Online scheduling of plug-in vehicles in dynamic pricing schemes

A. Arif, M. Babar, I. Ahamed, E. AlAmmar, H.P. Nguyen, I.G. Kamphuis, N. Malik

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
8 Downloads (Pure)

Abstract

In recent years there has been an increase in the use of plug-in electric vehicles (PEVs). Though it has many advantages, PEVs could stress the grid if their charging schedule is not managed properly. There are several issues to be addressed such as (i) Scheduling of PEV under different types of Tariffs (ii) Prediction of load profile and charging requirement. This paper presents three algorithms for scheduling PEV charging by an aggregator under different conditions. This paper has an aim to present the approaches for different possible cases to schedule PEVs charging. In order to achieve this ambition, first, a simple placement algorithm to schedule the PEVs is developed provided prices are predetermined. Then the scheduling problem is formulated as a single stage decision making problem in case prices are not known in advance and PEVS are independent atomic loads. However, it has been researched that PEVs are independent non-atomic loads. Finally, this paper modifies other cases and then the problem is formulated as a multistage decision making problem to determine the optimum schedule in more realistic scenario. In all cases, proposed algorithms are simulated for a building with an objective to schedule PEVs charging during working hours such that the total cost of energy is minimized and the requirements of user are also satisfied.
Original languageEnglish
Pages (from-to)25–36
JournalSustainable Energy, Grids and Networks
Volume7
DOIs
Publication statusPublished - 2016

Keywords

  • Demand response; Learning automata; Online algorithm; Plug-in electric vehicle; Real time pricing; Reinforcement learning

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