Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties

被引:60
作者
Dong, Wei [1 ,2 ]
Yang, Qiang [1 ,2 ]
Fang, Xinli [3 ]
Ruan, Wei [4 ]
机构
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
[2] Zhejiang Lab, Hangzhou 310000, Peoples R China
[3] POWERCHINA HUADONG Engn Corp Ltd, Hangzhou 311122, Peoples R China
[4] Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China
关键词
Microgrid energy management system; Fuzzy inference system; Meta-heuristic algorithm; Operational uncertainty; ELECTRIC VEHICLES; RENEWABLE ENERGY; SYSTEM; OPTIMIZATION; MODEL; ALGORITHM; DISPATCH; STORAGE; DEMAND;
D O I
10.1016/j.asoc.2020.106882
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The intelligent decision making of multi-energy management in a microgrid is a non-trivial task due to the intermittent and stochastic nature of highly penetrated renewable energy sources and demand. To address such a challenge, the energy management system often adopts the prediction based day-ahead energy scheduling and real-time energy dispatch to optimally coordinate the operation of dispatchable components, e.g., battery-based energy storage and thermal units. This paper presents an adaptive optimal fuzzy logic based energy management solution to develop appropriate day-ahead fuzzy rules for real-time energy dispatch adaptively in the presence of operational uncertainties. The solution determines the optimal fuzzy inference system (e.g., the membership function shape and the inference rules set) based on the predicted information over a certain period through a novel offline meta-heuristic optimization algorithm. The real-time energy dispatch based on the obtained optimal fuzzy logic rules can be further carried out to meet the various operational criteria, e.g., minimal power fluctuation and operational cost. The proposed solution is extensively evaluated through simulation experiments in comparison with two existing approaches: the online rule-based dispatch method and the meta-heuristic optimization-based offline scheduling method. The numerical results demonstrate the superior performance of the proposed energy management solution. (C) 2020 Elsevier B.V. All rights reserved.
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页数:14
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