Modeling and Impact Analysis for Solar Road Integration in Distribution Networks

Shi, Bochen; Dai, Wei; Luo, Ceheng; Goh, Hui Hwang; Li, Jinghua

doi:10.1109/tste.2022.3230686

Cited by 5 publications

(1 citation statement)

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“…However, the charging price and queueing time at EVCSs are expected to influence the preferences of EV drivers for charging stations and, as a result, change the distribution of the electrical demand. Therefore, it is crucial to include coupling parameters in the coordination and scheduling of PTNs [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

Dai,

Zeng,

Wang

et al. 2024

Energies

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The exchange of sensitive information between power distribution networks (PDNs) and urban transport networks (UTNs) presents a difficulty in ensuring privacy protection. This research proposes a new collaborative operation method for a coupled system. The scheme takes into account the schedulable capacity of electric vehicle charging stations (EVCSs) and locational marginal prices (LMPs) to handle the difficulty at hand. The EVCS hosting capacity model is built and expressed as the feasible area of charging power, based on AC power flow. This model is then used to offer information on the real schedulable capacity. By incorporating the charging loads into the coupling nodes between PDNs and UTNs, the issue of coordinated operation is separated and becomes equal to the optimal problem involving charging loads. Based on this premise, the most efficient operational cost of PDNs is transformed into a comparable representation of cost information in PDNs. This representation incorporates LMP information that guides charging decisions in UTNs. The suggested collaborative scheduling methodology in UTNs utilises the collected projection information from the static traffic assignment (STA) to ensure data privacy protection and achieve non-iterative calculation. Numerical experiments are conducted to illustrate that the proposed method, which uses a smaller amount of data, achieves the same level of optimality as the coordinated optimisation.

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