免疫遗传算法及其在电力系统EELD中的应用

电力系统EELD问题是一个满足一定约束条件的多目标优化问题,利用基于进化策略的免疫遗传算法对这一问题求解.将发电燃料成本和污染控制成本视为抗原,各电力生产单元发电量的最优解视为抗体,以一个含有5个电力生产单元的燃煤电力系统模型为对象,给出利用免疫遗传算法解决这一问题的主要方法和步骤.并与基于遗传算法和Hopfield神经网络方法进行比较分析.结果证明此算法可以优化分配电力系统中各电力单元发电量,达到环境经济合理配置.

Application research of immune genetic algorithm in EELD of power system

An immune genetic algorithm based on evolutional strategy was used to achieve multiple-objective optimization with constraints of a power system's economic emission load dispatch(EELD).With costs of generating electricity fuel and controlling pollution regarded as antigens and quantities of generated electricity in all power units regarded as antibodies,a power system model with five coal-burning generating units was researched.A comparison of application effects of three algorithms was performed: an immune genetic algorithm,a general genetic algorithm,and a Hopfield neural network.The results show that this method can assign quantities of generated electricity for all power units effectively,so economic and environmental factors can be distributed rationally.