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Research Papers

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

[+] Author and Article Information
Zheren Ma

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

Mohamed L. Shaltout

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

Dongmei Chen

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: dmchen@me.utexas.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 20, 2016; final manuscript received February 9, 2017; published online June 5, 2017. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 139(9), 091008 (Jun 05, 2017) (11 pages) Paper No: DS-16-1260; doi: 10.1115/1.4036074 History: Received May 20, 2016; Revised February 09, 2017

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.

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Figures

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Fig. 1

Power coefficient versus tip speed ratio and blade pitch angle

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Fig. 2

Integrated wind turbine and battery system

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Fig. 3

Wind speed data for site 102: (a) historical wind speed data and (b) wind speed forecast

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Fig. 4

Thirty-minutes average power versus wind speed curve for the NREL 5 MW wind turbine

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Fig. 5

Schematic plot of proposed power scheduling approach

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Fig. 6

Illustration of determining desired SOC

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Fig. 7

Desired SOC versus wind speed curves for different wind sites

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Fig. 8

Schematic plot of proposed real-time controller

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Fig. 9

A contour line for Cp*=0.4 and designed operation points for different active power controllers

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Fig. 10

Look-up table for reference TSR when c=0.7

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Fig. 11

Performance comparison between various scheduling approaches with a 1 MW h BESS and standard real-time controller

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Fig. 12

Performance comparison of active power controllers for reference power set point tracking

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Fig. 13

Fatigue loads comparison of active power controllers for reference power set point tracking

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Fig. 14

Induced damage equivalent loads (DELs) with various active power controllers compared to the baseline where the baseline DEL is obtained using standard controller

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Fig. 15

Performance comparison among various scheduling approaches and real-time controllers with a 1 MW h BESS

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Fig. 16

Generalized results with various scheduling approaches, real-time controllers, and battery capacities compared to the baseline, where the baseline performance is obtained using heuristic scheduling method, the standard real-time controller, and a 0.5 MW h battery

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