Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage  

作　　者：Kaicheng Liu Chen Liang Xiaoyang Dong Liping Liu 

机构地区：[1]China Electric Power Research Institute,Beijing,100192,China [2]Electric Power Research Institute,State Grid Gansu Electric Power Company,Lanzhou,730000,China [3]State Key Laboratory of Power Grid Safety,Beijing,100084,China

出　　处：《Energy Engineering》2024年第4期933-949,共17页能源工程（英文）

基　　金：The Science and Technology Project of the State Grid Corporation of China(Research and Demonstration of Loss Reduction Technology Based on Reactive Power Potential Exploration and Excitation of Distributed Photovoltaic-Energy Storage Converters:5400-202333241 A-1-1-ZN).

摘　　要：Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

关 键 词：Photovoltaic power generation spatio-temporal prediction temporal convolutional network long short-term memory network 

分 类 号：TM615[电气工程—电力系统及自动化]