Optimization of a combined heat and power system with wind turbines

Abstract In this paper, an optimization model is developed for a system consisting of both combined heat and power units and wind turbines. The analysis is conducted in the scope of economic dispatch. In the present model, the probability (rather than the average) of stochastic wind power is considered as a constraint. This approach avoids the probabilistic infeasibility appearing in conventional approaches. It is shown that the effects of wind speed on the generated power and heat can be readily assessed in terms of reliability.

[1]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[2]  A. Leon-Garcia.  Probability, statistics, and random processes for electrical engineering , 2008 .

[3]  Y. Y. Hong,et al.  Genetic Algorithms Based Economic Dispatch for Cogeneration Units Considering Multiplant Multibuyer Wheeling , 2002, IEEE Power Engineering Review.

[4]  Zong Woo Geem,et al.  Handling non-convex heat-power feasible region in combined heat and power economic dispatch , 2012 .

[5]  C. S. Chang,et al.  Stochastic multiobjective generation dispatch of combined heat and power systems , 1998 .

[6]  Malabika Basu.  Artificial immune system for combined heat and power economic dispatch , 2012 .

[7]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[8]  M. Fesanghary,et al.  Combined heat and power economic dispatch by harmony search algorithm , 2007 .

[9]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[10]  S. Roy.  Market Constrained Optimal Planning for Wind Energy Conversion Systems over Multiple Installation Sites , 2002, IEEE Power Engineering Review.

[11]  M.-R. Haghifam,et al.  Reliability and availability modelling of combined heat and power (CHP) systems , 2011 .

[12]  Amir Hossein Gandomi,et al.  Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect , 2012, Appl. Soft Comput..

[13]  Amir Hossein Gandomi,et al.  Short-term load forecasting of power systems by gene expression programming , 2010, Neural Computing and Applications.

[14]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[15]  Antonio Moreno-Munoz,et al.  Improvement of power quality using distributed generation , 2010 .

[16]  Wilsun Xu,et al.  Minimum Emission Dispatch Constrained by Stochastic Wind Power Availability and Cost , 2010, IEEE Transactions on Power Systems.

[17]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[18]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[19]  J. Lobry,et al.  Impact of the Wind Geographical Correlation Level for Reliability Studies , 2007, IEEE Transactions on Power Systems.

[20]  S. Chowdhury,et al.  Microgrids and Active Distribution Networks , 2009 .

[21]  Hadi Saadat,et al.  Power System Analysis , 1998 .

[22]  Lingfeng Wang,et al.  Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization , 2006, 2006 IEEE Power Engineering Society General Meeting.

[23]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[24]  Amir Hossein Gandomi,et al.  Combined heat and power economic dispatch by mesh adaptive direct search algorithm , 2011, Expert Syst. Appl..