How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Grigorev, Artur; Mao, Tuo; Berry, Adam; Tan, Joachim; Purushothaman, Loki; Mihăiţă, Adriana-Simona

doi:10.1109/itsc48978.2021.9564561

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…Selain itu peningkatan penggunaan kendaraan listrik menurut Wang et al (2021) menyebabkan kemacetan lalu lintas tambahan. Hal ini dijelaskan Grigorev et al (2021) disebabkan oleh waktu tunggu yang signifikan dalam pengisian baterai di stasiun pengisian. Akibatnya, lalu lintas yang padat ini menyebabkan peningkatan konsumsi energi pada kendaraan listrik (Jonas & Macht, 2019) dan (Deflorio & Castello, 2017).…”

Section: Analisis Metanarasiunclassified

“…Ini berarti bahwa adopsi EVs dapat berdampak negatif pada lalu lintas dan meningkatkan kemacetan jalan. Penelitian Grigorev et al (2021) juga menemukan bahwa kemacetan akibat adopsi kendaraan listrik disebabkan oleh waktu tunggu yang signifikan dalam pengisian baterai di stasiun pengisian. Sementara itu penelitian Jonas & Macht (2019) dan Deflorio & Castello (2017) menemukan bahwa lalu lintas yang padat berdampak pada meningkatnya konsumsi energi pada kendaraan listrik.…”

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“…Penelitian oleh Nur & Kurniawan (2021) dan Rajagukguk (2022) telah membahas mengenai pengaruh pemberian insentif terhadap keberhasilan adopsi KBLBB. Sementara itu Wang et al (2021), Grigorev et al (2021), Jonas & Macht (2019) dan (Deflorio & Castello, 2017) membahas mengenai dampak yang dihasilkan dari adopsi KBLBB. Meskipun telah ada penelitian yang membahas adopsi KBLBB dan dampaknya pada lingkungan, masih ada kesenjangan dalam pemahaman mengenai sudut pandang pemerintah dalam melihat akar masalah sehingga mengeluarkan kebijakan insentif KBLBB.…”

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Analisis Naratif Kebijakan Insentif Kendaraan Bermotor Listrik Dalam Mengurangi Emisi Karbon

Fitri,

Samputra

2024

SLJIL

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Pemerintah menerapkan strategi insentif Kendaraan Bermotor Listrik Berbasis Baterai (KBLBB) untuk mencapai target net zero emission. Namun dalam implementasinya terdapat kontra narasi dari kebijakan tersebut. Penelitian ini bertujuan untuk menganalisis latar belakang pemerintah dalam menetapkan kebijakan insentif KBLBB di Indonesia dengan menganalisis narasi pemerintah dan kontra narasi dari berbagai pihak. Penelitian ini menggunakan pendekatan kualitatif dengan metode Narrative Policy Analysis (NPA). Sumber data menggunakan dokumen instansi pemerintah berupa transkrip pidato dan siaran pers serta berita media online. Hasil perbandingan narasi pemerintah dengan kontra narasi dari akademisi dan kelompok masyarakat sipil, menunjukan terdapat perbedaan sistem kepercayaan. Pemerintah menilai penyebab emisi karbon bersumber dari kendaraan pribadi berbahan bakar BBM, sehingga solusinya dengan menggunakan KBLBB. Sementara itu narasi kontra meyakini bahwa KBLBB tidak menjadi solusi polusi dan emisi karena pembangkit listrik untuk KBLBB tersebut masih berasal dari energi fosil berupa minyak, gas dan batu bara. Pada dasarnya pihak kontra menyetujui konversi BBM untuk mengurangi emisi karbon, namun pemilihan listrik dinilai kurang tepat saat ini karena keterbatasan bahan baku dan emisi yang ditimbulkan. Strategi kebijakan insentif KBLBB perlu dipertimbangkan ulang dengan jaminan kepastian bahan baku listrik berasal dari energi terbarukan, kesiapan pengisian baterai dan sistem pengelolaan limbanya, serta pemberian insentif yang lebih tepat sasaran.

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Section: Analisis Metanarasiunclassified

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Analisis Naratif Kebijakan Insentif Kendaraan Bermotor Listrik Dalam Mengurangi Emisi Karbon

Fitri,

Samputra

2024

SLJIL

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“…The global EV market is expected to reach over one billion EVs by 2050 [1]. This projection poses three main challenges: the accessibility of charging for EV users, the availability of electric grid capacity to support the charging of electric vehicles, and the use of EV batteries as a source of flexibility for grid services [2]. To avoid a systematic reinforcement of the electricity grid due to the enormous power demand of EVs, several studies have been carried out in recent years on smart charging to manage the charging infrastructure for electric vehicles [1, [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Electrical Vehicle Smart Charging Using the Open Charge Point Interface (OCPI) Protocol

Guillemin,

Choulet,

Guyot

et al. 2024

Energies

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This paper proposes a new approach to the design of smart charging systems. It aims to separate the role of the Smart Charging Service Provider (SCSP) from the role of the Charge Point Operator (CPO) to provide real flexibility and efficiency of mass deployment. As interoperability is required for this purpose, the challenge is to use standard equipment and protocols in the design of the smart charging Energy Management System (EMS). The use of an Open Charge Point Interface (OCPI) is crucial for an interface between the EMS and the Charge Point Operator. The smart charging EMS developed has been implemented and successfully tested with two CPOs, with different use cases: (1) EV charging infrastructure at office buildings, and (2) EV charging infrastructure installed at a public car park facility.

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“…Grigorev et al [15] present a modelling approach to analyze the impact of EVs on traffic congestion and energy consumption. Yin and Zao [16] use real-time traffic flow in an urban environment to plan the infrastructure of charging stations.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation

Witt

2023

LOGI – Scientific Journal on Transport and Logistics

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To make travelling with electric vehicles (EVs) over long distances as convenient as travelling with traditional vehicles, charging stations along motorways are necessary. Furthermore, waiting times for free charging points must be short to enable a fast onward journey, and this also on days with heavy traffic volumes. To determine the required number of charging stations in more detail, a model was created that simulates the process of arriving and leaving cars at a charging park based on real traffic data. For the traffic data, a location and date in the Munich region were chosen that represent a peak demand and thus a “worst case” scenario. The ability to cover such scenarios as well seems to be important because otherwise severe congestion with long waiting times would appear on days with heavy traffic, which would make the use of EVs very unattractive. It turned out that 150 to 600 charging stations – depending on the considered scenario – would be necessary to charge proportions of 10% to 20% of all passing cars.

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How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Cited by 5 publications

References 15 publications

Analisis Naratif Kebijakan Insentif Kendaraan Bermotor Listrik Dalam Mengurangi Emisi Karbon

Analisis Naratif Kebijakan Insentif Kendaraan Bermotor Listrik Dalam Mengurangi Emisi Karbon

Electrical Vehicle Smart Charging Using the Open Charge Point Interface (OCPI) Protocol

Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation

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