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Keywords

electric vehicles; charging guidance; collaborative optimization; real‑time pricing; rolling prediction

Abstract

Disorderly charging of a large number of electric vehicles (EVs) can lead to issues such as traffic congestion on road networks and low voltage at distribution network nodes. To address this, an analysis of the EVs‑Traffic‑Distribution (ETD) interaction characteristics is conducted, leading to the development of a collaborative optimization framework for ETD. Firstly, travel and charging demand models are derived through analyzing the behavior characteristics of EVs. Next, the shortest path, optimal time, and minimal energy consumption routes that satisfy travel demand are proposed based on the Floyd algorithm. Then, node charging load is forcasted by rolling prediction and real‑time electricity prices are formulated, both based on BP neural network models. Finally, an orderly charging guidance strategy is proposed to meet the charging demand based on three types of routes and real‑time electricity prices. The simulation results show that the proposed guidance strategy can reduce EVs charging costs while effectively mitigating road congestion and low voltage problems.

DOI

10.19781/j.issn.1673-9140.2023.05.005

First Page

44

Last Page

56

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