Multi-agent deep reinforcement learning-based multi-time scale energy management of urban rail traction networks with distributed photovoltaic–regenerative braking hybrid energy storage systems-Reference-Cited by-同舟云学术

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Multi-agent deep reinforcement learning-based multi-time scale energy management of urban rail traction networks with distributed photovoltaic–regenerative braking hybrid energy storage systems

Published:2024-08 Issue: Volume:466 Page:142842
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Li Guannan^ORCID,Or Siu Wing^ORCID

Publisher

Elsevier BV

Reference51 articles.

1. Optimal operation of electric railways with renewable energy and electric storage systems;Aguado;IEEE Trans. Smart Grid,2018

2. Enhancing energy management of a stationary energy storage system in a DC electric railway using fuzzy logic control;Alnuman;Int. J. Electr. Power Energy Syst.,2022

3. A coordinated emergency response scheme for electricity and watershed networks considering spatio-temporal heterogeneity and volatility of rainstorm disasters;Cao;IEEE Trans. Smart Grid,2024

4. Dynamic voltage unbalance constrained economic dispatch for electrified railways integrated energy storage;Chen;IEEE Trans. Ind. Inf.,2022

5. Chance-constrained optimization of storage and PFC capacity for railway electrical smart grids considering uncertain traction load;Chen;IEEE Trans. Smart Grid,2024

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