全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

风水气互补发电优化的云模型自适应粒子群优化算法

DOI: 10.13334/j.0258-8013.pcsee.2014.S.003, PP. 17-24

Keywords: 风水气互补发电,云模型,自适应粒子群优化算法

Full-Text   Cite this paper   Add to My Lib

Abstract:

提出一种改进的自适应粒子群优化(particleswarmoptimization,PSO)算法,对基本PSO算法中的惯性权重系数作云处理。由于云模型具有随机性和稳定倾向性,使得处理后的惯性权重既具有传统的趋向性,满足快速寻优能力,又具有随机性,有利于提高种群的多样性,提高收敛速度。在对认知系数和社会系数的处理上,考虑两者的相互关联性,在坐标平面内构造收敛曲线,让两者沿收敛曲线随时间动态调整,使得算法在进化过程中既能够保证收敛,又提高了算法性能。建立了风、水、气多种清洁能源互补的发电模型。模型考虑风电预测的随机误差,以一次能源天然气的消耗量最低为目标函数,约束条件包含了风能、水能等清洁能源的完全消纳。通过云模型粒子群算法求解该模型,并与基本遗传算法和PSO算法的结果进行比较,验证了所建模型可行性和算法的有效性。

References

[1]  Mohammed A,Mario C,Giuseppe S,et al.Multi-criteria optimal sizing of photovoltaic-wind turbine grid connected systems[J].IEEE Transactions on Energy Conversion,2013,28(2):370-379.
[2]  Edgardo D,Castronuovo J A,Pe?as L.On the optimization of the daily operation of a wind-hydro power plant[J].IEEE Transactions on Power Systems,2004,19(3):1599-1606.
[3]  包能胜.风电-燃气轮机互补发电系统若干关键问题的研究[D].北京:清华大学,2007. Bao Nengsheng.Research on the several key issues of hybrid power system combining wind farm with small gas turbine power plants[D].Beijing:Tsinghua University,2007(in Chinese).
[4]  吴杰康,唐利涛,黄奂,等.基于遗传算法和数据包络分析法的水火电力系统发电多目标经济调度[J].电网技术,2011,35(5):76-81. Wu Jiekang,Tang Litao,Huang Huan,et al.Multi- objective economic scheduling for hydrothermal power systems based on genetic algorithm and data envelopment analysis[J].Power System Technology,2011,35(5):76-81(in Chinese).
[5]  张海峰,高峰,吴江,等.含风电的电力系统动态经济调度模型[J].电网技术,2013,37(5):1298-1303. Zhang Haifeng,Gao Feng,Wu Jiang.et al.A dynamic economic dispatching model for power grid containing wind power generation system[J].Power System Technology,2013,37(5):1298-1303(in Chinese).
[6]  艾欣,刘晓,孙翠英.含风电场电力系统机组组合的模糊机会约束决策模型[J].电网技术,2011,35(12):202-207. Ai Xin,Liu Xiao,Sun Cuiying.A fuzzy chance constrained decision model for unit commitment of power grid containing large-scale wind farm[J].Power System Technology,2011,35(12):202-207(in Chinese).
[7]  元博,周明,李庚银,等.基于可靠性指标的含风电电力系统的发电和运行备用的协调调度模型[J].电网技术,2013,37(3):800-807. Yuan Bo,Zhou Ming,Li Gengyin,et al.A coordinated dispatching model considering generation and operating reserve for wind power integrated power system based on ELNSR[J].Power System Technology,2013,37(3):800-807(in Chinese).
[8]  吴杰康,熊焰.风、水、气互补发电模型的建立及求解[J].电网技术,2014,38(3):603-609. Wu Jiekang,Xiong Yan.Establishment and solution of the complementary power generation model of wind-energy,hydro-energy and natural gas[J].Power System Technology,2014,38(3):603-609(in Chinese).
[9]  陈海良,郭瑞鹏.基于改进离散粒子群算法的电力系统机组组合问题[J].电网技术,2011,35(12):94-99. Cheng Hailiang,Guo Ruipeng.Unit commitment based on improved discrete particle swarm optimization[J].Power System Technology,2011,35(12):94-99(in Chinese).
[10]  李整,谭文,秦金磊.一种用于机组组合问题的改进双重粒子群算法[J].中国电机工程学报,2012,32(35):189-196. Li Zheng,Tan Wen,Qin Jinlei.An improved dual particle swarm optimization algorithm for unit commitment problem[J].Proceedings of the CSES,2012,32(35):189-196.
[11]  李勇,王建君,曹丽华.考虑电网调度实时性要求的机组负荷优化分配[J].中国电机工程学报,2011,31(32):122-128. Li Yong,Wang Jianjun,Cao Lihua.Optimal load dispatch of units considering the real-time demand of power network dispatching[J].Proceedings of the CSES,2011,31(32):122-128(in Chinese).
[12]  王华秋,曹长修.基于模拟退火的并行粒子群优化研究[J].控制与决策,2005,20(5):500-504. Wang Huaqiu,Cao Changxiu.Parallel particle swarm optimization based on simulated annealing[J].Control and Decision,2005,20(5):500-504(in Chinese).
[13]  Eberhart R,Kennedy J.New optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro-machine and Human Science.Piscataway,NJ,USA:IEEE,1995:39-43.
[14]  Kennedy J,Eberhart R C.Particle swarm optimization [C]//IEEE International Conference on Neural Networks.Piscataway,NJ,USA:IEEE,1995:1942-194.
[15]  Trelea I C.The particle swarm optimization algorithm:convergence analysis and parameter selection [J].Information Proceeing Letters,2003,85(6):317-325.
[16]  Eberhart R C,Shi Y.Comparing inertia weights and constriction factors in particle swarm optimization[C]// Proceedings of the IEEE Conference on Evolutionary Computation.California:IEEE,2000:84-88.
[17]  Venter G,Sobieszczanski-Sobieski J.Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization[J].Structural and Multidisciplinary Optimization,2004,26(1-2):121-131.
[18]  Ratnaweera A,Halgamuge S K,Watson H C.Self- organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Transactions on Evolutionary Computation,2004,8(3):240-255.
[19]  李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34. Li Deyi,Liu Changyu.Study on the universality of the normal cloud model[J].Engineering Science,2004,6(8):28-34(in Chinese).
[20]  张光卫,何锐,刘禹,等.基于云模型的进化算法[J].计算机学报,2008,31(7):1082-1091. Zhang Guangwei,He Rui,Liu Yu,et al.An evolutionary algorithm based on cloud model[J].Chinese Journal of Computers,2008,31(7):1082-1091(in Chinese).
[21]  柴日发,徐文骞,曾文华.基于云模型的BP算法改进[J].计算机仿真,2002,19(3):123-126. Chai Rifa,Xu Wenqian,Zeng Wenhua.BP algorithm improvement based-on clouds model[J].Computer Simulation,2002,19(3):123-126 (in Chinese).
[22]  马穎,田维坚,樊养余.基于云模型的自适应量子粒子群算法[J].模式识别与人工智能,2013,26(8):787-793. Ma Ying,Tian Weijian,Fan Yangyu.Adaptive quantum-behaved particle swarm optimization algorithm based on cloud model[J].Pattern Recognition and Artificial Intelligence,2013,26(8):787-793(in Chinese).
[23]  李德毅,刘常昱,杜鹢,等.不确定性人工智能[J].软件学报,2004,15(11):1584-1595. Li Deyi,Liu Changyu,Du Yi,et al.Artificial intelligence with uncertainty[J].Journal of Software,2004,15(11):1584-1595(in Chinese).
[24]  李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20. Li Deyi,Meng Haijun,Shi Xuemei.Membership clouds and membership cloud generators[J].Journal of Computer Research and Development,1995,32(6):15-20(in Chinese).
[25]  Stewart D A,Essenwanger O M.Frequency distribution of wind speed near the surface[J].Journal of Applied Meteorology,1978,17(11):1633-1642.
[26]  李晓雨.三参数Weibull参数估计方法研究[D].北京:北京交通大学,2012. Li Xiaoyu.Research on estimation for the three-parameter Weibull distribution[D].Beijing:Beijing Jiaotong University,2012(in Chinese).

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133