Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

Chen, Longxiang; He, Huan; Jing, Rui; Xie, Meina; Ye, Kai

doi:10.1016/j.energy.2024.132757

Energy

2024

DOI: 10.1016/j.energy.2024.132757

|View full text |Cite

Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

Longxiang Chen,

Huan He,

Rui Jing

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning

Faghri,

Tahami,

Amini

et al. 2025

Sustainable Cities and Society

View full text Add to dashboard Cite

Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning

Faghri,

Tahami,

Amini

et al. 2025

Sustainable Cities and Society

View full text Add to dashboard Cite

Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review

Hu,

Wang,

Tan

2024

Energies

View full text Add to dashboard Cite

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

Cited by 2 publications

References 36 publications

Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning

Real-time energy flexibility optimization of grid-connected smart building communities with deep reinforcement learning

Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review

Contact Info

Product

Resources

About