Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview

Li, Xiaohui; Wang, Zhenpo; Sun, Fuchun; Cui, Dingsong; Hecht, Christopher; Figgener, Jan; Sauer, Dirk Uwe

doi:10.1016/j.energy.2023.126647

Cited by 44 publications

(6 citation statements)

References 116 publications

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“…However, although the V2G flexibility, which is the amount of available resources, is an important factor in determining performance, these studies have been conducted based on simple assumptions using historical data [14,15], fixed given values [16], stochastic random models [17][18][19][20], and parametric models such as battery size [21]. Only a few empirical studies have investigated V2G flexibility [22][23][24][25], as presented in Table 3. However, these simple estimations of V2G flexibility based on a long-term average model [22], a data-dependent model [23,24], and a stochastic random model [25] are limited in terms of their general use.…”

Section: Focusing On the Impact Of Ev Chargers Control Approachesmentioning

confidence: 99%

“…Only a few empirical studies have investigated V2G flexibility [22][23][24][25], as presented in Table 3. However, these simple estimations of V2G flexibility based on a long-term average model [22], a data-dependent model [23,24], and a stochastic random model [25] are limited in terms of their general use.…”

Section: Focusing On the Impact Of Ev Chargers Control Approachesmentioning

confidence: 99%

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Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea

Jang,

Kim,

2023

Sustainability

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With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered.

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Section: Focusing On the Impact Of Ev Chargers Control Approachesmentioning

confidence: 99%

Section: Focusing On the Impact Of Ev Chargers Control Approachesmentioning

confidence: 99%

Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea

Jang,

Kim,

2023

Sustainability

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“…Several studies have explored the effects of EVs on the power grid (PG). According to [17], The study offers a thorough and encompassing exploration of EV behavior modeling and its role in the development of algorithms for integrating EVs with the grid. A range of models has been devised to capture the intricacies of EV usage patterns, the decision-making processes regarding charging, and the manner in which EVs respond to intelligent charging strategies [18,19].…”

Section: Introductionmentioning

confidence: 99%

Global Trends in Electric Vehicle Battery Efficiency and Impact on Sustainable Grid

Şimşir,

Ghayth

2024

jsesd

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Over the past decade, transportation electrification has emerged as a pivotal focus of the article. Electric vehicles (EVs) have progressively gained traction in the market, displacing conventional internal combustion engine vehicles. This surge in EV popularity has led to a corresponding increase in the number of charging stations, thereby significantly influencing the power grid (PG). Various charging strategies and grid integration approaches are being devised to mitigate the potential negative impacts of EV charging while optimizing the advantages of integrating EVs with the grid. This paper provides a comprehensive overview of the current state of the EV market, standards, charging infrastructure, and the PG’s response to the impact of EV charging. The article provides a comprehensive assessment of how forthcoming advancements in EV technology, including connected vehicles, autonomous driving, and shared mobility, will intricately influence the integration of EVs with the PG. Ultimately, the article concludes by meticulously analyzing and summarizing both the challenges and recommendations pertinent to the prospective expansion of EV charging infrastructure and grid integration. The proliferation of venture capital investments in nascent start-up ventures specializing in EV and battery technologies has experienced a pronounced surge, reaching an impressive sum of nearly USD 2.1 billion in 2022. This notable increase represents a substantial uptick of 30% compared to the figures recorded in 2021. Furthermore, these investments have been directed towards two key areas: advancements in battery technology and the acquisition of critical minerals. This discernible shift in investment trends underscores the growing recognition of the strategic importance and potential profitability associated with innovations in EV and battery technologies. In 2022, global expenditures on EVs surpassed USD 425 billion, marking a substantial 50% increase compared to the previous year, 2021. Remarkably, a mere 10% of these expenditures can be attributed to governmental support, with the bulk stemming from consumer investments.

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“…However, the increasing number of EVs brings new problems. Orderly charging gradually replaces random charging, because it can improve grid performance, reduce charging costs, even reduce construction costs, and improve safety 1–3 …”

Section: Introductionmentioning

confidence: 99%

“…Orderly charging gradually replaces random charging, because it can improve grid performance, reduce charging costs, even reduce construction costs, and improve safety. [1][2][3] Reasonable scheduling of EVs, whether in terms of time or space, has been explored by many scholars. Different optimization goals mean different optimization schemes.…”

mentioning

confidence: 99%

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

Xiaoxin

Chen

2023

Energy Science & Engineering

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Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing.

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Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview

Cited by 44 publications

References 116 publications

Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea

Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea

Global Trends in Electric Vehicle Battery Efficiency and Impact on Sustainable Grid

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

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