电力系统动态经济调度的改进文化粒子群算法

发布时间：2018-05-25 10:43

本文选题：风电 + 动态经济调度　；参考：《南昌航空大学》2016年硕士论文

【摘要】：电力系统的动态经济调度问题(Dynamic economic dispatch,DED)是电力系统经济调度中的重要研究课题,它的非凸、非线性、高维以及多约束使得优化起来很艰难。这些年煤炭等资源存量越来越低,各个国家纷纷重视了新型能源的开发和利用,风电首当其冲发展迅猛。如今,对于含风力发电的电力系统动态经济调度问题的研究各个国家才刚刚起步。风能是一种无污染的可再生绿色能源,能量来源丰富,取之不尽用之不竭,但同时具有很强的随机性,不能调度性和波动性,与发电机组并网的动态经济负荷调度问题很难用传统的数学方法来求解。在这种的情况的背景下,DED不再是仅考虑火力发电的燃煤成本最小化为单一目标,如今在实时控制中并且还要系统安全的前提下充分利用风力发电,减少环境污染等等的经济问题。本文的主要研究内容如下:1)在已有的粒子群算法,文化算法和文化粒子群算法等众多研究成果的基础上,进一步探讨文化算法的信念空间进化机制,探讨粒子群算法惯性权重的动态变化,提出一种改进的基于动态改变惯性权重双重更新的并行文化粒子群算法。通过测试一些带有复杂约束条件的函数,并与其他几种算法做比较来验证此改进算法对于处理非凸非线性多约束的函数的可行性和有效性。2)从最初始传统的电力系统模型开始分析,然后引进风电场并且进一步分析研究风电并网对电力系统的经济调度的影响,根据以往人们的研究成果上,建立了了基于阀点效应,正负旋转备用,风速预测误差以及负荷预测误差等约束的风电并网的动态经济调度模型。3)最后本文提出的改进算法对含风电并网的动态经济负荷分配调度问题上进行优化求解验证了此模型的可行性以及改进算法的有效性,并与一些参考文献的算法应用于此模型进行求解对比进一步验证此算法的优越性。
[Abstract]:Dynamic economic dispatch is an important research topic in power system economic dispatch. Its non-convex, nonlinear, high-dimensional and multi-constraint make optimization difficult. In recent years, coal and other resources have become lower and lower. Many countries have attached importance to the development and utilization of new energy sources, and wind power is the first to bear the brunt of the rapid development. Nowadays, the research on dynamic economic dispatch of power system with wind power generation is just beginning. Wind energy is a kind of non-pollution renewable green energy, which is rich in energy sources, inexhaustible, but also has a strong randomness, non-scheduling and volatility. It is difficult to solve the dynamic economic load scheduling problem with the grid connection of generating sets by traditional mathematical methods. In this context, DED is no longer just considering the cost of coal-fired power generation to be minimized as a single objective, but now makes full use of wind power in real-time control and system safety. Reduce environmental pollution and other economic problems. The main contents of this paper are as follows: (1) on the basis of the existing research achievements, such as particle swarm optimization, cultural algorithm and cultural particle swarm optimization, we further explore the belief space evolution mechanism of cultural algorithm. This paper discusses the dynamic change of inertia weight in particle swarm optimization algorithm, and proposes an improved parallel cultural particle swarm optimization algorithm based on the double update of dynamic change inertia weight. By testing some functions with complex constraints, And compared with other algorithms to verify the feasibility and effectiveness of the improved algorithm to deal with non-convex nonlinear multi-constraint functions. 2) starting from the original traditional power system model analysis. Then the wind farm is introduced and the influence of wind power grid connection on the economic dispatch of power system is further analyzed. Based on the previous research results, the valve point effect, positive or negative rotation reserve is established. Wind speed forecasting error and load forecasting error etc. Dynamic economic dispatching model of wind power grid. 3) finally, the improved algorithm proposed in this paper is used to solve the dynamic economic load distribution problem with wind power grid connection. The feasibility of the model and the effectiveness of the improved algorithm are verified. The advantages of this algorithm are further verified by comparison with some reference algorithms applied to this model.
【学位授予单位】：南昌航空大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM73;TP18

【相似文献】