新能源电力系统中的分布式光伏净负荷预测

Distributed Photovoltaic Net Load Forecasting in New Energy Power Systems

为响应碳达峰、碳中和的需求,构建一套完整的"源-网-荷-储"的新能源电力系统,提出了一种基于Hamiltonian Monte Carlo推断深度高斯过程(HMCDGP)算法的分布式光伏净负荷预测模型.首先,分别使用直接预测和间接预测两种形式对预测模型的精度进行实验并得到点预测结果;其次,使用所提出的模型进行概率预测实验并得到区间预测结果;最后,通过以澳洲电网记录的300户净负荷数据为基础的对比实验验证所提模型的优越性.在得到准确的净负荷概率预测后,可以通过电力调度充分利用光伏产出,减少化石能源使用,从而减少碳排放.

To respond to the demand of achieving carbon peaking and carbon neutrality goals,and to construct a complete“source-grid-load-storage”new energy power system,a distributed photovoltaic net load forecasting model based on Hamiltonian Monte Carlo inference for deep Gaussian processes(HMCDGP)is proposed.First,direct and indirect forecasting methods are used to examine the accuracy of the proposed model and to obtain spot forecasting results.Then,the proposed model is used to perform probability forecasting experiments and produce interval prediction results.Finally,the superiority of the proposed model is verified through the comparative experiments based on the net load data of 300 households recorded by Australia Grid.After obtaining the exact net load probabilistic forecasting results,the photovoltaic production can be fully utilized via power dispatch,which can reduce the use of fossil energy and further reduce the carbon emission.