含风电场的电力系统多目标无功优化

发布时间：2018-04-13 03:10

本文选题：多目标无功优化 + 风电场　；参考：《中国矿业大学》2017年硕士论文

【摘要】：随着经济的发展,我国的能源安全和环境问题已不容忽视,风能作为一种可再生、无污染的绿色能源,给电力行业的发展带了无限前景。根据国家能源局发布的《风电发展“十三五”规划》,“十三五”期间风电投资规模将达7000亿以上。由于风能的随机性、波动性和间歇性,风电的大规模开发和利用必将对电力系统的潮流产生较大影响。因此,分析含风电场的电力系统潮流计算方法,研究电力系统多目标无功优化算法,确定大容量风场接入系统的无功优化策略,对于提高风能利用率、改善电压质量以及保证系统运行稳定性都具有重要的意义。本文在研究了含风电场电力系统潮流计算方法的基础上,采用PSO-NSGA-II算法对含风电场的电力系统进行多目标无功优化。首先,研究了基于牛顿-拉夫逊法和QV联合迭代模型的含风电场电力系统潮流计算方法。本文探讨了电力系统潮流计算几种常用方法的迭代思想,在建立了异步风力发电机组的数学模型和输出功率模型之后,着重分析了风电场并网节点的处理方法。其次,采用基于Pareto的多目标优化方法,同时考虑有功网损、节点电压偏移量和静态电压稳定裕度多个目标函数,研究了电力系统多目标无功优化方法。无功优化问题属于多目标的非线性规划问题,基于Pareto的优化方法能够综合考虑各个目标的情况,求得更合适的优化方案。再次,结合控制变量既有离散变量又有连续变量的特点,针对带精英策略的非支配排序遗传算法在离散空间收敛快的优点,引入在连续空间收敛快的粒子群算法思想,提出了更适用于无功优化问题的PSO-NSGA-II算法,并利用约束违反度的概念求解有约束优化问题。最后,通过接入风电场的IEEE-14节点系统算例仿真,验证风电场潮流计算方法的正确性和PSO-NSGA-II算法的有效性。通过风电场并网节点的模型对比,验证所选QV联合迭代模型能够兼顾计算速度和准确性。通过与NSGA-II算法进行评价指标的对比,证明PSO-NSGA-II算法能够有效地处理多目标优化问题,且具有更好的全局收敛性能。
[Abstract]:With the development of economy, the problems of energy security and environment in our country can not be ignored. Wind energy, as a kind of renewable and pollution-free green energy, brings an infinite prospect to the development of electric power industry.According to the 13th Five-Year Plan of Wind Power Development issued by the State Energy Administration, the scale of wind power investment will reach more than 700 billion during the 13th Five-Year Plan period.Due to the randomness, volatility and intermittency of wind energy, the large-scale development and utilization of wind power will have a great impact on the power flow of power system.Therefore, the power flow calculation method with wind farm is analyzed, the multi-objective reactive power optimization algorithm is studied, and the reactive power optimization strategy for large capacity wind field access system is determined, which can improve the utilization rate of wind energy.It is very important to improve the voltage quality and ensure the stability of the system.Based on the study of the power flow calculation method of the power system with wind farm, the PSO-NSGA-II algorithm is used to optimize the reactive power of the power system with wind farm.Firstly, the power flow calculation method of power system with wind farm is studied based on Newton-Raphson method and QV combined iteration model.In this paper, the iterative idea of several common methods for power flow calculation is discussed. After the mathematical model and output power model of asynchronous wind turbine are established, the processing method of connected nodes of wind farm is analyzed emphatically.Secondly, the multi-objective reactive power optimization method based on Pareto is studied by taking into account the active power network loss, node voltage offset and static voltage stability margin.Reactive power optimization is a multi-objective nonlinear programming problem. The optimization method based on Pareto can comprehensively consider the situation of each objective and obtain a more suitable optimization scheme.Thirdly, considering the characteristics that control variables have both discrete variables and continuous variables, aiming at the advantage of fast convergence in discrete space of non-dominated sorting genetic algorithm with elite strategy, the idea of particle swarm optimization (PSO) with fast convergence in continuous space is introduced.A new PSO-NSGA-II algorithm for reactive power optimization is proposed, and the concept of constraint violation is used to solve the constrained optimization problem.Finally, the correctness of the wind farm power flow calculation method and the validity of the PSO-NSGA-II algorithm are verified by the simulation of the IEEE-14 node system connected with the wind farm.By comparing the models of grid connected nodes of wind farm, it is verified that the QV joint iterative model can take into account the calculation speed and accuracy.By comparing with the evaluation index of NSGA-II algorithm, it is proved that the PSO-NSGA-II algorithm can deal with the multi-objective optimization problem effectively and has better global convergence performance.
【学位授予单位】：中国矿业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614;TM714.3

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