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松弛节点电压对优化潮流及电力市场经济性的影响 被引量:4

Effects of slack bus voltage on optimal power flow and economy of power market
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摘要 用优化潮流(OPF)寻求发电总成本最低的运行方案是电力公司追求的主要目标之一。在网络拓扑和负荷水平一定的情况下,松弛节点电压的改变对优化程序和电力市场的经济性指标,如程序收敛性、系统发电总成本、有功网损和无功网损均会产生直接影响。文中运用优化工具对这一问题进行研究,并对IEEE-11、14和30节点系统进行仿真,仿真结果表明:采用优化技术可显著降低系统的发电总成本;维持恰当的松弛节点电压幅值也有助于节省发电费用和降低网损,带来理想的经济效益。对无功网损为负的网络,要对电压稳定和系统安全予以更多关注。 One of the main motives of electric utilities is to find out the scheme with minimum generation cost by means of OPF.When grid topology and loads are given,the change of slack bus voltage will directly affect the solution of OPF and economic per-formance of power market,such as convergence of program,total generation cost,MW or Mvar losses of power systems.For the first time ,the paper makes a more thorough research on this subject by optimization,and has completed simulations on IEEE11,14and30-bus systems.Some universal conclusions are obtained.Test results show that using of optimizing approach can reduce total generation cost and MW losses;a right voltage at slack bus is also helpful to reduce generation cost and grid losses,and bring utili-ties ideal benefits.To the system with negative Mvar losses,voltage stability and security of system should be paid more attentions.
出处 《中国电力》 CSCD 北大核心 2002年第3期35-39,共5页 Electric Power
基金 中国博士后科学基金资助项目(2001~2003年度)
关键词 电力系统 松弛节点电压 优化潮流 电力市场 经济性 power market slack bus voltage OPF total generation cost grid losses
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  • 1刘自发,葛少云,余贻鑫.基于混沌粒子群优化方法的电力系统无功最优潮流[J].电力系统自动化,2005,29(7):53-57. 被引量:74
  • 2李钟煦,刘玉田.一种地区电网分布式无功优化方法[J].电力系统及其自动化学报,2005,17(2):80-83. 被引量:7
  • 3俞俊霞,赵波.基于改进粒子群优化算法的最优潮流计算[J].电力系统及其自动化学报,2005,17(4):83-88. 被引量:36
  • 4诸俊伟.电力系统分析[M].北京:水利电力出版社,1995..
  • 5Natsuki Higashi,Hitoshi Iba.Particle swarm optimi-zation with Gaussian mutation[C]//Proceedings of the 2003 IEEE Swarm Intelligence Symposium,Phoenix,USA,2003:72-79.
  • 6Devicharan D,Mohan C K.Particle swarm optimiz-ation with adaptive linkage learning[C]//Proceedings of the 2004 International Conference on Evolutionary Computation.NewYork,USA,23nd,2004,1:530-535.
  • 7Ho S L,Yang Shiyou,Ni Guangzheng,et al.A par-ticle swarm optimization-based method for multiobjective design optimizations[J].IEEE Trans on Magnetics,2005,41(5):1756-1759.
  • 8Eckart Zitzler,Lothar Thiele.Multiobjective evolu-tionary algorithms:a comparative case study and the strength pareto approach[J].IEEE Trans on Evolutionary Computation,1999,3(4):257-271.
  • 9Coello C A,Lechuga M S.MOPSO:a proposal for multiple objective particle swarm optimization[J].IEEE Trans on Evolutionary Computation.,2002,2(4):1051-1056.
  • 10Shi Y,Eberhart R C.Empirical study of particle swarm optimization[C]//Proceeding of the 1999 Congress on Evolutionary Computation.Washington DC,USA,18nd,1999,3:1945-1950.

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