基于混沌粒子群算法的火电厂厂级负荷在线优化分配  

Online Unit Load Economic Dispatch Based on Chaotic-particle Swarm Optimization Algorithm

作　　者：司风琪[1] 顾慧[1] 叶亚兰[1] 汪军[1] 徐治皋[1] 

机构地区：[1]东南大学能源与环境学院,江苏省南京市210096

出　　处：《中国电机工程学报》2011年第26期103-109,共7页Proceedings of the CSEE

摘　　要：机组负荷优化分配是降低发电厂能耗水平的重要技术手段,该文针对厂级负荷在线优化分配对算法速率和精度的要求,提出一种新的机组负荷实时分配模型,分别给出了机组自动发电控制和厂级负荷分配方式下负荷响应速率约束方程,并提出一种自适应约束边界,可显著提高算法计算效率,在满足电网对机组负荷品质要求的前提下实现全厂煤耗量最小的目标。提出采用混沌粒子群算法来求解实时负荷优化分配问题,采用自适应惯性权重以加快算法收敛速度,在粒子群算法解的邻域内进行混沌优化搜索,避免算法陷入局部极值点。文中给出了厂级负荷在线优化分配算法步骤,并进行了算例分析,验证了所提模型和算法的有效性。Unit economic load dispatch （ELD） plays an important role for energy saving and emissions reduction in power plant. To improve the accuracy and efficiency of ELD optimization algorithm, a new online unit economic load dispatch model was presented by balancing minimizing coal consumption and the requirement of load quality from grid. The constraints of unit load changing velocity with adaptively variable limits of constraints were given for both automatic generation control and ELD mode, which contribute to higher efficiency of algorithm. A chaotic particale swarm optimization algorithm was implemented for seeking the best solution of given model. In this algorithm, the adaptive inertia weight factors are used to accelerate the convergence speed, and chaotic searching is conducted within the neighbour set of the solutions to avoid the local minima. The detailed algrithm steps were also given in this paper. The case study revealed the validity of proposed model and algorithm.

关 键 词：电站 负荷分配 混沌粒子群 算法 优化 

分 类 号：TK121[动力工程及工程热物理—工程热物理]