the analysis of multi-state systems with dependent elements , 2004, Reliab. Eng. Syst. Saf..

[16]  R. R. Booth,et al.  Power System Simulation Model Based on Probability Analysis , 1972 .

[17]  Pathak Anjumoni,et al.  Basic tools and techniques in Biotechnology , 2015 .

[18]  Kang Chong-qing Dependent probabilistic sequence operations for wind power output analyses , 2012 .

[19]  Gengyin LI,et al.  Universal generating function based probabilistic production simulation for wind power integrated power systems , 2017 .

[20]  Jiang Wu,et al.  Modeling Dynamic Spatial Correlations of Geographically Distributed Wind Farms and Constructing Ellipsoidal Uncertainty Sets for Optimization-Based Generation Scheduling , 2015, IEEE Transactions on Sustainable Energy.

[21]  Matti Lehtonen,et al.  Wind speed modeling using a vector autoregressive process with a time-dependent intercept term , 2016 .

[22]  Wei Li,et al.  The Hierarchical Weighted Multi-State $k$-out-of- $n$ System Model and Its Application for Infrastructure Management , 2010, IEEE Transactions on Reliability.

[23]  Wei-Chang Yeh The Extension of Universal Generating Function Method to Search for All One-to-Many $d$ -Minimal Paths of Acyclic Multi-State-Arc Flow-Conservation Networks , 2008, IEEE Transactions on Reliability.

[24]  Bai Li-chao SEQUENCE-BASED ANALYSIS OF PROBABILISTIC PRODUCTION COST SIMULATION , 2002 .

[25]  X. Wang,et al.  Equivalent energy function approach to power system probabilistic modeling , 1988 .

[26]  Gregory Levitin,et al.  Universal Generating Function Models , 2003 .

[27]  Wang Songyan A Wind Speed Modeling Method for Multiple Wind Farms Considering Correlation and Statistical Characteristics , 2013 .

[28]  Michael C. Caramanis,et al.  Probabilistic production costing , 1983 .

[29]  Li Dong Power System Probabilistic Production Simulation With Wind Generation Based on Available Capacity Distribution , 2012 .