ATC calculation including wind: A probabilistic study and a comparison of MCS and LHS

This paper presents a probabilistic assessment of Available Transfer Capability (ATC) in the presence of intermittent wind resources in the power system. An optimal power flow based approach is considered to calculate ATC wherein wind resources are considered as an equivalent active power generation placed in the transmission system, the optimal location being decided by the Voltage sensitivity indices. A probabilistic load model is considered using two different sampling techniques, viz. Monte Carlo Sampling (MCS) and Latin Hypercube Sampling (LHS) and a comparison of results for ATC calculation using the two sampling methods is put forth.

[1]  J.H. Zhang,et al.  Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition , 2009, IEEE Transactions on Power Systems.

[2]  D. Martin,et al.  A comparison of Gumbel and Weibull statistical models to estimate wind speed for wind power generation , 2014, 2014 Australasian Universities Power Engineering Conference (AUPEC).

[3]  Yajing Gao,et al.  Available transfer capability calculation with large offshore wind farms connected by VSC-HVDC , 2012, IEEE PES Innovative Smart Grid Technologies.

[4]  Luo Gang,et al.  Probabilistic assessment of available transfer capability considering spatial correlation in wind power integrated system , 2013 .

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

[6]  Yixin Ni,et al.  Available transfer capability calculation with static security constraints , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[7]  A. Khairuddin,et al.  ATC determination incorporating wind generation , 2013, 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO).

[8]  Chanan Singh,et al.  Probabilistic Assessment of TTC in Power Systems Including Wind Power Generation , 2012, IEEE Systems Journal.

[9]  M.G. Da Silva,et al.  Probabilistic Assessment of Available Transfer Capability Based on Monte Carlo Method With Sequential Simulation , 2007, IEEE Transactions on Power Systems.

[10]  J. Cidras,et al.  Modeling of wind farms in the load flow analysis , 2000 .

[11]  Ying Xiao,et al.  Available transfer capability (ATC) evaluation by stochastic programming , 2000 .

[12]  A. Berizzi,et al.  A Monte Carlo Approach for TTC Evaluation , 2007, IEEE Transactions on Power Systems.

[13]  R. Kanimozhi,et al.  A Novel Line Stability Index for Voltage Stability Analysis and Contingency Ranking in Power System Using Fuzzy Based Load Flow , 2013 .

[14]  Yixin Ni,et al.  Calculation of total transfer capability incorporating the effect of reactive power , 2003 .

[15]  Wenyuan Li,et al.  Assessing transfer capability requirement for wind power generation using a combined deterministic and probabilistic approach , 2009, 2009 IEEE Power & Energy Society General Meeting.

[16]  N. A. M. Ismail,et al.  A comparison of voltage stability indices , 2014, 2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014).

[17]  Mehdi Ehsan,et al.  A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks , 2012, IEEE Systems Journal.

[18]  Zuwei Yu,et al.  Fractional weibull wind speed modeling for wind power production estimation , 2009, 2009 IEEE Power & Energy Society General Meeting.

[19]  Dongyuan Shi,et al.  Probabilistic load flow computation with polynomial normal transformation and Latin hypercube sampling , 2013 .

[20]  Shye-Chorng Kuo,et al.  Comparative analysis on power curve models of wind turbine generator in estimating capacity factor , 2014 .

[21]  Chen Wang,et al.  Optimal Power Flow Solution Incorporating Wind Power , 2012, IEEE Systems Journal.

[22]  Y. H. Song,et al.  Available transfer capability enhancement using FACTS devices , 2003 .