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基于粒子群算法求解电力市场发电商最优供给函数模型 被引量:18

PSO Algorithm Based Optimal Supply Function Model for Power Producer
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摘要 发电商最优供给函数是一个两层优化问题,其中上层是发电商生产效益最大化问题,下层是基于最优潮流的市场最优化调度问题。启发式算法由于简单易行,最优解具有全局性,与初始点选择无关,因此是解决这类问题的一个良好选择。文中运用启发式粒子群优化算法(PSO)求解发电商生产效益两层优化问题,以获得发电商最优供给函数,与确定性方法的计算结果进行了比较,并对最优解的全局性和初始点选择进行了讨论。IEEE 30节点6机系统验证了所提方法的有效性。 The optimal supply function model for a power producer is a hi level mathematical programming problem in which the upper optimization is to maximize the producer's surplus whilst the lower one is to realize the market optimal dispatch based on optimal power flow. Thus the heuristic approach can be an alternative to solve the model above with simplicity and immune to the local optima. Particle swarm optimization (PSO) integrating with the heuristic approach is then presented in this paper to obtain the optimal supply functions for power producers. In addition, the result of the model proposed is compared with that of the deterministic approach, and the integrity of the optimal solutions is discussed as well as the selection of the starting point. The IEEE 30 bus system has proved the feasibility of the proposed model.
出处 《电力系统自动化》 EI CSCD 北大核心 2006年第2期45-50,共6页 Automation of Electric Power Systems
关键词 发电商 电力市场 最优供给函数 两层优化模型 粒子群优化算法 启发式算法 power producer electricity market optimal supply function bi-level optimization model particle swarm optimization heuristic approach
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参考文献17

  • 1WEBER J D, OVERBYE T J. A Two level Optimization Problem for Analysis of Market Bidding Strategies. In:Proceedings of IEEE Power Engineering Society Summer Meeting, Vol 2. Edmonton(Alta,Canada):1999. 682-687.
  • 2HOBBS B F, METZLER C B, PANG J S. Strategic Gaming Analysis for Electric Power Systems: An MPEC Approach.IEEE Trans on Power Systems, 2000, 15(2): 638-645.
  • 3FERRERO R W, RIVERA J F, SHAHIDEHPOUR S M.Application of Games with Incomplete Information for Pricing Electricity in Deregulated Power Pools. IEEE Trans on Power Systems, 1998, 13(1):184-189.
  • 4NANSEN P, JAUMARD B, SAVARD G.New Branch-and-bound Rules for Linear Bilevel Programming. SIAM Journal on Science and Statistical Computing, 1992, 13(5): 1194-1217.
  • 5SAVARD G, GAUVIN J. The Steepest Descent Direction for the Nonlinear Bi-level Programming Problem. Operations Research Letters, 1994, 15(5): 265-273.
  • 6BARD J F. An Efficient Point Algorithm for a Linear Two-stage Optimization Problem. Operations Research, 1983, 31(4):670-684.
  • 7WHITE D J,ANANDALINGAM G. A Penalty Function Approach for Solving Bilevel Linear Programs. Journal of Global Optimization, 1993, 3(4), 397-419.
  • 8MATHIEU R, PITTARD L, ANANDALINGAM G. Genetic Algorithm Based Approach to Bi-level Linear Programming.Operations Research, 1994, 28(1):1-21.
  • 9ODUGUWA V, ROY R. Bi-ievel Optimization Using Genetic Algorithm. In: Proceedings of IEEE International Conference on Artificial Intelligence Systems (ICAIS'02). Divnomorskoe(Russia) : 2002. 322-327.
  • 10KENNEDY J, EBERHART R. Particle Swarm Optimization.In, Proceedings of IEEE International Conference on Neural Network, Vol 4. Perth(Australia):1995. 1942-1948.

二级参考文献46

  • 1Eberhart R C, Hu X. Human Tremor Analysis Using Particle Swarm Optimization. In: Proc Congress on Evolutionary Computation 1999. Washington: 1999. 1927~1930.
  • 2Fukuyama Y, Yoshida H. A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems.In: Proc Congress on Evolutionary Computation 2001. Seoul(Korea) : 2001.
  • 3IEEE Task Force. Load Representation for Dynamic Performance Analysis. IEEE Trans on Power Systems, 1988,3(1): 134~148.
  • 4鞠 平(Ju Ping).电力系统非线性辨识(Nonlinear Parameter Identification of Power System).南京:河海大学出版社(Nanjing:Hohai University Press),1999.
  • 5沈善德(Shen Shande).电力系统辨识(Parameter Identification of Power System).北京:清华大学出版社(Beijing:Tsinghua University Press),1993.
  • 6Kennedy J,Eberhart R C.Swarm Intelligence.San Diego:Morgen Kaufmann Publishers,2001.
  • 7Eberhart R C,Kennedy J.A New Optimizer Using Particle Swarm Theory.In;Proceedings of the Sixth International Symposium on Micro Machine and Human Science.Nagoya(Japan):1999.39~43.
  • 8Kennedy J, Eberhart R. Particle swarm optimization[C]. IEEE International Conference on Neural Network, Perth, Australia, 1995.
  • 9Shi Y, Eberhart R. A modified particle swarm optimizer[C]. IEEE International Conference on Evolutionary Computation, Anchorage,Alaska, USA, 1998.
  • 10Clerc M. The swarm and the queen: toward a deterministic and adaptive particle swarm optimization[C]. Proceedings of the Congress of Evolutionary Computation, Washington DC, 1999.

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