含风电并网的复合型能源电力系统的优化调度

发布时间：2018-06-27 22:28

本文选题：风电预测 + 时间序列　；参考：《昆明理工大学》2017年硕士论文

【摘要】：风力发电作为一种清洁能源,在全世界范围内发展迅速。虽然风电具有一定的优势,主要表现在降低污染物排放和发电成本等方面,但是随之而来的是大规模风电并网会给电力系统产生一定的影响。风电的最显著特征是具有较大的随机性,大规模的风电并网将对电网的安全运行带来挑战,并对系统调度计划的安排产生一定不良的影响。可是在最初进行电网规划的时候,是没有将风电并网考虑在内的。因此,本文主要对两方面的内容进行研究:提高风电的预测精度和含清洁可再生能源的复合型能源的电力系统的优化调度。首先使用单一的BP神经网络进行预测,预测精度不高。为了提高预测精度,使用遗传算法对BP神经网络有关参数进行优化,预测精度较单一BP神经网络有所提高;随后又建立时间序列模型,优化网络的输入变量。以我国某风电场的某台机组为例,分别使用三种方法对风机出力进行直接和间接预测。仿真结果表明,这两种方法提高了单一 BP神经网络的预测精度,适合风电场的短期预测。在提高风力发电预测精度的基础上,对风-火-水复合型能源电力系统建立多目标调度模型并进行求解。对于多目标、多约束的模型,利用传统的线性或动态规划求解方法失效。本文采用粒子群智能算法寻优。针对基本的粒子群算法易于陷入局部最优,本章在增加扰动的LDW的基础上,提出动态自适应的改变惯性权重系数和较小概率的引入随机个体的改进粒子群算法。仿真分析结果验证了算法的优越性能。在稳定的24节点系统中配以少量风电机组和水电机组来建立优化调度模型,以期为大的新能源并网的电力系统提供理论性和应用型的依据。在传统的经济调度问题的基础上,加入了火电机组不频繁改变出力作为目标函数,保证火电机组运行的经济性。以水电站的发电用水量不同,将调度日分为三种情形,按水电站的汛期、平水期和枯水期三个时期的发电特点进行调度,并利用本文基于动态自适应的改变惯性权重系数和较小概率的引入随机粒子的改进PSO算法进行求解。仿真结果表明:水电站在风-火-水复合型能源电力系统中发挥着调节作用,水电站的发电可用水量的多少决定着水电站在复合型能源电力系统中的调节能力的强弱。
[Abstract]:As a clean energy, wind power generation is developing rapidly all over the world. Although wind power has some advantages, mainly in reducing pollutant emissions and generation costs, but with the large-scale wind power grid will have a certain impact on the power system. The most prominent feature of wind power is that it has a large randomness. The large-scale wind power grid connection will bring challenges to the safe operation of the power grid and have a certain negative impact on the scheduling of the system. However, in the initial planning of the grid, wind power was not taken into account. Therefore, this paper mainly focuses on two aspects: improving the prediction accuracy of wind power and optimal dispatching of power system with clean and renewable energy. First, a single BP neural network is used for prediction, and the prediction accuracy is not high. In order to improve the prediction accuracy, genetic algorithm is used to optimize the parameters of BP neural network, and the prediction accuracy is improved compared with the single BP neural network, and then the time series model is established to optimize the input variables of the network. Taking one unit of a wind farm in China as an example, three methods are used to predict the fan output directly and indirectly. The simulation results show that these two methods can improve the prediction accuracy of single BP neural network and are suitable for short-term wind farm prediction. On the basis of improving the prediction accuracy of wind power generation, the multi-objective dispatching model of wind-fire water complex energy power system is established and solved. For multi-objective and multi-constrained models, traditional linear or dynamic programming methods are used to solve the problems. In this paper, particle swarm optimization (PSO) algorithm is used. The basic particle swarm optimization (PSO) algorithm is easy to fall into local optimum. In this chapter, an improved particle swarm optimization algorithm is proposed based on increasing the disturbance of LDW, which can change the inertia weight coefficient and the probability of random individuals. The simulation results verify the superior performance of the algorithm. In a stable 24-bus system, a few wind turbines and hydropower units are used to establish the optimal dispatching model, which can provide theoretical and applied basis for the power system with large new energy connected to the grid. On the basis of the traditional economic scheduling problem, the thermal power unit is added as the objective function of infrequently changing the output force to ensure the economic operation of the thermal power unit. According to the different water consumption of hydropower stations, the dispatching days are divided into three cases, and the dispatching is carried out according to the characteristics of the hydropower stations in the flood season, the average water period and the low water period. An improved PSO algorithm based on dynamic adaptive variable inertial weight coefficient and small probability is used to solve the problem. The simulation results show that the hydropower station plays a regulating role in the wind-fire water complex energy power system, and the amount of water available to generate electricity in the hydropower station determines the regulating capacity of the hydropower station in the composite energy power system.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73

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