含分布式发电配电网无功优化研究

发布时间：2018-08-25 12:01

【摘要】：当前,大容量机组发电、超高压远距离输电以及跨区域的大电网并联是当今电力系统主要的生产、输送和分配方式。随着科学技术和社会经济的快速发展,企业和用户对电能的质量和可靠性的要求不断提高,配电网的规模不断扩大,其结构也随之日益复杂。配电网无功优化具有改变电能质量,调节配电网各节点电压,减少系统有功损耗等优点。近年来,全球变暖和环境污染问题日益严重,采用可再生能源发电的分布式发电技术得到全社会的重视。与传统的集中式大规模发电相比,分布式发电具有成本低,能源利用率高,环境污染少,能提高配电网的可靠性和灵活性的优点。在实际并网运行中,分布式发电要向配电网输送有功和无功功率,由于其具有供电间歇性和随机性,这将改变配电网潮流大小和流向,造成配电网的电压越限,网损加大,可靠性降低等情况。因此需要通过配电网无功优化降低分布式发电对配电网的影响,保证配电网的电能质量和可靠性。本文主要研究含分布式发电配电网无功优化问题。首先详细阐述配电网潮流计算方法及其计算过程。其次通过分析对比不同数量、位置和容量的分布式发电对配电网电压和网损的影响,理论分析及算例结果表明分布式发电具有降低配电网有功损耗,有效防止配电网局部节点电压越限的能力。针对粒子群算法易陷于局部优化问题和蚁群算法由于积累信息素导致寻优时间过长的缺点,利用粒子群算法的结构简单以及参数设定少的优点以及蚁群算法强大的全局搜索能力,提出将粒子群算法与蚁群算法相结合的混合优化算法(PSO-ACO),并结合惩罚函数构建无功优化数学模型。以IEEE-33节点配电系统进行系统仿真,与采用基本粒子群算法和蚁群算法的无功优化结果相比较,结果表明提出的混合算法的收敛性和稳定性更好,能有效的解决含DG配电网无功优化问题。
[Abstract]:At present, generation of large capacity units, ultra-high voltage long-distance transmission and parallel connection of large power grids across regions are the main modes of production, transmission and distribution of power systems. With the rapid development of science and technology and social economy, the requirements of power quality and reliability of enterprises and users are constantly improved, the scale of distribution network is expanding, and its structure is becoming more and more complex. The reactive power optimization of distribution network has the advantages of changing the power quality, regulating the voltage of each node in the distribution network and reducing the active power loss of the system. In recent years, the problem of global warming and environmental pollution is becoming more and more serious. Compared with the traditional centralized large-scale generation, distributed generation has the advantages of low cost, high energy efficiency, less environmental pollution, and can improve the reliability and flexibility of distribution network. In the actual operation of grid connection, distributed generation must transmit active and reactive power to the distribution network. Because of its intermittent and randomness of power supply, this will change the power flow and flow direction of the distribution network, resulting in the voltage of the distribution network exceeding the limit and the loss of the distribution network increasing. Reduced reliability, etc. Therefore, it is necessary to reduce the influence of distributed generation on distribution network through reactive power optimization in distribution network to ensure the power quality and reliability of distribution network. In this paper, the reactive power optimization problem of distribution network with distributed generation is studied. First, the power flow calculation method and its calculation process of distribution network are described in detail. Secondly, by analyzing and comparing the influence of distributed generation with different quantity, position and capacity on distribution network voltage and network loss, the theoretical analysis and numerical example show that distributed generation can reduce the active power loss of distribution network. Ability to effectively prevent local node voltage overruns in distribution networks. The particle swarm optimization (PSO) algorithm is prone to be trapped in local optimization problems and the ant colony algorithm (ACO) has the disadvantages of long searching time due to accumulation of pheromone. Based on the advantages of simple structure and less parameter setting of particle swarm optimization (PSO), and the powerful global searching ability of ant colony algorithm (ACO), A hybrid optimization algorithm (PSO-ACO) combining particle swarm optimization (PSO) with ant colony algorithm (ACO) is proposed and a mathematical model of reactive power optimization is constructed with penalty function. Compared with the results of reactive power optimization based on basic particle swarm optimization and ant colony algorithm, the simulation results of IEEE-33 node distribution system show that the proposed hybrid algorithm has better convergence and stability. It can effectively solve the problem of reactive power optimization in distribution network with DG.
【学位授予单位】：沈阳农业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM714.3

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