Application of Improved Reinforcement Learning Technology for Real Time Operation and Scheduling Optimization of Virtual Power Plant

Chao, First A. Zhang; Ying, Second B. Zhang; Yu, Third C. Ji

doi:10.1109/ispec58282.2023.10402841

2023 IEEE Sustainable Power and Energy Conference (iSPEC) 2023

DOI: 10.1109/ispec58282.2023.10402841

|View full text |Cite

Application of Improved Reinforcement Learning Technology for Real Time Operation and Scheduling Optimization of Virtual Power Plant

First A. Zhang Chao,

Second B. Zhang Ying,

Third C. Ji Yu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

Pesántez,

Guamán,

Córdova

et al. 2024

Energies

View full text Add to dashboard Cite

The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.

show abstract

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

Pesántez,

Guamán,

Córdova

et al. 2024

Energies

View full text Add to dashboard Cite

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.

Application of Improved Reinforcement Learning Technology for Real Time Operation and Scheduling Optimization of Virtual Power Plant

Cited by 1 publication

References 3 publications

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

Contact Info

Product

Resources

About