Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

作     者：Yunlei Zhang RuifengCao Danhuang Dong Sha Peng RuoyunDu Xiaomin Xu 

作者机构：State Grid Zhejiang Electric Power Co.Ltd.Hangzhou310007China Strategy and Development Research CenterEconomic and Technical Research InstituteState Grid Zhejiang Electric Power Co.Hangzhou310000China Beijing Key Laboratory of New Energy Power and Low Carbon Development ResearchNorth China University of Electric PowerBeijing102206China 

出 版 物：《Energy Engineering》 (能源工程（英文）)

年 卷 期：2022年第119卷第5期

页      面：1829-1841页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 0202[经济学-应用经济学] 02[经济学] 1002[医学-临床医学] 020205[经济学-产业经济学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by a State Grid Zhejiang Electric Power Co.,Ltd.Economic and Technical Research Institute Project(Key Technologies and Empirical Research of Diversified Integrated Operation of User-Side Energy Storage in Power Market Environment,No.5211JY19000W) supported by the National Natural Science Foundation of China(Research on Power Market Management to Promote Large-Scale New Energy Consumption,No.71804045) 

主　　题：Energy storage scheduling short-term load forecasting deep learning network convolutional neural network CNN long and short term memory network LTSM 

摘      要：In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many *** studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction *** the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction *** experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.