電気自動車用充電スタンドの最適な設置場所を特定する新モデルを開発(New Model Finds Best Sites for Electric Vehicle Charging Stations)

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2022-06-06 ノースカロライナ州立大学(NCState)

ノースカロライナ州立大学の研究者らは、電気自動車(EV)充電設備の最適な設置場所や、地域の電力網に過度の負担をかけずに充電設備を設置するための計算モデルを開発しました。

<関連情報>

ユーザー平衡判断を伴う電動モビリティのための配電・充電ネットワーク統合設計 Joint power distribution and charging network design for electrified mobility with user equilibrium decisions

Leila Hajibabai,Asya Atik,Amir Mirheli
Computer-Aided Civil and Infrastructure Engineering  Published: 06 June 2022
DOI:https://doi.org/10.1111/mice.12854

Abstract

Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV-dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed-integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG ) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real-world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east-coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights.

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