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一种用于机组组合问题的改进双重粒子群算法

, PP. 189-195

Keywords: 机组组合,双重粒子群优化,分时段,临界算子,罚函数

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Abstract:

为了更经济快速地解决机组组合问题,提出一种改进双重粒子群优化(particleswarmoptimization,PSO)算法,包含离散部分和连续部分。离散PSO分时段优化机组的启停状态,在种群更新时加入了临界算子,改进了可行解的判别条件,各机组出力最低值的和要在一定程度上低于负荷需求值,并考虑机组启停时间的向前继承和向后约束。连续PSO用于启停状态确定过程中和确定后的负荷分配,考虑功率平衡约束、热备用约束和机组的出力上下限约束。求解经济负荷分配时,利用罚函数的方法满足机组的爬坡速率约束,最后得到煤耗最小值。采用2个24时段的算例进行仿真,实验结果表明新算法减少了搜索量,提高了收敛速度,并为机组组合问题提出了新思路。

References

[1]  白晓民,于尔铿. 用动态规划法进行电力系统机组组合最优化[J]. 中国电机工程学报, 1984,4(1):11-19. Bai Xiaomin,Yu Erkeng.Optimization for unit commitment of electric power system by dynamic programming[J].Proceedings of the CSEE,1984,4(1)
[2]  王治国,刘吉臻,谭文,等. 基于快速性与经济性多目标优化的火电厂厂级负荷分配研究[J]. 中国电机工程学报, 2006,26(19):86-92. Wang Zhiguo,Liu Jizhen,Tan Wen,et al.Multi-objective optimal load distribution based on speediness and economy in power plants[J].Proceedings of the CSEE,2006,26(19)
[3]  黎静华,韦化. 求解机组组合问题的领域搜索法[J]. 中国电机工程学报, 2008,28(13):33-40. Li Jinghua,Wei Hua.Unit commitment via local search point method[J].Proceedings of the CSEE,2008,28(13)
[4]  Virmani S,Adrian E C,Imhof K,et al. Implementation of a lagrangian relaxation based unit commitment problem [J]. IEEE Trans. on Power Systems, 1989,4(4):1373-1380.
[5]  李树山,李刚,程春田,等. 动态机组组合与等微增率法相结合的火电机组节能负荷分配方法[J]. 中国电机工程学报, 2011,31(7),41-47. Li Shushan,Li Gang,Cheng Chuntian,et al.Thermal unit?s energy conservation load dispatch method with combining dynamic unit commitment into equal incremental principle[J].Proceedings of the CSEE,2011,31(7),41-47(in Chinese).
[6]  余廷芳,林中达. 部分解约束算法在机组负荷优化组合中的应用[J]. 中国电机工程学报, 2009,29(2):107-112. Yu Tingfang,Lin Zhongda.Application of float genetic algorithms-partially solved combined with punishing function in power plant units commitment problem [J].Proceedings of the CSEE,2009,29(2)
[7]  蔡超豪,蔡元宇. 机组优化组合的遗传算法[J]. 电网技术, 1997,21(1):44-47,51. Cai Chaohao,Cai Yuanyu.Optimization of unit commitment by genetic algorithm[J].Power System Technology,1997,21(1)
[8]  Ouyang Z,Shahidehpour S M. A hybrid artificial neural network-dynamic programming approach to unit commitment[J]. IEEE Trans. on Power Systems, 1992,7(1):236-242.
[9]  顾锦汶,杨佰新. 电力系统机组组合优化的快速模拟退火算法[J]. 中国电机工程学报, 1992,12(6):69-73. Gu Jinwen,Yang Baixin.The unit commitment by fast simulated annealing algorithm[J].Proceedings of the CSEE,1992,12(6)
[10]  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. Hu Jiasheng,Guo Chuangxin,Cao Yijia.A hybrid particle swarm optimization method for unit commitment problem [J].Proceedings of the CSEE,2004,24(4)
[11]  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. Lou Suhua,Yu Xinmei,Xiong Xinyin,et al.Unit commitment using improved discrete particle swarm optimization algorithm[J].Proceedings of the CSEE,2005,25(8)
[12]  Kennedy J,Eberhart R. Particle swarm optimization[C]// Proceedings of IEEE Conference on Neural Networks. Perth, Australia:IEEE,1995:1942-1948.
[13]  Eberhart R,Kennedy J. A new optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan:IEEE,1995:39-43.
[14]  Kennedy J,Eberhart R. A discrete binary version of the particle swarm optimization[C]//1997 IEEE International Conference on Systems. Perth, Australia:IEEE,1997:4104-4108.
[15]  韩学山,柳焯. 考虑发电机组输出功率速度限制的最优机组组合[J]. 电网技术, 1994,18(6):11-16. Han Xueshan,Liu Zhuo.Optimal unit commitment considering unit?s ramp-rate limits[J].Power System Technology,1994,18(6)
[16]  Yang H T,Yang Paichuan. A parallel genetic algorithm approach to solving the unit commitment problem:implementation on the transputer networks[J]. IEEE Trans. on Power Systems, 1997,12(2):661-668.

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