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Coordinated optimization of emergency power vehicles and distribution network reconfiguration considering the uncertain restoration capability of e-taxis

Zhang, Lu, Jiang, Shujun, Zhang, Bo, Li, Gen, Wang, Zhaoqi and Tang, Wei 2022. Coordinated optimization of emergency power vehicles and distribution network reconfiguration considering the uncertain restoration capability of e-taxis. IEEE Transactions on Industry Applications 58 (2) , pp. 2707-2717. 10.1109/TIA.2021.3137132

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Abstract

Network reconfiguration and emergency power vehicles (EPVs) dispatching are widely used in distribution networks for load restoration. However, their capabilities are limited by the allocated amounts of circuit breakers and EPVs. E-taxis can also participate in the restoration as a kind of mobile energy storage using the vehicle to grid (V2G) technology. However, the uncertainty of E-taxis should be considered in the restoration. To achieve better effectiveness of the restoration and fully utilize the capability of network reconfiguration, EPVs and E-taxis, this paper proposes a coordinated restoration optimization method considering the uncertain restoration capabilities of discharging stations with E-taxis. A joint probability distribution function is established based on Gaussian Mixture Model to describe the uncertainty of station discharging capabilities considering the correlation of user rationality, taxi state-of-charge and transportation status. Then, a bi-level programming model embedded with the chance constraint programming is developed to optimize the coordinated dynamic restoration scheme of the network reconfiguration and EPV dispatching, with the consideration of the mobility of EPVs during the restoration. Simulations studies are performed to verify the proposed method.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 0093-9994
Date of First Compliant Deposit: 15 December 2021
Date of Acceptance: 13 December 2021
Last Modified: 11 Apr 2022 14:38
URI: https://orca.cardiff.ac.uk/id/eprint/146162

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