Abstract
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.
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Acknowledgments
Raphael Fonteneau is a Postdoctoral Fellow of the F.R.S.-FNRS. The authors also thank the Walloon Region who has funded this research in the context of the BATWAL project. The authors also thank Bertrand Cornelusse for valuable discussions.
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Aittahar, S., François-Lavet, V., Lodeweyckx, S., Ernst, D., Fonteneau, R. (2015). Imitative Learning for Online Planning in Microgrids. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2015. Lecture Notes in Computer Science(), vol 9518. Springer, Cham. https://doi.org/10.1007/978-3-319-27430-0_1
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DOI: https://doi.org/10.1007/978-3-319-27430-0_1
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