Using analogs ensembles and genetic algorithm to handle uncertainty in a microgrid

Fausto Calderon-Obaldia (1, 2) , Anne Migan-Dubois (1) , Jordi Badosa (3) , Vincent Bourdin (4)

1 GeePs - Laboratoire Génie électrique et électronique de Paris
2 UCR - Universidad de Costa Rica
3 LMD - Laboratoire de Météorologie Dynamique (UMR 8539)
4 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

In this work, a novel approach to deal with the PV forecast uncertainty during the energy management of a microgrid is presented. A novel adaptation of an analogs ensembles method allows to obtain a Sharpness indicator that is correlated with the PV forecast uncertainty. This indicator can be used to dynamically restrict the usable battery capacity when doing the day-ahead optimal scheduling using a genetic algorithm. This permits to deal with the PV uncertainty internally within the microgrid. This gives a total certainty to the grid operator about the power needs of the microgrid one day in advance. In this way, in a big scale, the uncertainty caused by a higher penetration of renewable energy sources in the national grid could be highly reduced. The main results of a real study-case are presented and the limitations of the method for its implementation are also discussed.

Mots clés: PV Systems and Storage -Modelling; Design; Operation and Performance Subtopic 3: Operation; Performance and Maintenance of PV Systems uncertainty; analogs ensemble; microgrids; energy management system; genetic algorithm; battery management

Domaines: Science des matériaux [cond-mat.mtrl-sci]; Electronique; Energie électrique

Fausto Calderon-Obaldia, Anne Migan-Dubois, Jordi Badosa, Vincent Bourdin. Using analogs ensembles and genetic algorithm to handle uncertainty in a microgrid. 37th European Photovoltaic Solar Energy Conference and Exhibition (Eu-PVSEC), Sep 2020, Lisbon, Portugal. ⟨hal-03312287⟩