This paper aims to design an algorithm dedicated to operational planning for microgrids in the challenging case where the scenarios of production and consumption are not known in advance. Using expert knowledge obtained from solving a family of linear programs, we build a learning set for training a decision-making agent. The empirical performances in terms of Levelized Energy Cost (LEC) of the obtained agent are compared to the expert performances obtained in the case where the scenarios are known in advance. Preliminary results are promising.
CITATION STYLE
Aittahar, S., François-Lavet, V., Lodeweyckx, S., Ernst, D., & Fonteneau, R. (2015). Imitative learning for online planning in microgrids. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9518, pp. 1–15). Springer Verlag. https://doi.org/10.1007/978-3-319-27430-0_1
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