Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Dufo-López, Rodolfo & Bernal-Agustín, José L. & Contreras, Javier, 2007. "Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage," Renewable Energy, Elsevier, vol. 32(7), pages 1102-1126.
- Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
- Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
- Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
- Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
- Samende, Cephas & Cao, Jun & Fan, Zhong, 2022. "Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints," Applied Energy, Elsevier, vol. 317(C).
- Liu, Jinhui & Xu, Zhanbo & Wu, Jiang & Liu, Kun & Guan, Xiaohong, 2021. "Optimal planning of distributed hydrogen-based multi-energy systems," Applied Energy, Elsevier, vol. 281(C).
- Louis Desportes & Inbar Fijalkow & Pierre Andry, 2021. "Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage," Energies, MDPI, vol. 14(15), pages 1-22, August.
- Correa, G. & Muñoz, P. & Falaguerra, T. & Rodriguez, C.R., 2017. "Performance comparison of conventional, hybrid, hydrogen and electric urban buses using well to wheel analysis," Energy, Elsevier, vol. 141(C), pages 537-549.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Grigorios L. Kyriakopoulos & Konstantinos G. Aravossis, 2023. "Literature Review of Hydrogen Energy Systems and Renewable Energy Sources," Energies, MDPI, vol. 16(22), pages 1-21, November.
- Ward Suijs & Sebastian Verhelst, 2023. "Scaling Performance Parameters of Reciprocating Engines for Sustainable Energy System Optimization Modelling," Energies, MDPI, vol. 16(22), pages 1-28, November.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
- Samende, Cephas & Cao, Jun & Fan, Zhong, 2022. "Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints," Applied Energy, Elsevier, vol. 317(C).
- Li, Zichen & Xia, Yanghong & Bo, Yaolong & Wei, Wei, 2024. "Optimal planning for electricity-hydrogen integrated energy system considering multiple timescale operations and representative time-period selection," Applied Energy, Elsevier, vol. 362(C).
- Christian Pötzinger & Markus Preißinger & Dieter Brüggemann, 2015. "Influence of Hydrogen-Based Storage Systems on Self-Consumption and Self-Sufficiency of Residential Photovoltaic Systems," Energies, MDPI, vol. 8(8), pages 1-21, August.
- Ritu Kandari & Neeraj Neeraj & Alexander Micallef, 2022. "Review on Recent Strategies for Integrating Energy Storage Systems in Microgrids," Energies, MDPI, vol. 16(1), pages 1-24, December.
- Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
- Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.
- Federica Cucchiella & Idiano D’Adamo & Paolo Rosa, 2015. "Industrial Photovoltaic Systems: An Economic Analysis in Non-Subsidized Electricity Markets," Energies, MDPI, vol. 8(11), pages 1-16, November.
- Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
- Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
- Lee, Dong-Yeon & Elgowainy, Amgad & Vijayagopal, Ram, 2019. "Well-to-wheel environmental implications of fuel economy targets for hydrogen fuel cell electric buses in the United States," Energy Policy, Elsevier, vol. 128(C), pages 565-583.
- Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018.
"Cooperating with machines,"
Nature Communications, Nature, vol. 9(1), pages 1-12, December.
- Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
- Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
- Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
- Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
- Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
- Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
- Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
- Klein, Martin & Deissenroth, Marc, 2017.
"When do households invest in solar photovoltaics? An application of prospect theory,"
Energy Policy, Elsevier, vol. 109(C), pages 270-278.
- Martin Klein & Marc Deissenroth, 2018. "When Do Households Invest in Solar Photovoltaics? An Application of Prospect Theory," Papers 1808.05572, arXiv.org.
- Bernadette Fina & Hans Auer, 2020. "Economic Viability of Renewable Energy Communities under the Framework of the Renewable Energy Directive Transposed to Austrian Law," Energies, MDPI, vol. 13(21), pages 1-31, November.
- Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
- Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
More about this item
Keywords
deep reinforcement learning; multi-agent deep deterministic policy gradient; battery and hydrogen energy storage systems; decarbonisation; renewable energy; carbon emissions; deep-Q network;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6770-:d:1245684. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.