含风电场的机组组合二阶段随机模型及其改进算法

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (397 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract This paper introduces two-stage stochastic model of unit commitment with wind farms. The objective cost of the model is divided into generating cost of thermal units and load shedding penalty cost. Due to the randomness of wind power, the latter cost is in the form of expectation. At the same time, a SAA-adaptive multi-cut L-shaped algorithm is proposed, where the sample average approximation (SAA) theory translates the proposed model into a certain one and the Adaptive multi-cut L-shaped algorithm solves the model. A kind of global assist variables is employed to save history optimal cuts and set upper limit of the main model’s constraint number. The iteration number of the proposed one is between the single-cut and multi-cut L-Shaped methods, while the computing time is the least. Finally, 3-uint, 10-unit and 100 unit systems are simulated with different sample numbers. The results verify the convergence and validity of the proposed model, and show the correctness of dealing with uncertainty of wind power with more samples.

Key words： Wind power unit commitment two-stage model sample average approximation stochastic programming L-shaped algorithm

Received: 26 June 2015 Published: 01 September 2016

PACS:

TM715

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Shi Tao
	Gao Shan
	Zhang Ningyu

Cite this article:

Shi Tao,Gao Shan,Zhang Ningyu. Two-Stage Stochastic Model of Unit Commitment with Wind Farm and an Improved Algorithm[J]. Transactions of China Electrotechnical Society, 2016, 31(16): 172-180.

URL:

https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I16/172

[1] 杨秀媛, 肖洋, 陈树勇. 风电场风速和发电功率预测研究[J]. 中国电机工程学报, 2005, 25(11): 1-5.
Yang Xiuyuan, Xiao Yang, Chen Shuyong. Wind speed and generated power forecasting in wind farm[J]. Proceedings of the CSEE, 2005, 25(11): 1-5.
[2] 葛晓琳, 张粒子. 考虑调峰约束的风水火随机机组组合问题[J]. 电工技术学报, 2014, 29(10): 222-230.
Ge Xiaolin, Zhang Lizi. Wind-hydro-thermal stochastic unit commitment problem considering the peak regulation constraints[J]. Transactions of China Electrotechnical Society, 2014, 29(10): 222-230.
[3] 徐帆, 刘军, 张涛. 考虑抽水蓄能机组的机组组合模型及求解[J]. 电力系统自动化, 2012, 36(12): 36-40.
Xu Fan, Liu Jun, Zhang Tao. Unit commitment problem with pumped-storage units[J]. Automation of Electric Power Systems, 2012, 36(12): 36-40.
[4] 盛四清, 孙晓霞. 考虑节能减排和不确定因素的含风电场机组组合优化[J]. 电力系统自动化, 2014, 38(17): 54-59.
Sheng Siqing, Sun Xiaoxia. Unit commitment optimi- zation containing wind farm considering energy saving, emission reducing and uncertainties[J]. Auto- mation of Electric Power Systems, 2014, 38(17): 54- 59.
[5] 杨林峰, 简金宝, 韩道兰. 机组组合问题的超立方锥松弛模型及其求解方法[J]. 电工技术学报, 2013, 28(7): 252-261.
Yang Linfeng, Jian Jinbao, Han Daolan. A hyper- cube cone relaxation model and solution for unit commitment[J]. Transactions of China Electrotech- nical Society, 2013, 28(7): 252-261.
[6] Feng Y, Ryan S M. Scenario reduction for stochastic unit commitment with wind penetration[C]//PES General Meeting, National Harbor, MD, 2014: 1-5.
[7] Pozo D, Contreras J. A chance-constrained unit commitment with an n-k security criterion and significant wind generation[J]. IEEE Transactions on Power System, 2013, 28(3): 2842-2851.
[8] 汪春, 吴可, 张祥文. 规模化电动汽车和风电协同调度的机组组合问题研究[J]. 电力系统保护与控制, 2015, 43(11): 41-48.
Wang Chun, Wu Ke, Zhang Xiangwen. Unit commitment considering coordinated dispatch of large scale electric vehicles and wind power gener- ation[J]. Power System Protection and Control, 2015, 43(11): 41-48.
[9] 吴小珊, 张步涵, 袁小明. 求解含风电场的电力系统机组组合问题的改进量子离散粒子群优化方法[J]. 中国电机工程学报, 2013, 33(4): 45-52.
Wu Xiaoshan, Zhang Buhan, Yuan Xiaomin. Solutions to unit commitment problems in power systems with wind farms using advanced quantum- inspired binary PSO[J]. Proceedings of the CSEE, 2013, 33(4): 45-52.
[10] 孙东磊, 韩学山, 杨金洪. 计及电压调节效应的电力系统机组组合[J]. 电工技术学报, 2016, 31(5): 107-117.
Sun Donglei, Han Xueshan, Yang Jinhong. Power system unit commitment considering voltage regula- tion effect[J]. Transactions of China Electrotech- nical Society, 2016, 31(5): 107-117.
[11] 艾小猛, 韩杏宁, 文劲宇. 考虑风电爬坡事件的鲁棒机组组合[J]. 电工技术学报, 2015, 30(24): 188- 195.
Ai Xiaomeng, Han Xingning, Wen Jinyu. Robust unit commitment considering wind power ramp events[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 188-195.
[12] 叶荣, 陈皓勇, 王钢. 多风电场并网时安全约束机组组合的混合整数规划解法[J]. 电力系统自动化, 2010, 34(5): 29-33.
Ye Rong, Chen Haoyong, Wang Gang. A mixed integer programming method for security-constrained unit commitment with multiple wind farms[J]. Auto- mation of Electric Power Systems, 2010, 34(5): 29- 33.
[13] Xiong Peng, Jirutitijaroen P. Convergence acceler- ation techniques for the stochastic unit commitment problem[C]//2010 IEEE 11th International Confer- ence on Probabilistic Methods Applied to Power Systems, Singapore, 2010: 364-371.
[14] Wang Qianfan, Guan Yongpei, Wang Jianhui. A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output[J]. IEEE Transactions on Power Systems, 2012, 27(1): 206-215.
[15] 张宁宇, 高山, 赵欣. 一种考虑风电随机性的机组组合模型及其算法[J]. 电工技术学报, 2013, 28(5): 22-29.
Zhang Ningyu, Gao Shan, Zhao Xin. An unit commitment model and algorithm with randomness of wind power[J]. Transactions of China Electro- technical Society, 2013, 28(5): 22-29.
[16] Yong Fu, Shahidehpour M. Security-constrained unit commitment with AC constraints[J]. IEEE Transa- ctions on Power Systems, 2005, 20(3): 1538-1550.
[17] Pagnoncelli B K, Ahmed S, Shapiro A. Sample average approximation method for chance constrained programming: theory and applications[J]. Journal of Optimiz Theory and Applications, 2009, 142(2): 399-416.
[18] Kleywegy A J, Shapiro A, Mello T H D. The sample average approximation method for stochastic discrete optimization[J]. SIAM Journal of Optimization, 2002, 12(2): 479-502.
[19] Ahmed S, Shapiro A. The sample average appro- ximation method for stochastic programs with integer recourse[J]. SIAM Journal of Optimization, 2002: 1-23.
[20] Mark W K, Morton D P, Wood R K. Monte Carlo bounding techniques for determining solution quality in stochastic programs[J]. Operations Research Letters, 1999, 24(1-2): 47-56.
[21] Glynn P, Robinson S. Introduction to stochastic programming[M]. New York: Springer-Verlag, 1997.
[22] Kall P, Wallace S W. Stochastic programming[M]. Chichester: John Wiley&Sons, 1994.
[23] Birge J. Aggregation bounds in stochastic linear pro- gramming[J]. Mathematical Programming, 1985, 31(1): 25-41.
[24] Trukhanov S, Ntaimo L. Adaptive multicut aggregation for two-stage stochastic linear programs[J]. European Journal of Operational Research, 2010, 206(2): 395-406.
[25] Carrion M, Arroyo J M. A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J]. IEEE Transactions on Power Systems, 2006, 21(3): 1371-1378.
[26] Ongsakul W, Petcharaks N. Unit commitment by enhanced adaptive Lagrangian relaxation[J]. IEEE Transactions on Power Systems, 2004, 19(1): 620- 628.