Open AccessIntegrated active/reactive power scheduling of interdependent microgrid and EV fleets based on stochastic multi‐objective normalised normal constraint
Author(s) -
Saffari Mohammadali,
Kia Mohsen,
Vahidinasab Vahid,
Mehran Kamyar
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.1406
Subject(s) - microgrid , mathematical optimization , ac power , interdependence , computer science , scheduling (production processes) , integer programming , linear programming , electric vehicle , voltage , wind power , operations research , reliability engineering , power (physics) , engineering , mathematics , electrical engineering , physics , quantum mechanics , political science , law
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.