Decision tree-based optimization for flexibility management for sustainable energy microgrids

被引:33
作者
Huo, Yuchong [1 ,2 ]
Bouffard, Francois [1 ,2 ]
Joos, Geza [1 ]
机构
[1] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ H3A 0E9, Canada
[2] Grp Etud & Rech Anal Decis GERAD, Montreal, PQ H3T 1J4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Decision tree; Economic dispatch; Flexibility; Machine learning; Microgrid; Microgrid controller; SYSTEM;
D O I
10.1016/j.apenergy.2021.116772
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
In this paper, we apply a flexibility based operational planning paradigm to microgrid energy dispatch. The classic energy dispatch problem with energy storage and dispatchable thermal generation assets requires the solution of mixed-integer optimization problems. Such approaches are not amenable to most remote microgrids and practical field microgrid implementations, where controls are rule-based and typically implemented by programmable logic controllers. Albeit such rule-based dispatch controls are always feasible, they cannot optimize fully over the availability of renewable generation and asset capacities of microgrids, especially energy storage. In this paper we propose a systematic method to generate the microgrid dispatch rule base with the objective of matching as much as possible the control performance obtained by full mixed-integer optimization. To achieve this we develop a rigorous control mapping method based on decision trees. The numerical results demonstrate that the decision tree-based dispatch strategy can provide feasible and near optimal dispatch decisions for microgrids. Its computational efficiency is very high, a feature promising for real-time in-field implementation.
引用
收藏
页数:10
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