Smart electric vehicles charging with centralised vehicle-to-grid capability for net-load variance minimisation under increasing EV and PV penetration levels

Secchi, Mattia; Barchi, Grazia; Macii, David

doi:10.1016/j.segan.2023.101120

Cited by 17 publications

(5 citation statements)

References 55 publications

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“…When addressing grid operation challenges, topics such as peak load regulation [10], integration of distributed energy resources (DERs) like solar photovoltaic (PV) or wind power [11][12][13], and grid stability issues due to EV charging, such as network voltage or frequency [14,15], are widely discussed. While many studies propose well-coordinated algorithm designs and novel solver techniques to tackle specific optimization problems, the impact of infrastructure configuration on algorithm performance has yet to be extensively addressed.…”

Section: State Of the Field/literature Reviewmentioning

confidence: 99%

The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid

Yoon,

Dargaville

2024

Energies

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The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus.

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Section: State Of the Field/literature Reviewmentioning

confidence: 99%

The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid

Yoon,

Dargaville

2024

Energies

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“…RAMP has already been used in many scientific publications, for instance, for the simulation of electricity (Dimovski et al, 2023), heating (Nicolo Stevanato et al, 2020, cooking (Nicolò Stevanato et al, 2020) and electric mobility (Secchi et al, 2023) demand time series at scales ranging from districts (Pasqui et al, 2023) or villages (Villarroel-Schneider et al, 2023) to continents (Pickering et al, 2022). It has dozens of users globally and has recently become a multi-institution software development effort, actively developed by TU Delft, VITO, Sympheny, the Reiner Lemoine Institut, the University of Liège and the Leibniz University Hannover.…”

Section: Statement Of Needmentioning

confidence: 99%

RAMP: stochastic simulation of user-driven energy demand time series

Lombardi,

Duc,

Tahavori

et al. 2024

JOSS

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“…If the charging is not synchronized, there will be a significant impact in the form of overloaded conductors and cables, low voltages at the consumer end, and a violation of the planning limit [23]. It has been proposed that various demand-side management methods [24,25] be implemented to deal with the increased power consumption by FC stations. One strategy is to use systems that store energy [26,27].…”

Section: Table 1 the DC Charging Levelsmentioning

confidence: 99%

Grid-Connected EV Fast Charging Stations Using Vector Control and CC-CV Techniques

Abd El Halim,

Bayoumi,

El-Khattam

et al. 2023

JESA

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Electric cars are becoming more and more popular. In 2019, 2.2 million electric cars were sold around the world, and that number increased in 2021 to 6.6 million. Microgrids prove to be a viable resolution to the challenge of integrating large-scale electric vehicles and renewable energy sources into the electric power system, given the exponential expansion of both industries and the demand for electric vehicles (EVs). Additionally, the principal policy objective of the government is to enhance the accessibility of public recharge stations designed to accommodate EVs. The study applies cutting-edge technologies to the development of fast-charging (FC) stations. Because DC charging offers unlimited power and quick power transfer, vehicle-to-grid technology can be implemented in a microgrid using DC power transmission. However, incorporating EVs into a microgrid system presents some operational difficulties. In this study, these difficulties are related to power quality (PQ) problems like harmonics in power systems, which have an impact on consumers as well as utilities. The efficacy of the control system was assessed through the simulation of design models employing MATLAB-based vector control and constant current-constant voltage (CC-CV) techniques. Through the reduction of the system's total harmonic distortion (THD), both strategies contribute to the enhancement of the PQ and performance of the control system as assessed by these simulations. Based on the findings, the controller demonstrated a decrease in THD and an enhancement in waveform quality, resulting in high accuracy and good performance. The DC-Bus receives power transmission from the AC network that is near the unity power factor (PF) and is distinguished by a sinusoidal current.

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Smart electric vehicles charging with centralised vehicle-to-grid capability for net-load variance minimisation under increasing EV and PV penetration levels

Cited by 17 publications

References 55 publications

The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid

The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid

RAMP: stochastic simulation of user-driven energy demand time series

Grid-Connected EV Fast Charging Stations Using Vector Control and CC-CV Techniques

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