A Forecasting Based Hierarchical Energy Management for Sustainable Data Centers
Li, Huan LU ; Alakula, Mats LU
and Gualous, Hamid
(2023)
49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
- Abstract
Renewable energy (RE) integrated with a hybrid electric-hydrogen storage system (HEHSS) is considered a promising solution for data centers to increase their energy efficiency and decrease their greenhouse emissions. This paper proposes a forecasting based hierarchical energy management strategy for a data center with wind energy, tidal energy and HEHSS, which is taken as a microgrid. A multi-population genetic algorithm-based day-ahead scheduling is used as the first layer and a rolling predictive control-based real-time dispatching is added as a second layer to minimize the degradation costs of HEHSS and maximize the revenue of the electricity and hydrogen trading. Also, a long-short-term-memory-based forecasting framework is... (More)
Renewable energy (RE) integrated with a hybrid electric-hydrogen storage system (HEHSS) is considered a promising solution for data centers to increase their energy efficiency and decrease their greenhouse emissions. This paper proposes a forecasting based hierarchical energy management strategy for a data center with wind energy, tidal energy and HEHSS, which is taken as a microgrid. A multi-population genetic algorithm-based day-ahead scheduling is used as the first layer and a rolling predictive control-based real-time dispatching is added as a second layer to minimize the degradation costs of HEHSS and maximize the revenue of the electricity and hydrogen trading. Also, a long-short-term-memory-based forecasting framework is developed to forecast power generation and load demand.
(Less)
- author
- Li, Huan
LU
; Alakula, Mats
LU
and Gualous, Hamid
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Energy management, Energy storage, Forecasting, Hierarchical systems, Hydrogen, Optimization methods
- host publication
- IECON Proceedings (Industrial Electronics Conference)
- publisher
- IEEE Computer Society
- conference name
- 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
- conference location
- Singapore, Singapore
- conference dates
- 2023-10-16 - 2023-10-19
- external identifiers
-
- scopus:85179508613
- ISBN
- 9798350331820
- DOI
- 10.1109/IECON51785.2023.10312227
- language
- English
- LU publication?
- yes
- id
- b90aef4c-3dea-4dfa-87cb-0e026cc414ac
- date added to LUP
- 2024-01-11 11:01:24
- date last changed
- 2024-01-11 11:03:37
@inproceedings{b90aef4c-3dea-4dfa-87cb-0e026cc414ac,
abstract = {{<p>Renewable energy (RE) integrated with a hybrid electric-hydrogen storage system (HEHSS) is considered a promising solution for data centers to increase their energy efficiency and decrease their greenhouse emissions. This paper proposes a forecasting based hierarchical energy management strategy for a data center with wind energy, tidal energy and HEHSS, which is taken as a microgrid. A multi-population genetic algorithm-based day-ahead scheduling is used as the first layer and a rolling predictive control-based real-time dispatching is added as a second layer to minimize the degradation costs of HEHSS and maximize the revenue of the electricity and hydrogen trading. Also, a long-short-term-memory-based forecasting framework is developed to forecast power generation and load demand.</p>}},
author = {{Li, Huan and Alakula, Mats and Gualous, Hamid}},
booktitle = {{IECON Proceedings (Industrial Electronics Conference)}},
isbn = {{9798350331820}},
keywords = {{Energy management; Energy storage; Forecasting; Hierarchical systems; Hydrogen; Optimization methods}},
language = {{eng}},
publisher = {{IEEE Computer Society}},
title = {{A Forecasting Based Hierarchical Energy Management for Sustainable Data Centers}},
url = {{http://dx.doi.org/10.1109/IECON51785.2023.10312227}},
doi = {{10.1109/IECON51785.2023.10312227}},
year = {{2023}},
}