Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets-Reference-Cited by-同舟云学术

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Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

Published:2024-09 Issue:3 Volume:2 Page:392-406
ISSN:2771-9626
Container-title:IEEE Transactions on Energy Markets, Policy and Regulation
language:
Short-container-title:IEEE Trans. on Enrgy Mrkts, Pol and Reg

Author:

Li Jinhao¹^ORCID,Wang Changlong²^ORCID,Zhang Yanru³^ORCID,Wang Hao¹^ORCID

Affiliation:

1. Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University, Melbourne, VIC, Australia

2. Department of Civil Engineering, Monash University, Melbourne, VIC, Australia

3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Funder

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9794458/10679512/10458427.pdf?arnumber=10458427

Reference40 articles.

1. Climate change 2022: Mitigation of climate change. Contribution of working group III to the sixth assessment report of the intergovernmental panel on climate change;Shukla,2022

2. Challenges and solution technologies for the integration of variable renewable energy sources—a review

3. Victorian electricity sector renewable energy transition;State Government of Victoria,2022

4. Large-scale battery storage knowledge sharing report,2019

5. Guide to ancillary services in the national electricity market,2015

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