Bi-level optimisation framework for electric vehicle fleet charging

Škugor, Branimir and Deur, Joško (2015) Bi-level optimisation framework for electric vehicle fleet charging. = Bi-level optimisation framework for electric vehicle fleet charging. In: 10th Conference on Sustainable Development of Energy, Water and Environment Systems - SDEWES, 27.09-02.10.2015., Dubrovnik, Hrvatska.

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The paper proposes bi-level optimisation framework for electric vehicle (EV) fleet charging based on realistic EV fleet and transport demand model. The EV fleet is modelled as a single so-called aggregate battery and parameterised by using recorded data of a particular delivery vehicle fleet. This EV fleet model is used within the inner level of bi-level optimisation framework, where the aggregate charging power variable is optimised by using the dynamic programming (DP) algorithm. In the superimposed level of optimisation framework, the final state-of-charge (SoC) values of EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of bi-level optimisation, it is needed to recalculate transport demand-related input time distributions of the aggregate battery model. To simplify this process, 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 EV model. The bi-level optimisation framework represents the extension of the single-level optimisation thus enabling the multi-parameter optimisation of the considered transport-energy system as well as optimisation of different economic-related aspects, e.g. investment vs. operational costs. The bi-level optimisation approach is validated by comparing its optimisation results with the previously obtained results based on a single-level optimisation approach where the final SoC values were fixed to 100%.

Item Type: Conference or Workshop Item (Lecture)
Keywords (Croatian): electric vehicle fleet, aggregate battery, transport demand, modelling, charging optimisation, NSGA-II, 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: No
Indexed in Current Contents: No
Date Deposited: 19 Sep 2016 15:39
Last Modified: 16 Oct 2018 15:25

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