计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法；对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。

Short-term load forecasting of power system considering photovoltaic and wind power grid connection

To improve the prediction accuracy of short-term power load after a large number of distributed energy resources such as photovoltaic and wind power are connected to the power grid, and to promote the improvement of the absorption capacity of the power grid, this paper adopts the wavelet-radial basis function(RBF) neural network for photovoltaic output and short-term power load, and uses BPNN, RBFNN, WNN and ELMANNN neural networks to predict the power data after the overall average empirical mode decomposition(EEMD) are used for wind power, and uses particle swarm algorithm and grey correlation degree to correct the wind power prediction data. Finally, the equivalent load concept is used to analyze the influence of photovoltaic and wind power integration on load forecasting, and the three models are effectively combined to obtain the equivalent load model of short-term load prediction of power system considering photovoltaic and wind power generation. Case analysis shows that compared with other methods, the proposed method has higher prediction accuracy in short-term load prediction of power system considering photovoltaic and wind power integration.