An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles-Reference-Cited by-同舟云学术

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

Published:2024-07 Issue: Volume:107 Page:105352
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Sepehrzad Reza^ORCID,Langeroudi Amir Saman Godazi^ORCID,Khodadadi Amin,Adinehpour Sara,Al-Durra Ahmed^ORCID,Anvari-Moghaddam Amjad^ORCID

Funder

Khalifa University of Science and Technology

Publisher

Elsevier BV

Reference44 articles.

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4. Electrical consumption forecasting in sports venues: A proposed approach based on neural networks and ARIMAX Models;Almaleck;Sustainable Cities and Society,2024

5. Distributed consensus-based reactive power sharing in microgrids: A predictive virtual capacitance control technique;Babayomi;International Journal of Electrical Power & Energy Systems,2022

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