Decentralised hierarchical coordination of electric vehicles in multi‐microgrid systems. Issue 13 (17th June 2019)

• Record Type: Journal Article
• Authors: Zou, Suli; Ma, Zhongjing; Yang, Nan
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 13(2019)
• Page Start: 2899
• Page End: 2906
• Publication Date: 2019-06-17
• Subjects: distributed power generation -- minimisation -- electric vehicles -- pricing; electricity cost -- MG -- total demand -- peaky demands -- aggregate optimisation problem -- quadratic cost function -- decentralised method -- logarithmic convergence rate -- MMG system -- decentralised algorithm -- optimality -- decentralised hierarchical coordination -- electric vehicles -- multimicrogrid system -- optimal coordination -- EVs -- multitime period -- coordination problems -- individual microgrid -- optimisation problem -- system cost minimization -- time‐of‐use price trajectory; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6767
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Here, the authors study the optimal coordination of electric vehicles (EVs) in a multi‐microgrid (MMG) system with respect to a given time‐of‐use (TOU) price trajectory over a multi‐time period. The authors firstly formulate a class of EV charging/discharging coordination problems for each individual microgrid (MG) to minimise the electricity cost of this MG, while the implemented strategy may result in high variations of the total demand and even build new peaky demands. To mitigate these negative effects, the authors build an aggregate optimisation problem with quadratic cost function under certain bounds on the electricity costs of each MG. The authors further propose a decentralised method for the underlying optimisation problem and verify the convergence of the system to the optimal strategy with a logarithmic convergence rate. Furthermore, the authors consider the power exchange capacity between the MMG system and the main grid, and present a decentralised algorithm to obtain an optimal strategy that minimises the system cost under this capacity constraint. Also, the convergence, the optimality, and the convergence rate of the proposed algorithm are shown.