Case report
https://doi.org/10.32985/ijeces.13.1.7
Electric Energy Management for Plug-in Electric Vehicles Charging in the Distribution System by a dual cascade scheduling algorithm
Supipat Panichtanakom
; Prince of Songkla University, Faculty of Engineering, Department of Electrical Engineering Hatyai, Songkla, Thailand
Kusumal Chalermyanont
; Prince of Songkla University, Faculty of Engineering, Department of Electrical Engineering Hatyai, Songkla, Thailand
Abstract
This paper presents an algorithm for plug-in electric vehicles (PEVs) charging in the three-phase distribution system for residential houses. It aims to prevent violent voltage level deviation and increasing losses on the three-phase distribution system due to uncontrolled charging and allocate power to each plug-in electric vehicle. The algorithm is comprised of two processes. The first process is power limitation and limited power of load imbalance by if-else rules, while the second process is power allocation to each PEV by the dual cascade scheduling algorithm which is the integration of tasking scheduling algorithms. A 100 kVA distribution transformer and 30 houses are defined in the simulation situation. Also, the available PEVs in single-phase, two-phase, and three-phase systems are assigned for verification of the proposed algorithm. Root-mean-square deviation (RMSD) referred to the satisfaction of PEV owners, total PEVs charged energy, and the average percentage of achieved charging time, as the result indicators. The results show the proposed algorithm can provide good results without rejected PEVs charging. Furthermore, this paper also displays the analysis of voltage level, percentage of voltage unbalance factor, and loss in the distribution system. In the future, coordination with home appliances to gain a high load margin or electric energy cost control will be improved in the proposed algorithm
Keywords
Electric energy management; Plug-in electric vehicles charging; Dual cascade scheduling algorithm
Hrčak ID:
273320
URI
Publication date:
3.2.2022.
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