A bi-level optimisation framework for electric vehicle fleet charging management

Škugor, Branimir and Deur, Joško (2016) A bi-level optimisation framework for electric vehicle fleet charging management. = A bi-level optimisation framework for electric vehicle fleet charging management. Applied Energy, 184. pp. 1332-1342. ISSN 0306-2619. Vrsta rada: ["eprint_fieldopt_article_type_article" not defined]. Kvartili JCR: Q1 (2016). Točan broj autora: 2. (In Press)

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The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State- of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent- based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport- energy system.

Item Type: Article (["eprint_fieldopt_article_type_article" not defined])
Keywords (Croatian): electric vehicle fleet; aggregate battery; modelling; charging optimisation; genetic algorithm; dynamic programming
Subjects: TECHNICAL SCIENCE > Mechanical Engineering
Divisions: 900 Department of Robotics and Production System Automation > 910 Chair of Engineering Automation
Indexed in Web of Science: Yes
Indexed in Current Contents: Yes
Citations JCR: 9 (11.09.2018.)
Quartiles: Q1 (2016)
Citations SCOPUS: 11 (11.09.2018.)
Date Deposited: 19 Sep 2016 15:24
Last Modified: 18 Oct 2018 11:48
URI: http://repozitorij.fsb.hr/id/eprint/6397

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