Abstract
The cost competitiveness of green hydrogen production via electrolysis
presents a significant challenge for its large-scale adoption. One potential
solution to make electrolyzers profitable is to diversify their products and
participate in various markets, generating additional revenue streams.
Electrolyzers can be utilized as flexible loads and participate in various
frequency-supporting ancillary service markets by adjusting their operating set
points. This paper develops a mixed-integer linear model, deriving an optimal
scheduling strategy for an electrolyzer providing Frequency Containment Reserve
(FCR) services in the Nordic synchronous region. Depending on the hydrogen
price and demand, results show that the provision of various FCR services,
particularly those for critical frequency conditions (FCR-D), could
significantly increase the profit of the electrolyzer.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm) |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2023 |
| ISBN (Electronic) | 978-1-6654-5556-5 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids - Glasgow, United Kingdom Duration: 31 Oct 2023 → 3 Nov 2023 |
Conference
| Conference | 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids |
|---|---|
| Country/Territory | United Kingdom |
| City | Glasgow |
| Period | 31/10/2023 → 03/11/2023 |
| Series | IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
|---|---|
| ISSN | 2474-2902 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
Keywords
- Electrolyzer
- Scheduling
- Frequency-supporting ancillary services
- Mixed-integer linear optimization
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