Strategy of Smoothing Wind Power Fluctuation Based on H-P filtering Method

Advances in battery energy storage technology have provided new ideas for smoothing wind power fluctuations. However due to the high cost, battery energy storage cannot be configured on a large scale in power systems. By applying the H-P filtering method, the time-varying wind power fluctuation can be decomposed into long-term trend component parts and short-term fluctuation component parts. A coordinated control method is proposed in this paper that the long-term trend component parts of wind power fluctuations are stabilized by thermal power units while short-term fluctuation component parts are suppressed by battery energy storage systems (BESS). Thereby avoiding frequent regulation of the thermal power units and saving the capacity requirement of the BESS. Finally, the effectiveness of the proposed strategy is verified based on the PSCAD/EMTDC simulation model. The simulation results indicate the feasibility and advantage of the proposed coordinated control method.

[1]  S. Williamson,et al.  A Five-Parameter Analytical Curvefit Model for Open-Circuit Voltage Variation with State-of-Charge of a Rechargeable Battery , 2018, 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

[2]  Purnomo Sidi Priambodo,et al.  State of Charge (SoC) Analysis and Modeling Battery Discharging Parameters , 2018, 2018 4th International Conference on Science and Technology (ICST).

[3]  Seddik Bacha,et al.  On-line identification of DFIG parameters with rotor current reconstitution , 2014, 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER).

[4]  Feng Li,et al.  Joint Dispatching of Wind-thermal Power Considering Wind Power Consumption and Network Constraints , 2018, 2018 International Conference on Power System Technology (POWERCON).

[5]  Xiaodong Yuan,et al.  A Nonlinear Least Squares Method of Energy Storage Systems for Wind Power Fluctuations Smoothing , 2018, 2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC).

[6]  E. Prescott,et al.  Postwar U.S. Business Cycles: An Empirical Investigation , 1997 .

[7]  Johan Meyers,et al.  Power smoothing in large wind farms using optimal control of rotating kinetic energy reserves , 2015 .

[8]  Hazlee Azil Illias,et al.  Intermittent Smoothing Approaches for Wind Power Output: A Review , 2017 .

[9]  Subhashish Bhattacharya,et al.  Optimal Control of Battery Energy Storage for Wind Farm Dispatching , 2010, IEEE Transactions on Energy Conversion.

[10]  Xiao-Ping Zhang,et al.  Wind Power Smoothing by Controlling the Inertial Energy of Turbines With Optimized Energy Yield , 2017, IEEE Access.

[11]  Etienne Ntagwirumugara,et al.  Economic Analysis for Distributed Energy Supply Wind-Thermal Pumped Storage System and Technical Performance , 2018, 2018 IEEE PES/IAS PowerAfrica.

[12]  Marco Liserre,et al.  Overview of Multi-MW Wind Turbines and Wind Parks , 2011, IEEE Transactions on Industrial Electronics.

[13]  L. Ran,et al.  Use of turbine inertia for power smoothing of wind turbines with a DFIG , 2004, 2004 11th International Conference on Harmonics and Quality of Power (IEEE Cat. No.04EX951).

[14]  P. Sorensen,et al.  Power Fluctuations From Large Wind Farms , 2007, IEEE Transactions on Power Systems.