Integrated active/reactive power scheduling of interdependent microgrid and EV fleets based on stochastic multi-objective normalised normal constraint

This study proposes an integrated framework for coordinated optimisation of the interdependent microgrid (MG) and electric vehicle (EV) fleet entities using the normalised normal constraint approach. By considering the active/reactive power management option of the bidirectional charger enabled EVs...

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Veröffentlicht in:IET generation, transmission & distribution Jg. 14; H. 11; S. 2055 - 2064
Hauptverfasser: Saffari, Mohammadali, Kia, Mohsen, Vahidinasab, Vahid, Mehran, Kamyar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 05.06.2020
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ISSN:1751-8687, 1751-8695
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Zusammenfassung:This study proposes an integrated framework for coordinated optimisation of the interdependent microgrid (MG) and electric vehicle (EV) fleet entities using the normalised normal constraint approach. By considering the active/reactive power management option of the bidirectional charger enabled EVs in the proposed model, the authors investigate the effectiveness of EV's integration in the presence of the techno-economical objective functions. This work concentrates on the trade-off analysis of two conflicting objectives, including the economic objective of the MG's operation cost minimisation and the technical objective of the MG's voltage deviation. Besides, they consider several uncertainty sources, e.g. wind, EV and solar panel (PV) power provision, as well as market price fluctuations in the proposed model affecting the aforementioned techno-economic trade-off solution. The proposed model is a stochastic multi-objective mixed-integer non-linear programming problem where the authors apply the designed integrated framework on a modified IEEE 18-bus test case in GAMS software. Through numerical results, they demonstrate MG optimal operation changes due to different MGO priorities and study the positive effects of EVs integrated energy management on the bi-objective operation.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2019.1406