Probabilistic Spinning Reserve Model of Power System Containing Wind

Abstract:

In order to reflect the influences of the stochastic uncertain of wind power on the optimal decision-making of the power system spinning reserve, a probabilistic optimization model of the spinning reserve is proposed. Considering uncertain factors such as predicted-deviation of wind, predicted-deviation of load and fault outage of generator, the capacity outage probability table is combined with predicted-deviation of wind and predicted-deviation of load. By introducing the analytic expressed probability reserve constraints into the unit commitment model considering the wind power, the spinning reserve of power system can be optimized to the expectable contingency level. Using a calculation example, the effectiveness of the model is proved, providing a new model for uncertainty analysis and optimal scheduling decisions of power system containing wind.

• Periodical: Applied Mechanics and Materials (Volumes 799-800)
• Pages: 1238-1243
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.799-800.1238
• Online since: October 2015
• Authors: Ning Ning Cui*, Zhe Nan Zhang, Wei Liu, Yang Zhang, Qiang Li
• Keywords: COPT, LOLP, Spinning Reserve, Unit Commitment, Wind Power

References

[1] Jianxue Wang, Xifan Wang, Chaohong Bie. Reserve in the power market [J]. Automation of Electric Power Systems, 2001, 25(15): 7-11, 14.

[2] Reddy S S, Bijwe P R, Abhyankar A R. Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties[J]. Systems Journal, IEEE , 2013, PP(99): 1-3.

DOI: 10.1109/jsyst.2013.2272236

[3] Holttinen H, Milligan M; Ela E; Menemenlis N ; Dobschinski J; Rawn B; Bessa R. J; Flynn D; Gomez Lazaro E; Detlefsen N. Methodologies to determine operating reserves due to increased wind power[C]. Power and Energy Society General Meeting (PES), 2013 IEEE , Vancouver, BC, (2013).

DOI: 10.1109/pesmg.2013.6673067

[4] Menemenlis N, Huneault M, Robitaille A. Computation of dynamic operating balancing reserve for wind power integration for the time-horizon 1–48 hours[C]. Power and Energy Society General Meeting (PES), 2013 IEEE , Vancouver, BC, (2013).

DOI: 10.1109/pesmg.2013.6672338

[5] Le Wang, Zhiwei Yu, Fushuan Wen. A chance-constrained programming approach to determine requirement of optimal spinning reserve capacity[J]. Power System Technology, 2006, 30(20): 14-19.

[6] Bunn D W. Forecasting loads and prices in competitive power markets [J]. Proceedings of the IEEE, 2000, 88(2): 163-169.

DOI: 10.1109/5.823996

[7] Ronan Doherty, Mark O'Malley. A new approach to quantify reserve demand in systems with significant installed wind capacity [J]. IEEE Transactions on Power Systems, 2005, 22(2): 587-594.

DOI: 10.1109/tpwrs.2005.846206

[8] Bouffard F, Galiana F D. Stochastic security for operations planning with significant wind power generation[J]. IEEE Trans on Power Systems, 2008, 23(2): 306-316.

DOI: 10.1109/tpwrs.2008.919318

[9] Yongji Guo. Power system reliability analysis[M]. Beijing: Tsinghua University Press, 2003: 11-49.

[10] CHATTOPADHYAY D, BALDICK R. Unit commitment with probabilistic reserve[C]. 2002 IEEE Power Engineering Society Winter Meeting, New York, (2002).

DOI: 10.1109/pesw.2002.984999

[11] MIGUEL, ARROYO J M. A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J]. IEEE Trans on Power Systems, 2006, 21(3): 1371-1378.

DOI: 10.1109/tpwrs.2006.876672

[12] Kazarlis S A, Bakirtzis A G, Petridis V. A genetic algorithm solution to the unit commitment problem[J]. IEEE Trans on Power Systems, 1996, 11(2): 83–92.

DOI: 10.1109/59.485989

[13] Changyun Chen. The optimal operation study of power system with wind farm under market conditions[D]. Beijing: Beijing Jiaotong University, (2008).