Optimal Energy Scheduling of Flexible Industrial Prosumers via Reinforcement Learning-Reference-Cited by-同舟云学术

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Optimal Energy Scheduling of Flexible Industrial Prosumers via Reinforcement Learning

Published:2023-06-25 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Belgrade PowerTech
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Author:

van den Bovenkamp Nick¹,Giraldo Juan S.²,Salazar Duque Edgar Mauricio³,Vergara Pedro P.¹,Konstantinou Charalambos⁴,Palensky Peter¹

Affiliation:

1. Delft University of Technology,Intelligent Electrical Power Grids - EEMCS,Netherlands

2. Energy Transition Studies Group, Netherlands Organisation for Applied Scientific Research (TNO),Netherlands

3. Eindhoven University of Technology,Electrical Energy Systems - EES,Netherlands

4. King Abdullah University of Science and Technology (KAUST),CEMSE Division,Saudi Arabia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10202231/10202243/10202699.pdf?arnumber=10202699

Reference16 articles.

1. Community energy storage operation via reinforcement learning with eligibility traces

2. Reinforcement Learning Based Energy Management Algorithm for Energy Trading and Contingency Reserve Application in a Microgrid

3. A deeper look at experience replay;zhang;ArXiv Preprint,2017

4. Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

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