Abstract：The unit commitment(UC) problem in wind power integrated system is not a traditional certain problem because of wind power's random. The solution which is economic and reliable is difficult to attain by traditional ways. This paper puts forwards a mathematical model of UC in wind power integrated system based on chance constrained programming (CCP). It describes the related constrained conditions by probability form, and transforms UC problem into inside and outside optimization sub-problems. The outside sub-problem is units' on/off status optimization. It is effectively solved by discrete binary particle swarm optimization (BPSO) and heuristic searching strategy. The inside sub-problem is economic dispatch(ED). Considering the random of wind power, the sub-problem is solved by improved particle swarm optimization (PSO) based on stochastic simulation which avoids local optimal solution and ensures the feasibility of generation plans. The optimization algorithm is proved feasible and effective by testing a 10-unit system and being compared with other methods.
江岳文, 陈冲, 温步瀛. 含风电场的电力系统机组组合问题随机模拟粒子群算法[J]. 电工技术学报, 2009, 24(6): 129-137.
Jiang Yuewen, Chen Chong, Wen Buying. Particle Swarm Research of Stochastic Simulation for Unit Commitmen in Wind Farms Integrated Power System. Transactions of China Electrotechnical Society, 2009, 24(6): 129-137.
[1] Kazarlis S A, Bakirtzis A G, Petridis V．A genetic algorithm solution to the unit commitment problem[J]. IEEE Transactions on Power Systems, 1996, 11(1): 83-92. [2] 袁晓辉, 王乘, 袁艳斌, 等. 一种求解机组组合问题的新型改进粒子群方法[J]. 电力系统自动化, 2005, 29(1): 34-38. [3] Gaing Zwe Lee. Discrete particle swarm optimization algorithm for unit commitment[C]. Proceeding of IEEE Power Engineering Society General Meeting, Toronto, 2003: 418-424. [4] 赵波, 曹一家. 电力系统机组组合问题的改进粒子群优化算法[J]. 电网技术, 2004, 28(21): 6-10. [5] 胡家声, 郭创新, 曹一家. 一种适合于电力系统机组组合问题的混合粒子群优化算法[J]. 中国电机工程学报, 2004, 24(4): 24-28. [6] 娄素华, 余欣梅, 熊信艮, 等. 电力系统机组启停优化问题的改进DPSO算法[J]. 中国电机工程学报, 2005, 25(8): 30-35. [7] 蒋秀洁, 吴永华, 杨敏. 基于PSO-GA算法的电力系统机组组合研究[J]. 继电器, 2006, 34(5): 34-38. [8] Tomonobu S, Shimabukuro K, Uezato K, et al. A fast technique for unit commitment problem by extended priority list[J]. IEEE Trans. on Power Systems, 2003, 18(2): 882-888. [9] 杨俊杰, 周建中, 喻菁, 等. 一种求解大规模机组组合问题的混合智能遗传算法[J]. 电网技术, 2004, 28(19): 47-50. [10] Kumaran G, Mouly V S R K. Using evolutionary computation to solve the economic load dispatch problem[C]. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, 2001: 296-301. [11] 刘勇, 侯志俭, 蒋传文. 求解机组组合问题的改进离散粒子群算法[J]. 电力系统自动化, 2006, 30(4): 35-39. [12] Damousis I G, Bakirtzis A G, Dokopoulos P S. Network-constrained economic dispatch using real-coded genetic algorith[J]. IEEE Trans. on Power Systems, 2003, 18(1): 198-205. [13] 雷亚洲. 与风电并网相关的研究课题[J]. 电力系统自动化, 2003, 27(8): 84-89. [14] 陈海焱, 陈金富, 段献忠. 含风电场电力系统经济调度的模糊建模及优化算法[J]. 电力系统自动化, 2006, 30(2): 22-26. [15] Schlueter R A, et al. Modification of power system operation for significant wind generation penetr- ation[J]. IEEE Transactions on Power Apparatus and Systems, 1983, PAS-102(1):153-161. [16] 刘宝碇, 赵瑞清. 随机规划与模糊规划[M]. 北京：清华大学出版社, 1998. [17] 雷亚洲, 王伟胜, 印永华, 等. 基于机会约束规划的风电穿透功率极限计算[J]. 中国电机工程学报, 2002, 22(5): 32-35. [18] 杨宁, 文福拴. 基于机会约束规划的输电系统规划方法[J]. 电力系统自动化, 2004, 28(14): 23-27. [19] 雷亚洲, 王伟胜, 印永华, 等. 风电对电力系统运行价值分析[J]. 电网技术, 2006, 26 (5): 10-14. [20] Bowden G J, Barker P R, Shestopal V O, et al. The weibull distribution function and wind power statistics[J]. Wind Engineering, 1983, 7: 85-98. [21] 王海超, 鲁宗相, 周双喜. 风电场发电容量可信度研究[J]. 中国电机工程学报, 2005, 25(10): 103-106. [22] 丁明, 吴义纯. 风力发电系统运行和规划问题综 述[J]. 电网技术, 2003, 7(3): 36-40. [23] 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. [24] Kennedy J, Eberhart R C. A discrete binary version of the particle swarm algorithm[C]. Proceedings of the Conference on System, Man and Cybernetics, Piscataway, 1997: 4104-4108. [25] 冯奇峰, 李言. 改进粒子群优化算法在工程优化问题中的应用研究[J]. 仪器仪表学报, 2005, 26(9): 984-987.
2009, Vol. 24 
