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Calculation of Economic Dispatch of Power System Based on Improved Particle Swarm Optimization Algorithm Abstract

机译:基于改进粒子群优化算法的电力系统经济调度计算

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For the power system economic dispatch problem, this paper takes the power system generation fuel cost as the objective function, and take into account the net loss of the power system. At the same time, it neglects the valve point effect in the process of the generation and establish power system model to meet the running power balance constraints and the motor running In this paper, the IEEE30BUS power system is chosen as an example, and this paper uses the improved particle swarm optimization algorithm to simulate the power system of the known fuel consumption parameters, and the simulation result is compared with the optimization results of the basic particle swarm optimization algorithm. The result of system simulation is: the optimization result of the traditional particle swarm optimization is that the total power is 1375.63 MW, the net loss is 11.89 MW and the total cost is 16562.98 ($/h); The optimization results obtained by the improved particle swarm optimization algorithm are that the total power is 1375.08 MW, the net loss is 10.49 MW, the total cost is 16546.86 ($/h). The net loss of the improved algorithm is reduced by 2% compared with the traditional algorithm and the total cost is reduced by 6.12 ($/h). The simulation results show that the improved particle swarm optimization algorithm can effectively solve the power system economic dispatch calculation problem.
机译:对于电力系统经济调度问题,本文采用电力系统生成燃料成本作为目标函数,并考虑了电力系统的净损失。同时,它忽略了生成过程中的阀点效应,建立了电力系统模型,以满足运行功率平衡约束和运行的电机,作为示例,选择了IEEE30Bus电力系统,本文使用改进的粒子群优化算法来模拟已知的燃料消耗参数的电力系统,并将仿真结果与基本粒子群优化算法的优化结果进行比较。系统仿真的结果是:传统粒子群优化的优化结果是总功率为1375.63 MW,净损失为11.89兆瓦,总成本为16562.98($ / h);通过改进的粒子群优化算法获得的优化结果是总功率为1375.08 MW,净损失为10.49兆瓦,总成本为16546.86($ / h)。与传统算法相比,改进算法的净损失减少了2%,总成本减少了6.12($ / h)。仿真结果表明,改进的粒子群优化算法可以有效解决电力系统经济派遣计算问题。

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