多目标优化机组分配方法的研究与仿真  

Multi-Objective Research and Simulation of the Optimized Method

作　　者：杜鹃[1] 

机构地区：[1]齐齐哈尔大学计算机与控制工程学院,黑龙江齐齐哈尔161006

出　　处：《计算机仿真》2013年第10期180-183,共4页Computer Simulation

摘　　要：研究电力机组的优化调度问题。传统电力调度中心对电力厂级负荷调度分配采用的是机组直调方式,将AGC负荷指令下发给每台机组,直接调度每台机组负荷。但是,由于网内涉及机组很多,传统方法只从系统优化的角度进行分配,无法顾及各机组单个情况,分配结果不理想。为了提高厂级负荷优化分配的精度,提出一种自适应逃逸粒子群算法的多目标厂级负荷优化分配方法(IPSO)。建立一种多目标厂级负荷优化分配数学模型;采用自适应逃逸粒子群算法对其进行求解,引入自适应惯性权重和遗传算法变异机制,保持粒子的多样样,降低局部最优解的出现概率,以提高PSO算法收敛速度;最大程度优化单个机组的调度。仿真结果表明,IPSO可以获得更优的厂级负荷优化分配方案,低了全厂总能量消耗。On the traditional power system control center for electricity ChangJi load distribution unit USES a straight way, the energy management system （EMS） to send AGC load under the instruction to each units, direct dispatching unit load per set. A lot of, but, due to the network units difference is very big, the EMS can only consid- er system optimization, couldn＇t care units of a single case, the distribution result is not ideal. Load Optimal distribu- tion is an important way to reduce energy consumption, this paper proposes a multi objective load optimal method（ IP- SO） based on adaptive escape particle swarm optimization algorithm to meet accuracy requirements of load optimal distribution. Firstly, a multi - objective load optimal distribution mathematical model is established, and then the mathematical model is solved by adaptive escape particle swarm algorithm which the adaptive inertia weight and ge- netic algorithm mutation mechanism is introduced to improve the convergence speed of PSO algorithm and to prevent the local optimal, lastly, the simulation results are carried out to test the performance of IPSO algorithm. The simula- tion results show that the proposed algorithm can get better optimal load distribution scheme and reduce total energy consumption of the total plant.

关 键 词：厂级负荷 优化 粒子群优化算法 自适应惯性权重 变异机制 

分 类 号：TK249[动力工程及工程热物理—动力机械及工程]