A hybrid deep learning-based online energy management scheme for industrial microgrid

被引：31

作者：

Lu, Renzhi ^{[1
,2
]}

Bai, Ruichang ^{[1
]}

Ding, Yuemin ^{[3
]}

Wei, Min ^{[2
,4
]}

Jiang, Junhui ^{[5
]}

Sun, Mingyang ^{[6
]}

Xiao, Feng ^{[7
]}

Zhang, Hai-Tao ^{[1
,8
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China

[2] Minist Educ, Key Lab Ind Internet Things & Networked Control, Chongqing 400065, Peoples R China

[3] Norwegian Univ Sci & Technol, Dept Energy & Proc Engn, N-7034 Trondheim, Norway

[4] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China

[5] Hanyang Univ, Dept Elect Syst Engn, Ansan 15588, South Korea

[6] Zhejiang Univ, Dept Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China

[7] North China Elect Power Univ, Sch Control & Comp Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China

[8] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China

来源：

APPLIED ENERGY | 2021年 / 304卷 / 304期

基金：

中国国家自然科学基金;

关键词：

Online energy management; Demand response; Industrial microgrid; Deep learning; Convolutional neural network; Long short-term memory; DEMAND RESPONSE; FRAMEWORK; LOADS; PRICE; POWER; DISPATCH; MODEL;

D O I：

10.1016/j.apenergy.2021.117857

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The fluctuations in electricity prices and intermittency of renewable energy systems necessitate the adoption of online energy management schemes in industrial microgrids. However, it is challenging to design effective and optimal online rolling horizon energy management strategies that can deliver assured optimality, subject to the uncertainties of volatile electricity prices and stochastic renewable resources. This paper presents an adaptable online energy management scheme for industrial microgrids that minimizes electricity costs while meeting production requirements by repeatedly solving an optimization problem over a moving control window, taking advantage of forecasted future prices and renewable energy profiles implemented by a hybrid deep learning model. The predicted values over the control horizon are assumed to be uncertain, and a multivariate Gaussian distribution is used to handle the variations in electricity prices and renewable resources around their predicted nominal values. Simulation results under different scenarios using real-world data verify the effectiveness of the proposed online energy management scheme, assessed by the corresponding gaps with respect to several selected benchmark strategies and the ideal boundaries of the best and worst known solutions. Furthermore, the robustness of the scheme is verified by considering severe errors in forecasted electricity prices and renewable profiles.

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