电力系统计及网损的经典法有功负荷经济分配研究

发布时间：2018-04-17 13:24

本文选题：电力系统 + 有功负荷经济分配　；参考：《南昌大学》2016年硕士论文

【摘要】：电力系统在近几十年来飞速发展,各地区电网相互联同,电网规模日益扩大。电网负荷变化波动较大、负荷峰谷差值较大等问题都对电力系统安全经济运行提出了更高的要求。本篇论文主要结合稀疏矩阵技术利用经典法对电力系统有功优化中负荷经济分配问题展开研究,进行了理论分析、通过计算机编程来用算例对本算法进行验证。文章系统的对网络潮流计算进行了推导分析,基于稀疏矩阵技术对节点导纳矩阵提出了新的存贮方式、对求解阻抗矩阵的过程以及对牛顿法潮流中雅克比矩阵形成过程提出了新改进。在计算时间以及储存空间上得到了极大的优化,并且基于稀疏矩阵技术对牛顿拉夫逊和直流潮流算法分别提出了简化快速求解方法并依次进行验证。文章的最后建立了计及网损的有功负荷经济分配研究模型,考虑系统网损并对现有的几种有功网损微增率的算法进行推导改进和计算比较,并将改进后的算法应用到电力系统负荷经济分配中。文章的最后就是通过对IEEE-5节点、IEEE-14节点算例对改进经典法进行了验证和比较。
[Abstract]:With the rapid development of power system in recent decades, the scale of power grid is expanding day by day.Many problems such as large fluctuation of load and large difference between peak and valley put forward higher requirements for the safe and economical operation of power system.This paper mainly uses sparse matrix technology to study the load economic distribution problem in power system active power optimization using classical method, and carries out theoretical analysis, and verifies this algorithm by computer programming examples.In this paper, the network power flow calculation is deduced and analyzed systematically, and a new storage method of node admittance matrix based on sparse matrix technique is proposed.The process of solving impedance matrix and the forming process of Jacobian matrix in Newton power flow are proposed.The computation time and storage space are optimized greatly. Based on sparse matrix technique, a simplified and fast algorithm for Newton Raphson and DC power flow is proposed and verified in turn.At the end of the paper, the economic distribution model of active power load considering network loss is established. Considering the system network loss, several existing algorithms are deduced and improved, and the calculation results are compared.The improved algorithm is applied to load economic distribution of power system.At the end of the paper, the improved classical method is verified and compared with the IEEE-5 node IEEE-14 node.
【学位授予单位】：南昌大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM732;TM744

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