Calculation of reactive power compensation capacity in wind farms based on chance constrained programming

Although the problem of reactive power planning has been extensively studied, uncertain factors are rarely taken into account in traditional reactive power compensation models. For the wind farm output randomness, the paper applies chance constrained programming theories to establish the model of multi-objective wind farm reactive power compensation, which makes voltage level and power loss as the objective functions. The method of wind farm approached to the modeling and power flow calculation is discussed, and an improved differential evolution algorithm combined with Monte Carlo stochastic simulation technology is proposed to solve the model. Finally, the effectiveness of the proposed model and method is validated by IEEE 24-bus system with wind farms.

[1]  J. Cidras,et al.  Wind speed simulation in wind farms for steady-state security assessment of electrical power systems , 1999 .

[2]  C. Shunmuga Velayutham,et al.  Experimental Study on Recent Advances in Differential Evolution Algorithm , 2011, Int. J. Appl. Evol. Comput..

[3]  Josef Tvrdík Adaptation in differential evolution: A numerical comparison , 2009, Appl. Soft Comput..

[4]  M. P. Biswal,et al.  Genetic based fuzzy goal programming for multiobjective chance constrained programming problems with continuous random variables , 2006, Int. J. Comput. Math..

[5]  Zhang Zi-jian Multi-objective reactive power optimization in power system with wind farm , 2010 .

[6]  Cai Heng A Method of Selecting DC Links Termination at AC Locations Considering Stability and Economy at Once , 2011 .

[7]  Y. Kantar,et al.  Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function , 2008 .

[8]  A. Celik Weibull representative compressed wind speed data for energy and performance calculations of wind energy systems , 2003 .

[9]  Liubin Yan An Synthetic Approach to Determine Weights Based on the Identity of Subjective and Objective Weighted Attribute Value , 2007 .

[10]  Zhang Yue-qiang Multi-objective Reactive Power Optimization in Wind Power Integrated System Considering the Risk of Voltage Collapse , 2012 .

[11]  M. E. Hamedani Golshan,et al.  Distributed generation, reactive sources and network-configuration planning for power and energy-loss reduction , 2006 .

[12]  Cai Wei,et al.  A Method for Test of Gapless Harmonic Analysis in Power Quality Analyzer , 2011 .

[13]  Chen Chong Reactive power compensation optimization for grid-connected wind farm , 2008 .

[14]  Gu Li-chen Optimization of Reactive Power Planning for Power System Containing Wind Farms , 2010 .

[15]  Cw W. Yu,et al.  An investigation of reactive power planning based on chance constrained programming , 2007 .