Machine learning analysis for a flexibility energy approach towards renewable energy integration with dynamic forecasting of electricity balancing power
Abstract
One of the most important instruments to be able to provide the needed level of flexibility in the electricity system supporting renewable energy integration are balancing markets. We propose a dynamic approach of balancing procurement using machine learning algorithms. We apply a simulation for a Dynamic Day-Ahead Dimensioning Model to the Austrian delta control area. By using public data on renewables, generation and load we show that dynamic dimensioning and procurement of balancing power enables savings in comparison to static dimensioning and procurement with the same level of security.
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Dynamic scheduling and (safe) reinforcement learning could also be used to balance the electric grid in real time; some electricity system operators have started to pilot similar methods at small, test case-based scales.
“Dynamic scheduling [225,546] and (safe) reinforcement learning (RL) could also be used to balance the electric grid in real time; in fact, some electricity system operators have started to pilot similar methods at small, test case-based scales [520].”
From Tackling climate change with machine learning (2023) · cites this paper as [225]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Essl, A., Ortner, A., Haas, R., & Hettegger, P.
- Affiliations
- TU Wien (AT)
- AIT Austrian Institute of Technology GmbH (AT)
- Venue
- 2017 14th International Conference on the European Energy Market (EEM)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 1-6
- OpenAlex
- W2735389194
- Cited by
- 15
- References
- 21
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 3 · 2019: 3 · 2017: 1
- FWCI
- 1.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on the European Energy Market (EEM) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- balancing power, balancing market, renewable energy integration, power system flexibility, machine learning forecasting, power system security
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