LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers  

作　　者：Huayanran Zhou Yihong Zhou Junjie Hu Guangya Yang Dongliang Xie Yusheng Xue Lars Nordström 

机构地区：[1]State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China [2]Center for Electric Power and Energy,Technical University of Denmark,Lyngby,Denmark [3]State Grid Electric Power Research Institute,Nanjing,China [4]Division of Electric Power and Energy Systems,School of Electrical Engineering and Computer Science,KTH Royal Institute of Technology,Stockholm,Sweden

出　　处：《Journal of Modern Power Systems and Clean Energy》2021年第5期1205-1216,共12页现代电力系统与清洁能源学报（英文）

基　　金：This work was supported by the National Natural Science Foundation of China(No.51877078);the State Key Laboratory of Smart Grid Protection and Operation Control Open Project(No.SGNR0000KJJS1907535);the Beijing Nova Program(No.Z201100006820106)。

摘　　要：As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

关 键 词：Building energy management system(BEMS) electric vehicle(EV) long short-term memory(LSTM) recurrent neural network machine learning prosumer 

分 类 号：TM73[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]