考虑风电预测误差分布特性的机组组合模型与算法

发布时间：2019-06-29 08:25

【摘要】：能源是推动经济社会持续快速发展的关键,但日益短缺的化石能源与不断恶化的生存环境使得传统的能源供应体系面临严峻挑战。因此,风电、太阳能等清洁可再生能源受到持续关注。其中,风力发电因其实现技术趋于成熟、投资回报效果较优,以及政策支持等因素影响而得到大规模的开发利用,已经成为电力系统的主要电源之一。然而,由于风能的随机性,风电在提供清洁能源、减少碳排放的同时,也给电力系统的调度、运行带来了巨大挑战。一方面,风功率的随机波动性难以准确把握。虽然风功率的预测经过长期的研究实践取得了很大的进展,但其预测精度仍难以满足实际工程的需要。因此,有必要在已有预测技术基础上,通过对预测结果的误差特性分析与把握,从而降低风功率随机波动性对机组组合、经济调度等决策行为的影响,实现对风功率预测信息的高效利用。另一方面,随机波动的风电功率与传统的确定性调度方式间难以协调。风电渗透率的不断增大在给系统带来巨大经济、环境效益的同时,系统运行的不确定性同样随之增加,而传统的确定性机组组合、经济调度方式显然难以应对因大规模风电接入而引起的不确定性。由此,如何实现波动性风电与确定性机组组合间的有效衔接,对于提高电力系统运行经济性与安全性具有重要的理论与实际意义。据此,本文首先研究风功率预测误差特征的准确表达方法,继而探讨其在机组组合中的应用,并提出考虑预测误差时序特性的机组组合方法研究,以此改善含风电系统的经济性与安全性。主要工作可以概括如下：(1)研究风功率预测误差的概率分布特征,利用参数与非参数估计方法对预测误差进行拟合分析,包括正态分布,Beta分布,T-location-scale分布,以及非参数核密度估计方法,并进一步对现有的检验概率密度函数拟合效果的评价指标进行改进。利用实际风功率预测误差数据,对比分析了不同拟合方法、不同拟合参数对拟合效果的影响。(2)考虑风电预测误差在不同功率水平与不同时段下的差异性,提出一种功率-时序维度下的误差分段拟合方法,通过对功率、时序两个维度下的同时分段,提高误差拟合精度。针对分区过多问题,提出一种区段缩减方法,解决分组数量与拟合效果间的矛盾,从而提高拟合精度,减少了拟合计算量,使该方法更具实用意义。该研究表明同时考虑了误差功率-时序特征的拟合方法能够更精确的刻画风功率预测误差的分布特征。(3)提出一种可同时考虑预测误差时序分布特性与系统备用分类的机组组合模型。该模型将预测误差的时序特性与机组组合的时序特征相结合,使预测误差的时序特征得以准确把握；同时,该模型按传统成本、额外备用成本与风险成本对不同备用类别进行划分,并通过不同成本间的制约关系自动平衡置信水平的选取,通过时变置信水平适应误差时序分段分布特征,以此提升模型经济性与安全性：最后,利用含启发式搜索原则的混合粒子群算法来求解该模型。
[Abstract]:Energy is the key to the sustainable and rapid development of the economy and society, but the increasing shortage of the fossil energy and the deteriorating living environment make the traditional energy supply system facing a severe challenge. Therefore, clean renewable energy, such as wind power and solar energy, is constantly concerned. The wind power generation has become one of the main power sources of the power system because of its mature technology, better investment return effect and policy support. However, due to the randomness of wind energy, wind power can provide clean energy and reduce carbon emission, and also brings great challenges to the dispatching and operation of the power system. On the one hand, the random fluctuation of wind power is difficult to grasp accurately. Although the prediction of wind power has made great progress through long-term research, its prediction accuracy is still difficult to meet the needs of the actual project. Therefore, it is necessary to analyze and grasp the error characteristic of the prediction result on the basis of the existing prediction technology, so as to reduce the influence of the random fluctuation of the wind power on the decision-making behavior such as the combination of the unit and the economy, and realize the high-efficiency utilization of the wind power prediction information. On the other hand, the wind power of random fluctuation is difficult to coordinate with the traditional deterministic scheduling mode. The continuous increase of the wind power permeability brings great economic and environmental benefits to the system, and the uncertainty of the system operation is also increased, and the traditional deterministic unit combination and the economic dispatching mode obviously have difficulty in coping with the uncertainty caused by large-scale wind power access. Therefore, how to realize the effective connection between the volatility wind power and the deterministic unit combination is of great theoretical and practical significance to improve the operation economy and safety of the power system. In this paper, the accurate expression of the characteristic of the wind power prediction error is first studied, then its application in the unit combination is discussed, and the combination method of the unit considering the timing characteristics of the prediction error is put forward to improve the economy and safety of the wind power system. The main work can be summarized as follows: (1) The probability distribution characteristic of the wind power prediction error is studied, and the prediction error is fitted and analyzed by the parameter and the non-parameter estimation method, including the normal distribution, the Beta distribution, the T-location-scale distribution, and the non-parameter kernel density estimation method. And further improving the evaluation index of the fitting effect of the existing test probability density function. Using the actual wind power to predict the error data, the effects of different fitting methods and different fitting parameters on the fitting effect are compared and analyzed. (2) Considering the difference of the wind power prediction error under different power levels and different time periods, a method for fitting the error segment under the power-time sequence dimension is proposed, and the error fitting precision is improved by simultaneously segmenting the power and the time sequence. In view of the excessive partition problem, a section reduction method is proposed to solve the contradiction between the number of the packets and the fitting effect, so that the fitting precision is improved, and the fitting calculation amount is reduced, so that the method is more practical. The study shows that the method of fitting the error power-time-sequence feature can more accurately describe the distribution characteristics of the wind power prediction error. (3) A combined model of the combination of the prediction error timing distribution and the system standby classification can be considered at the same time. The model combines the timing characteristics of the prediction error with the timing characteristics of the combination of the units, so that the timing characteristics of the prediction error can be accurately grasped; meanwhile, the model is divided according to the traditional cost, the additional standby cost and the risk cost to different standby categories, And the model is solved by using a mixed particle swarm algorithm with a heuristic search principle.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM614

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