基于模糊免疫算法的电网电源自动规划优化方法研究

Research on the Optimization Method of Power Supply Automatic Planning Based on Fuzzy Immune Algorithm

为了满足日益增长的电力能源需求,分布式电源并入大电网的规模呈现逐渐加大的趋势。但是,由于可再生能源——风能、太阳能等存在着无规律特征,使得电源出力不确定,容易造成电力能源分布不均的现象,影响电网的稳定运行,提出基于模糊免疫算法的电网电源自动规划优化方法研究。构建电网电源(火力电源、风力电源、水力电源与光伏电源)数学模型,以此为基础,以投资成本、网络损耗及其环境效益为目标,构造电网电源自动规划优化目标函数,并确定约束条件——电源容量约束条件、功率平衡约束条件与供电可靠性约束条件,基于模糊免疫算法确定最佳的电源自动规划优化方案,执行确定方案即可实现电网电源的自动规划优化。实验数据显示:在不同实验工况背景下,提出方法应用后获得电力不足频率最小值为4%,电源规划优化成本最小值为101万元,污染物排放量最小值为10 mg/m3,充分证实了提出方法电源规划优化效果更好。

In order to meet the growing demand for electricity and energy,the scale of integrating distributed power sources into the large power grid is gradually increasing.However,due to the irregular characteristics of renewable energy sources such as wind and solar energy,the power output is uncertain,which can easily lead to uneven distribution of power energy and affect the stable operation of the power grid.Therefore,a fuzzy immune algorithm based automatic power planning optimization method for power grids is proposed.Build a mathematical model for power grid power sources(thermal power,wind power,hydraulic power,and photovoltaic power),based on which,with investment costs,network losses,and environmental benefits as objectives,construct an optimization objective function for automatic planning of power grid power sources,and determine the constraints-power source capacity constraints,power balance constraints,and power supply reliability constraints,Based on the fuzzy immune algorithm,the optimal automatic planning and optimization scheme for power sources can be determined.By executing the determined scheme,the automatic planning and optimization of power sources in the power grid can be achieved.The experimental data shows that under different experimental conditions,the minimum frequency of power shortage obtained by the proposed method is 4%,the minimum cost of power planning optimization is 1.01 million yuan,and the minimum pollutant emission is 10 mg/m3,fully confirming that the proposed method has better power planning optimization effect.