Optimal Power Dispatch and Control of an Integrated Wind Turbine and Battery System

Author:

Ma Zheren1,Shaltout Mohamed L.2,Chen Dongmei3

Affiliation:

1. Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712

2. Department of Mechanical Design and Production, Cairo University, Giza 12613, Egypt

3. Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712 e-mail:

Abstract

Battery energy storage systems (BESSs) have been integrated with wind turbines to mitigate wind intermittence and make wind power dispatchable as traditional power sources. This paper presents two phases of optimizations, namely, power scheduling and real-time control that allows an integrated wind turbine and BESS to provide the grid with consistent power within each dispatch interval. In the power scheduling phase, the desired battery state of charge (SOC) under each wind speed is first determined by conducting an offline probabilistic analysis on historical wind data. With this information, a computationally efficient one-step-ahead model predictive approach is developed for scheduling the integrated system power output for the next dispatch interval. In the real-time control phase, novel control algorithms are developed to make the actual system power output match the scheduled target. A wind turbine active power controller is proposed to track the reference power set point obtained by a steady-state optimization approach. By combining an internal integral torque control and a gain-scheduled pitch control, the proposed active power controller can operate around a desired tip speed ratio (TSR) without an accurate knowledge of turbine power coefficient curve. Compared to the conventional power scheduling and real-time controller, implementing the new methodology significantly reduces the ramp rate, generator torque changing rate, battery charging rate, and the power output deviation from the scheduled target. BESSs with various capacities and different wind profiles are considered to demonstrate the effectiveness of the proposed algorithms on battery sizing.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Reference33 articles.

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4. Jiang, X., Dong, B., Xie, L., and Sweeney, L., 2010, “Adaptive Gaussian Process for Short-Term Wind Speed Forecasting,” ECAI, pp. 661–666.http://www.ece.tamu.edu/~lxie/papers/Xie-AdaptiveGaussian-2010.pdf

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