含风电场的机组组合二阶段随机模型及其改进算法

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (397 KB)
输出: BibTeX | EndNote (RIS)

摘要提出一种含风电场的机组组合二阶段随机规划模型,将风电功率作为随机变量处理,目标函数包含常规机组发电成本和切负荷惩罚费用,由于风电功率存在多种可能的情景,后一种费用采用期望值形式,同时提出一种求解二阶段模型的SAA-自适应多切割L形算法,具体为首先基于抽样平均逼近（SAA）理论,将随机模型转换成确定性模型,然后提出一种自适应多切割L形算法求解。求解中引入全局辅助变量实现迭代过程中历史最优切割信息的保存,并设置主模型约束条件数上限保证模型始终具有较小的规模。与传统单切割和多切割L形算法相比,所提出算法的迭代次数介于两者之间,但计算时间要少于两者。最后通过3机、10机和100机算例在不同数量的风电情景下仿真计算,结果表明本文模型可以有效处理风电随机性,SAA-自适应多切割L形算法在样本数量较大的情况下保持了良好的收敛性和可靠性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	施涛
	高山
	张宁宇

关键词 ：风电, 机组组合, 二阶段模型, 抽样平均逼近, 随机规划, L形算法

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

收稿日期: 2015-06-26 出版日期: 2016-09-01

PACS:

TM715

基金资助:国家高技术研究发展计划（863计划）资助项目（2011AA05A105）

作者简介: 施涛男,1982年生,博士研究生,高级工程师,研究方向为新能源发电并网规划与优化运行。E-mail: shitao@epri.sgcc.com.cn（通信作者）;高山男,1973年生,副教授,博士生导师,研究方向为电力系统优化规划与运行。E-mail: shangao@seu.edu.cn

引用本文:

施涛, 高山, 张宁宇. 含风电场的机组组合二阶段随机模型及其改进算法[J]. 电工技术学报, 2016, 31(16): 172-180. Shi Tao, Gao Shan, Zhang Ningyu. Two-Stage Stochastic Model of Unit Commitment with Wind Farm and an Improved Algorithm. Transactions of China Electrotechnical Society, 2016, 31(16): 172-180.

链接本文:

https://dgjsxb.ces-transaction.com/CN/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.