智能电网多目标动态经济优化调度方法研究

发布时间：2018-01-29 07:27

本文关键词： 经济调度多目标风电场电动汽车差分进化算法 Pareto最优解　出处：《华北电力大学(北京)》2016年硕士论文　论文类型：学位论文

【摘要】：经济调度是指在满足一定的约束条件的基础上,通过调度机组出力满足模型目标函数的基本问题,它在本质上是一种最优化问题。随着电动汽车和可再生能源的发展,传统经济调度模型不再局限于传统火电机组,经济调度问题有了新的发展；同时,基于对可再生能源和电动汽车等多种因素的考虑,原先的单目标函数已经不能满足要求,因此,研究智能电网背景下的多目标动态经济调度模型具有重要意义。针对智能电网背景下的多目标动态经济调度问题,分析了新能源和电动汽车并网对传统经济调度问题带来的影响；提出了一种改进的差分进化算法,并利用该算法对含风电场动态经济调度模型求解；融入混沌和量子的思想提出了一种混合优化算法,并利用该算法对含风电场和电动汽车的多目标动态经济调度进行求解。具体工作如下：1.在差分进化算法的基础上结合人工蜂群算法(ABC)的观察蜂加速进化操作、侦查蜂随机搜索操作提出了一种改进差分进化算法(IDE)；并在此基础上,引入量子思想和混沌思想,提出了一种混合改进量子差分进化算法(HIQDE),最终实现了降低种群规模、防止早熟收敛、提高局部和全局搜索能力的目的。2.提出了基于IDE算法的含风电场的动态经济调度模型。利用本文提出的IDE算法,以IEEE-30节点系统为例,对模型进行MATLAB仿真,并将IDE算法实验结果与其他ABC、DE、PSO算法进行对比。通过对目标函数值(费用)和功损的比较,验证了本文提出的改进算法的有效性。3.提出了基于HIQDE算法的含电动汽车和风电场经济调度的多目标动态经济调度模型。以IEEE-39节点系统为例,利用前文提出的基于Pareto最优的混合改进量子差分进化算法,对模型进行MATLAB仿真实验。实验结果表明本文提出的研究方案能很好的解决含新能源和电动汽车的经济调度问题。
[Abstract]:Economic dispatch refers to the basic problem of satisfying the objective function of the model by dispatching the generating units on the basis of satisfying certain constraint conditions. It is an optimization problem in essence. With the development of electric vehicles and renewable energy, the traditional economic scheduling model is no longer confined to traditional thermal power units, and the economic scheduling problem has a new development. At the same time, based on the consideration of renewable energy and electric vehicles, the original single-objective function can not meet the requirements. It is of great significance to study the multi-objective dynamic economic dispatching model in the context of smart grid, aiming at the multi-objective dynamic economic dispatching problem in the context of smart grid. The influence of new energy and electric vehicle grid connection on the traditional economic scheduling problem is analyzed. An improved differential evolutionary algorithm is proposed and used to solve the dynamic economic scheduling model of wind farm. In this paper, a hybrid optimization algorithm is proposed, which is based on the theory of chaos and quantum. The algorithm is used to solve the multi-objective dynamic economic scheduling with wind farm and electric vehicle. The main work is as follows: 1. Based on the differential evolutionary algorithm (DEA), the artificial bee colony algorithm (ABC) is used to solve the problem. To watch bees accelerate evolution. In this paper, an improved differential evolutionary algorithm (IDEN) is proposed for random search. On the basis of this, a hybrid improved quantum differential evolution algorithm (HIQDEA) is proposed by introducing quantum thought and chaos theory, which can reduce the population size and prevent premature convergence. The purpose of improving the local and global search ability. 2. A dynamic economic scheduling model with wind farm based on IDE algorithm is proposed. Using the IDE algorithm proposed in this paper, the IEEE-30 node system is taken as an example. The model is simulated by MATLAB, and the experimental results of the IDE algorithm are compared with those of other ABCs de PSO algorithms. The value of the objective function (cost) and the power loss are compared. The effectiveness of the improved algorithm proposed in this paper is verified. 3. A multi-objective dynamic economic scheduling model including electric vehicle and wind farm economic scheduling based on HIQDE algorithm is proposed. The IEEE-39 node system is used as the node system. For example. The hybrid improved quantum differential evolution algorithm based on Pareto is proposed in this paper. The model is simulated by MATLAB. The experimental results show that the proposed scheme can solve the economic scheduling problem with new energy sources and electric vehicles.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM73;TP18

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