Optimal scheduling of wind farm with storage and forecasting based on improved genetic algorithms

The optimal Operation Scheduling for power output of a wind farm with storage units and forecasting system has been studied in this paper. Genetic algorithm(GA) is used to achieve the optimal scheduling of wind farm output power which maximize revenue and minimize costs over a required period. However, the Traditional Genetic Algorithm(TGA) has the characteristics of premature phenomenon and slow convergence; it cannot get the desirable result on such a multi-step scheduling scenario. An Improved Genetic Algorithm(IGA) is presented in this paper by modifying the fitness function, choice strategy and crossover strategy. Simulation shows that IGA has the advantages of fast convergence speed and strong capability of global search over traditional genetic algorithm. Finally, a method for optimal scheduling of wind farm with storage and forecasting based on improved genetic algorithms is presented and the experiments validate its feasibility and effectiveness.

[1]  P. Dokopoulos,et al.  A fuzzy expert system for the forecasting of wind speed and power generation in wind farms , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[2]  K. J. Tseng,et al.  Determination of Short-Term Power Dispatch Schedule for a Wind Farm Incorporated With Dual-Battery Energy Storage Scheme , 2012, IEEE Transactions on Sustainable Energy.

[3]  Xu Bo.  Genetic Algorithm and Application in Data Mining , 2010 .

[4]  Jun Cheng Liu,et al.  An Optimal Operation Scheduling for Wind Farm with Storage and Forecasting , 2012 .

[5]  Hu Wen-hua.  Application of Power Electronics Technology in Wind Power , 2006 .

[6]  Hugh Rudnick,et al.  Short-term hydrothermal generation scheduling model using a genetic algorithm , 2003 .

[7]  T. J. Overbye,et al.  Optimal storage scheduling for minimizing schedule deviations considering variability of generated wind power , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.