Optimal operation planning of wind farm using wind power forecasted data

In order to solve problems of global warming and depletion of energy resource, renewable energy systems such as wind generation is getting attention. This paper describes an optimal operation method to introduce the wind power forecasted data of the wind turbine generators (WTG). Moreover, the wind power forecasted data is considered its forecast error. The optimization purpose is to smooth the output power fluctuation of the wind farm (WF) and to obtain more benefit for electrical power selling. In the optimization of operation planning of WF, tabu search is used as the optimization method.

[1]  S. Bhattacharya,et al.  Control Strategies for Battery Energy Storage for Wind Farm Dispatching , 2009, IEEE Transactions on Energy Conversion.

[2]  Li Wang,et al.  Combining the Wind Power Generation System With Energy Storage Equipment , 2009, IEEE Transactions on Industry Applications.

[3]  M. O'Malley,et al.  A new approach to quantify reserve demand in systems with significant installed wind capacity , 2005, IEEE Transactions on Power Systems.

[4]  Tomonobu Senjyu,et al.  Application of Recurrent Neural Network to 3-Hours-Ahead Generating Power Forecasting for Wind Power Generators , 2009 .

[5]  T. Funabashi,et al.  Application of Recurrent Neural Network to Long-Term-Ahead Generating Power Forecasting for Wind Power Generator , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[6]  Ahlstrom,et al.  The future of wind forecasting and utility operations , 2005, IEEE Power and Energy Magazine.

[7]  Hu Minqiang,et al.  A novel exact and universal approach for calculating the differential leakage related to harmonic waves in AC electric motors , 2004 .

[8]  Le-Ren Chang-Chien,et al.  Strategies for Operating Wind Power in a Similar Manner of Conventional Power Plant , 2009, IEEE Transactions on Energy Conversion.

[9]  Tsung-Ying Lee Operating Schedule of Battery Energy Storage System in a Time-of-Use Rate Industrial User With Wind Turbine Generators: A Multipass Iteration Particle Swarm Optimization Approach , 2007, IEEE Transactions on Energy Conversion.

[10]  W.J. Lee,et al.  An integration of ANN wind power estimation into UC considering the forecasting uncertainty , 2005, IEEE Systems Technical Conference on Industrial and Commercial Power 2005..

[11]  G. Joos,et al.  Wind Power Impact on System Frequency Deviation and an ESS based Power Filtering Algorithm Solution , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[12]  Tomonobu Senjyu,et al.  Output power leveling of wind turbine Generator for all operating regions by pitch angle control , 2006 .

[13]  T. Nanahara,et al.  New Control Method for Regulating State-of- Charge of a Battery in Hybrid Wind Power/Battery Energy Storage System , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[14]  R. Yokoyama,et al.  Generation scheduling for wind power generation by storage battery system and meteorological forecast , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  J.A.P. Lopes,et al.  On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.

[16]  Ha Thu Le,et al.  Development and analysis of an ESS-based application for regulating wind farm power output variation , 2009, 2009 IEEE Power & Energy Society General Meeting.