Adaptive Genetic Algorithm Based Parameters Optimization of PI Controller for Battery System in Wind Farm

Abstract:

The randomness and intermittence of wind farm real power generation bring challenges to power system operation, and installing battery system for the mitigation of the fluctuation of wind farm output, following the short-term forecasting curve, even adjusting the output according to the operator’s requirement is a possible way to address the problem from the wind farm side. After a review of various storage control strategies for stabilizing the fluctuation of wind power output, the model of battery energy storage system as well as its control strategy is introduced. Adaptive Genetic Algorithm (AGA) is used for the optimization of PI control parameters. Simulation shows the effectiveness of the proposed method. Moreover, comparing with the trial-and-error method, the optimization algorithm proposed has the advantage of finding the optimal parameters under the lack of experience on PID control, and combined with trial-and-error method, the difficulties engineer could face on tuning the parameters of PI controller is decreased, which increases the feasibility for parameters of PI controller’s being transplanted to similar applications.

• Periodical: Advanced Materials Research (Volume 1055)
• Pages: 375-382
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1055.375
• Online since: November 2014
• Authors: Hui Ren, Dan Wei, David Watts, Jia Qi Fan
• Keywords: Adaptive Genetic Algorithm, Battery Storage Model, Parameter Optimization, PI Control, Wind Farm

References

[1] Botterud, A.; Zhou, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V., Wind power forecasting, unit commitment, and electricity market operations, Power and Energy Society General Meeting, 2011 IEEE , vol., no., p.1, 2, 24-29 July (2011).

DOI: 10.1109/pes.2011.6039192

[2] Frieder Borggrefe, and Karsten Neuhoff. Balancing and intraday market design: options for wind integration. German Institute for Economic Research. 2011, available online: http: / www. diw. de/discussionpapers.

DOI: 10.2139/ssrn.1945724

[3] Hui Ren, Wenhui Fan, Zengqiang Mi. A design and control review of the wind energy storage system. Energy Storage Science and Technology, 2013, 9, 2(5).

[4] PARKHIDEH B, ZENG Jie, BAEK S, et al. Improved wind farm's power availability by battery energy storage systems: modeling and control [C] /34th Annual Conference of IEEE Industrial Electronics, November 10-13, 2008, Orlando. USA: 784-789.

DOI: 10.1109/iecon.2008.4758053

[5] Quanyuan Jiang, Haijiao Wang. Two-time-scale coordination control for a battery energy storage system to mitigate wind power fluctuations. IEEE Trans on Energy Conversion, 2013, 28(1) : 52-61.

DOI: 10.1109/tec.2012.2226463

[6] TELEKE S, BARAN M E, HUANG A Q, et al. Control strategies for battery energy storage for wind farm dispatching. IEEE Trans on Energy Conversion, 2009, 24( 3) : 725-732.

DOI: 10.1109/tec.2009.2016000

[7] TELEKE S, BARAN M E, BHATTACHARYA S, et al. Optimal control of battery energy storage for wind farm dispatching. IEEE Trans on Energy Conversion, 2010, 25(3): 787-794.

DOI: 10.1109/tec.2010.2041550

[8] HUSS IENZF, IIS MAIL A B, LEE W C, et al. Voltage sag mitigation using NaS battery
based standby power supply. Proceedings of IEEE International Conference on Power Electronics and Drive Systems, November 28
December 1, 2005, Kuala Lumpur, Malaysia: 1317-
1321.

DOI: 10.1109/peds.2005.1619891

[9] Xiaofeng Sun; Guangming Pei; Shuai Yao; Zhe Chen, A novel multi-port dc/dc converter with bi-directional storage unit, Power Electronics and Motion Control Conference (IPEMC), 2012 7th International , vol. 3, no., p.1771, 1775, 2-5 June. (2012).

DOI: 10.1109/ipemc.2012.6259105

[10] Priewasser, R.; Agostinelli, M.; Unterrieder, C.; Marsili, S.; Huemer, M., Modeling, Control, and Implementation of D-DC Converters for Variable Frequency Operation, Power Electronics, IEEE Transactions on , vol. 29, no. 1, p.287, 301, Jan. (2014).

DOI: 10.1109/tpel.2013.2248751

[11] Guohan Lin; Guofan Liu, Tuning PID controller using adaptive genetic algorithms, Computer Science and Education (ICCSE), 2010 5th International Conference on , vol., no., p.519, 523, 24-27 Aug. (2010).

DOI: 10.1109/iccse.2010.5593559

[12] Guoxin Zhang; Bo Zhao; Liefeng Liu, Research on GA operation and application based on processes, Conference Anthology, IEEE , vol., no., p.1, 4, 1-8 Jan. (2013).

DOI: 10.1109/anthology.2013.6784831