Efficient Real-Time EV Charging Scheduling via Ordinal Optimization

Li, Teng; Jia, Qing‐Shan; Wang, Gongming; Yong, Yu

doi:10.1109/tsg.2021.3078445

Cited by 52 publications

(22 citation statements)

References 32 publications

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“…These methods are usually easy to implement, but they rely on some assumptions on the system and lack of performance guarantee. In light of this, event-based optimisation (Jiang, Jia, & Guan, 2022;Long & Jia, 2021b;Long, Tang, & Jia, 2017), ordinal optimisation (Long, Jia, Wang, & Yang, 2021) and hierarchical optimisation methods (Huang, Jia, & Guan, 2016) have been proposed recently to put forward new ideas for solving the problem quickly and efficiently.…”

Section: Smart Charging Of Electric Vehiclesmentioning

confidence: 99%

Control for smart systems: Challenges and trends in smart cities

Jia

Panetto

Macchi

et al. 2022

Annual Reviews in Control

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Section: Smart Charging Of Electric Vehiclesmentioning

confidence: 99%

Control for smart systems: Challenges and trends in smart cities

Jia

Panetto

Macchi

et al. 2022

Annual Reviews in Control

Self Cite

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“…[18] presented a flexible adaptive scheduling algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. [22] studied the real-time operation of a public charging station providing charging service to large-scale Plug-in Electric Vehicles (PEVs). [49] introduced a new policy called least laxity ratio to achieve a suitable notion of proportional fairness.…”

Section: Grid-to-vehicle (G2v) 21 Operation Of a Single Charging Stationmentioning

confidence: 99%

A Holistic Review on Advanced Bi-directional EV Charging Control Algorithms

Tang

Sun

et al. 2022

Preprint

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The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission. The fast development of EVs and charging facilities is driving the evolution of Internet of Vehicles (IoV) to Internet of Electric Vehicles (IoEV). IoEV benefits from both smart grid and Internet of Things (IoT) technologies which provide advanced bi-directional charging services and real-time data processing capability, respectively. The major design challenges of the IoEV charging control lie in the randomness of charging events and the mobility of EVs. In this article, we present a holistic review on advanced bi-directional EV charging control algorithms. For Grid-to-Vehicle (G2V), we introduce the charging control problem in two scenarios: 1) Operation of a single charging station and 2) Operation of multiple charging stations in coupled transportation and power networks. For Vehicle-to-Grid (V2G), we discuss how EVs can perform energy trading in the electricity market and provide ancillary services to the power grid. Besides, a case study is provided to illustrate the economic benefit of the joint optimization of routing and charging scheduling of multiple EVs in the IoEV. Last but not the least, we will highlight some open problems and future research directions of charging scheduling problems for IoEVs.CCS Concepts: • General and reference → Surveys and overviews.

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“…As the cost of V2G-compatible chargers continues to decline [8], software development becomes pivotal to efficiently aggregate EVs and optimally control their V2G responses while meeting the designated charging targets. While plenty of works have conducted techno-economic analyses (TEA) of V2G [9]- [12], few have considered complicating factors that practical V2G implementations must address. We group these factors into three categories.…”

Section: Introductionmentioning

confidence: 99%

Vehicle-to-Grid Fleet Service Provision considering Nonlinear Battery Behaviors

Jaworski¹,

Zheng²,

Preindl³

et al. 2023

Preprint

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The surging adoption of electric vehicles (EV) calls for accurate and efficient approaches to coordinate with the power grid operation. By being responsive to distribution grid limits and time-varying electricity prices, EV charging stations can minimize their charging costs while aiding grid operation simultaneously. In this study, we investigate the economic benefit of vehicle-to-grid (V2G) using real-time price data from New York State and a real-world charging network dataset. We incorporate nonlinear battery models and price uncertainty into the V2G management design to provide a realistic estimation of cost savings from different V2G options. The proposed control method is computationally tractable when scaling up to realworld applications. We show that our proposed algorithm leads to an average of 35% charging cost savings compared to uncontrolled charging when considering unidirectional charging, and bi-directional V2G enables additional 18% cost savings compared to unidirectional smart charging. Our result also shows the importance of using more accurate nonlinear battery models in V2G controllers and evaluating the cost of price uncertainties over V2G.

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Efficient Real-Time EV Charging Scheduling via Ordinal Optimization

Cited by 52 publications

References 32 publications

Control for smart systems: Challenges and trends in smart cities

Control for smart systems: Challenges and trends in smart cities

A Holistic Review on Advanced Bi-directional EV Charging Control Algorithms

Vehicle-to-Grid Fleet Service Provision considering Nonlinear Battery Behaviors

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