含光伏电站的66kV配电网广义负荷模型研究

发布时间：2018-02-15 07:27

  本文关键词： 广义负荷模型 光伏电站 电力参数寻优 电力系统仿真　出处：《沈阳农业大学》2017年硕士论文　论文类型：学位论文

【摘要】：电力工业发展是保障国民经济稳定运行、居民生活水平提高的重要保障。为了应对气候变化和能源危机,包含光伏发电在内的可再生能源有效利用已经成为国家战略,光伏电站接入电网成为有效措施。作为可再生能源供电的典型代表,光伏发电呈现强劲的发展势头。如何有效利用光伏发电成为目前急需解决的问题。本研究针对辽宁西部地区地区66kV配电网在光伏电站接入时广义负荷模型展开研究,目的是提出典型供电地区适用的广义负荷模型,可为电力系统仿真软件的开发和完善、定量分析光伏电站接入对电网的影响、提高光伏发电利用率提供参考。根据有效的广义负荷模型,找出解决相应问题的方法及策略,这样可以提高电力系统自动化程度、有效提升光伏电站和主电网互利共生关系、主动有效地提升电力能源市场改革交易等各个方面具有重要的理论意义和实用价值。广义负荷模型综合考虑了 66kV配电网的常规电源、光伏电站、无功补偿装置等元件的动态变化和有功、无功的相互影响特性。本研究是针对辽宁西部地区电网进行负荷建模研究。除了经典的静态负荷模型和电动机负荷模型以外,还着重研究光伏电站电源潮流电压特性、动态特性及其相互影响。由于线路电抗电阻参数的不同,系统潮流与电压的关系也存在着差异。选择典型66kV输电线路系统展开研究,相对易于获取实测数据。本研究围绕电网负荷模型及其参数自动辨识部分进行研究和软件开发。设计了含光伏电站的66kV配网广义负荷模型及其电力参数寻优的方法和具体实现过程。通过对66kV线路有功负荷数据方程的电力参数寻优,计算得到通用非线性线路功率与两端电压的关系模型的三个寻优常数。为电网负荷模型及其参数自动辨识提供解决思路,在电能发输配用系统规划和仿真分析中提供技术支持和相对完善的仿真平台。
[Abstract]:The development of electric power industry is an important guarantee to ensure the stable operation of national economy and the improvement of living standard. In order to cope with climate change and energy crisis, the effective use of renewable energy, including photovoltaic power generation, has become a national strategy. As a typical representative of renewable energy supply, photovoltaic power station access to the grid becomes an effective measure. Photovoltaic power generation shows a strong development momentum. How to effectively utilize photovoltaic power generation has become an urgent problem. This study focuses on the generalized load model of 66 kV distribution network in western Liaoning province when the photovoltaic power station is connected. The purpose of this paper is to propose a generalized load model for typical power supply areas, which can be used for the development and improvement of power system simulation software and the quantitative analysis of the impact of PV power station access on the power network. According to the effective generalized load model, the methods and strategies to solve the corresponding problems can be found, which can improve the degree of power system automation and effectively enhance the symbiotic relationship between photovoltaic power station and main grid. It is of great theoretical significance and practical value to actively and effectively promote the reform and transaction of power energy market. The generalized load model takes into account the conventional power supply and photovoltaic power station in 66kV distribution network. The dynamic changes of components such as reactive power compensator and the interaction between active and reactive power are studied in this paper. This study is aimed at the research of load modeling in western Liaoning power network. In addition to the classical static load model and motor load model, the dynamic change of reactive power compensator and the interaction between reactive power and reactive power are studied in this paper. The characteristics of power flow voltage, dynamic characteristics and mutual influence of photovoltaic power supply are also studied. Because of the difference of reactance parameters, the relationship between power flow and voltage is also different. A typical 66 kV transmission line system is selected to study the relationship between power flow and voltage. It is relatively easy to obtain the measured data. This study focuses on the research and software development of load model and parameter automatic identification of power network. The generalized load model of 66kV distribution network with photovoltaic power station and its power parameter optimization method are designed. By optimizing the power parameters of the active load data equation of 66kV transmission line, Three optimization constants of the general nonlinear line power and voltage relationship model are obtained, which provide a solution to the load model and its parameter automatic identification. It provides technical support and relatively perfect simulation platform in the system planning and simulation analysis of power generation, transmission and distribution.
【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】：TM615;TM743
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本文编号：1512746