面向分布式能源能量交互画像的虚拟电厂信息流量预测方法

Prediction Method for Information Flow in Virtual Power Plants Oriented Towards Distributed Energy Resources Interaction Profiles

虚拟电厂的信息流能够表示分布式资源与电力系统交互的活跃程度,信息流的精准预测对了解虚拟电厂的运行特性进而提高系统的控制效率至关重要。然而,虚拟电厂涉及的交互主体较多,其能量特性既受资源个体随机性的影响,又与海量资源聚合后的整体特性息息相关。因此传统的预测模型难以有效拟合虚拟电厂的信息流,亟需提出一种面向虚拟电厂信息流预测的模型。文章提出一种基于长短期记忆网络和变分模态分解的短期混合预测模型。首先采用变分模式分解基于变分原理提取流量序列的本征模态分量;然后,利用长短期记忆网络分别对每个模态分量序列进行建模和预测,并创新性地引入注意力机制来筛选本征模态分量中的重要特征序列;最后,所有子预测算法被整合为一个完整的预测模型。仿真结果表明,相比传统方法,该模型可以有效地提高虚拟电厂信息流量预测的准确性。

The information flow of virtual power plants(VPPs)can accurately represent the level of interaction between distributed energy resources(DERs)and the power system,and precise prediction of this information flow is crucial for understanding the operational characteristics of VPPs and improving the efficiency of system control.However,VPPs involve multiple interacting entities,and their energy characteristics are influenced by both the stochasticity of individual resources and the aggregate characteristics of a large number of resources.Therefore,traditional prediction models struggle to effectively fit the information flow of VPPs,necessitating the development of a model specifically tailored for predicting VPP information flow.This paper proposes a short-term hybrid prediction model based on long short-term memory(LSTM)and variational mode decomposition(VMD).The model first utilizes VMD to extract intrinsic mode components of the flow sequence based on the variational principle.Then,LSTM is employed to model and predict each mode component sequence separately,and an attention mechanism is innovatively introduced to select important feature sequences from the intrinsic mode components.Finally,all sub-prediction algorithms are integrated into a complete prediction model.Simulation results demonstrate that compared to traditional methods,this model can effectively improve the accuracy of VPP information flow prediction.