面向智能电网的配电网多目标动态无功优化研究

发布时间：2018-02-08 19:02

本文关键词： 配电网无功优化分布式电源混沌蜂群差分进化算法多目标优化动态优化　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】：配电网无功优化是改善配电网系统电能质量、有效提高电力系统稳定性的重要方法。电力系统无功优化不仅能降低系统的有功损耗,还能对电网的电压进行调整,从而保障电力系统安全、稳定的运行。随着智能电网建设进程的加快,分布式电源(DG)将批量并入电网,会对系统潮流、电压造成一定影响,使传统的配电网无功优化问题发生改变。因此,在DG并网的背景下,研究配电网无功优化问题具有重要的工程意义。由于DG的并网和DG的随机性使传统的无功优化模型不再适用,针对这一问题,本文提出了一种新型的优化算法——混沌蜂群差分进化算法(CABC-DE)。并分别用于建立含DG多目标无功优化模型和动态无功优化模型。具体工作如下：1.针对差分进化算法(DE)具有收敛过早、局部搜索能力较差的缺陷。本文采用人工蜂群算法(ABC)和混沌思想(Chaos)对其进行改进,提出了混沌蜂群差分进化算法(CABC-DE)。建立以最小化有功网损为目标函数的模型,将CABC-DE算法和DE算法分别用于IEEE-14和IEEE-30节点系统进行仿真实验,仿真结果表明了新算法的有效性。2.研究了DG并网后的配电网系统的多目标无功优化问题。在Pareto最优理论的基础上提出了多目标混沌蜂群差分进化算法。随后构建以有功网损最小、电压偏差最小、无功补偿最小为目标函数的多目标无功优化模型,在含DG的IEEE-30节点系统中进行仿真。仿真结果表明该算法能获得分布均匀的Pareto前沿,实现了对三个目标的优化,同时满足了电网运行对经济和安全两方面的要求。3.研究了DG并网后的配电网系统的动态无功优化。为了充分考虑到DG功率输出的随机性和配电网系统的安全性、经济性,首先构建了含DG的配电网动态无功优化模型,限制无功控制设备的操作次数以保障操作成本和设备使用寿命。然后考虑了系统负荷的变化,将日均负荷曲线分割大小时段,每个时段充分考虑DG的随机性,获得最优的动态无功优化方案。
[Abstract]:Reactive power optimization is an important method to improve the power quality and improve the stability of the power system. The reactive power optimization can not only reduce the active power loss, but also adjust the voltage of the distribution network. In order to ensure the safe and stable operation of the power system, with the acceleration of the construction process of the smart grid, the DGG will be incorporated into the power network in batches, which will have a certain impact on the power flow and voltage of the system. The traditional reactive power optimization problem of distribution network is changed. Therefore, under the background of DG, It is of great engineering significance to study the reactive power optimization problem in distribution network. Because of the connection of DG and the randomness of DG, the traditional reactive power optimization model is no longer applicable. In this paper, a new optimization algorithm, chaotic bee colony differential evolution algorithm (CABC-DEE), is proposed. It is used to establish multi-objective reactive power optimization model with DG and dynamic reactive power optimization model respectively. The specific work is as follows: 1. For differential evolution algorithm (DEE), it is premature to converge. In this paper, the artificial bee colony algorithm (ABC) and chaos algorithm (Chaos) are used to improve the local search ability, and a chaotic colony differential evolution algorithm (CABC-DEA) is proposed. A model is established to minimize the active power loss as the objective function. The CABC-DE algorithm and DE algorithm are used in the simulation experiment of IEEE-14 and IEEE-30 node system, respectively. The simulation results show the effectiveness of the new algorithm. 2. The multi-objective reactive power optimization problem of the distribution network system after DG is connected is studied. Based on the Pareto optimal theory, a multi-objective chaotic honeycomb differential evolution algorithm is proposed, and then the multi-objective chaotic honeycomb differential evolution algorithm is constructed. In order to minimize the active power network loss, The multi-objective reactive power optimization model with minimum voltage deviation and minimum reactive power compensation is simulated in the IEEE-30 node system with DG. The simulation results show that the algorithm can obtain uniformly distributed Pareto front and realize the optimization of the three targets. In order to fully consider the randomness of DG power output and the security of distribution network system, the dynamic reactive power optimization of DG connected distribution network system is studied in order to fully consider the randomness of DG power output and the security and economy of distribution network system. Firstly, the dynamic reactive power optimization model with DG is constructed to limit the operation times of reactive power control equipment to ensure the operation cost and the service life of the equipment. Then, considering the change of system load, the daily average load curve is divided into large and small periods. Considering the randomness of DG in every period, the optimal dynamic reactive power optimization scheme is obtained.
【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】：TM714.3;TP18

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