摘要
随着一次能源的逐近枯竭和社会对环保的要求日益增强,风能等新能源产业发展迅猛,风电在电网中的比例不断增加。然而风力发电具有随机波动性和不可控性的特点,大规模风电并网将给电力系统的安全运行及电能质量带来严峻的挑战。风电功率预测技术作为有效应对风电接入的关键技术之一,对指导系统调度运行、风电场生产安排具有十分重大的意义,在计及风电并网的现代电网运行过程中发挥着越来越大的作用。
本文结合风电功率预测实际应用需求,对短期风电功率预测技术进行了深入的研究,主要工作如下:
1、基于传统时间序列的自回归滑动平均ARMA模型,提出风电功率预测的ARMAX-GARCH模型,该模型能够同时考虑多变量因素的影响,并利用误差修正预测结果。应用该方法对某风场实际数据进行功率预测,验证了该模型能有效提高预测精度。
2、研究风电功率预测的BP神经网络模型,引入小波分解技术解决风电功率数据波动规律性差的问题,提出基于小波分解和BP神经网络的风电功率预测模型。同时,为克服BP网络易陷入局部极小、隐层节点数的选取缺乏指导等缺点,对BP算法进行了改进。经算例验证,该方法能有效提高预测精度。
3、研究风电功率预测的不确定性预测方法。分析基于前一时刻风速的风电功率条件概率密度分布的统计规律,基于该条件概率密度拟合分布估算不同置信水平下的置信区间,并应用实际算例验证了该方法的有效性。
4、研究风电功率预测的连续多步预测方法,基于模糊聚类分析技术实现对未来4小时每间隔15min的超短期预测和未来24小时每间隔1小时的短期预测,解决在缺乏数值天气预报数据情况下的连续多步预测难以实现的困难。
Along with the exhaustion of primary energy and the growing need of environmental protection, new energy such as wind power is developing rapidly. And the proportion of wind power in the grid is continuously increasing. However, because the wind power is intermittent and uncontrollable, large-scale wind power integrated into power system will bring severe challenges of power system safety operation and power quality. Wind power forecasting technology is one of the key technologies in coping with the problems. As it can guide the dispatching of grid and the production planning of wind farm effectively, it is playing a more and more important role when wind power integrated into power system.
This paper studies short-term wind power forecasting technology based on the demands that the practical application call for. The main work is as following:
1. Based on the traditional time series ARMA model, an improved prediction model ARMAX-GARCH is proposed. This method considers several influencing factors and corrects prediction results with prediction errors. Making an example and the result shows that the model can improve the prediction accuracy effectively.
2. Study a model of BP neural network, a short-term forecasting for wind power based on wavelet decomposition and BP neural network is proposed. By use of wavelet decomposition, the problem of poor data fluctuation regularity is solved. Meanwhile, in order to overcome the shortcomings which BP network is easy to fall into minimum and the selection of hidden layers nodes lack of guidance, the BP algorithm has been improved. Verified with a example and the result shows that the proposed method can improve the prediction accuracy effectively.
3. A method of condition probability density distribution is used to build the uncertainty prediction model for wind power prediction. This model can be used for estimating confidence interval under different confidence levels. The effectiveness of the method is validated by practical examples.
4. Based on fuzzy clustering analysis theory, a method of continuous multi-step prediction model is proposed. By using this method, the predictions of next four hours can be achieved in the form of every 15 minutes. Also the predictions of next 24 hours with the form of one hour can be achieved. This method solves the problem that it difficult to do continuous prediction without the data of NWP.
引文
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