中圖分類號：TM93 文獻標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.245284 中文引用格式： 翟哲,，余杰文，杜洋,，等. 計及可再生能源接入配電網(wǎng)的負(fù)荷預(yù)測和優(yōu)化[J]. 電子技術(shù)應(yīng)用,，2024，50(11)：35-41. 英文引用格式： Zhai Zhe,，Yu Jiewen,，Du Yang，et al. Load prediction and optimization of renewable energy access to the distribution network[J]. Application of Electronic Technique,，2024,，50(11)：35-41.
Load prediction and optimization of renewable energy access to the distribution network
Zhai Zhe1,，Yu Jiewen2，Du Yang3,，Cao Zejiang4
1.Dispatching and Control Center,， China Southern Power Grid； 2.China Southern Power Grid Artificial Intelligence Technology Co.,， Ltd.,； 3.Shenzhen Faben Information Technology Co.， Ltd.,； 4.China Southern Power Grid Digital Power Grid Technology （Guangdong） Co.,， Ltd.
Abstract： Currently, with the large-scale integration of renewable energy into the distribution network, the intermittency and randomness of renewable energy sources such as solar and wind power inevitably cause fluctuations in the distribution network. Considering the characteristics of renewable energy generation power and electricity load in the power grid over time, a load prediction and optimization method based on wavelet transform and neural network for renewable energy access to the distribution network 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 parameters 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.
Key words : cloud technology；neural network,；wavelet transform,；wind and solar power generation；load prediction,；power generation optimization
