计及可再生能源接入配电网的负荷预测和优化

Load prediction and optimization of renewable energy access to the distribution network

目前,可再生能源大量接入配电网,但是太阳能、风能、光伏及风电等可再生能源的间歇性和随机性不可避免地会造成配电网的波动。考虑电网内可再生能源发电功率与用电负荷随时间变化的特点,提出一种基于小波变换和神经网络的可再生能源接入配电网的负荷预测和优化方法。首先采集配电网的发电与负荷数据,利用小波变换处理收集到的数据,得到局部尺度和频率分解的特征参数,建立神经网络预测模型;然后,对经过小波变换后得到的特征参数进行训练,根据预测负荷对可再生能源的发电量进行调节,保持配电网供需侧的动态平衡。结果表明,所提方法能够对负荷进行有效预测,通过提前预测负荷量,保证配电网用电稳定性的同时,最大化利用可再生能源。

Currently,with the large-scale integration of renewable energy into the distribution network,the intermittency and ran‐domness of renewable energy sources such as solar and wind power inevitably cause fluctuations in the distribution network.Con‐sidering the characteristics of renewable energy generation power and electricity load in the power grid over time,a load predic‐tion and optimization method based on wavelet transform and neural network for renewable energy access to the distribution net‐work is proposed.Firstly,the grid operation data are collected,and the wavelet transform is used to process the collected data to obtain the feature parameters of local scale and frequency decomposition.A neural network is established.Then the feature param‐eters obtained after the wavelet transform are trained to obtain a model capable of predicting the load,according to which the power generation of renewable energy sources can be adjusted in time to maintain the dynamic balance between the supply and demand sides of the distribution network.The results show that the proposed method can effectively predict the load and regulate the power generation by observing the load in advance to ensure the stability of power consumption in the distribution network and simultaneously maximize the use of renewable energy.