The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Challenges of renewable energy penetration on power system flexibility: A survey
Open accessAbstract
Flexibility in power systems is ability to provide supply-demand balance, maintain continuity in unexpected situations, and cope with uncertainty on supply-demand sides. The new method and management requirements to provide flexibility have emerged from the trend towards power systems increasing renewable energy penetration with generation uncertainty and availability. In this study, the historical development of power system flexibility concept, the flexible power system characteristics, flexibility sources, and evaluation parameters are presented as part of international literature. The impact of variable renewable energy sources penetration on power system transient stability, small-signal stability, and frequency stability are discussed; the studies are presented to the researchers for further studies. Moreover, flexibility measurement studies are investigated, and methods of providing flexibility are evaluated.
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Cites
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Demand side management: benefits and challenges
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Impact of increased penetration of photovoltaic generation on power systems
Eftekharnejad, S., Vittal, V., Heydt, Keel, B., & Loehr, J. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2216294 · Cited by 832
The Relevance of Inertia in Power Systems
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Wind and solar energy curtailment: A review of international experience
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Cited by
Al-Shetwi, A. Q. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153645 · Cited by 703
Exploiting demand-side flexibility: State-of-the-art, open issues and social perspective
D’Ettorre, F., Banaei, M., Ebrahimy, R., Pourmousavi, S. A., Blomgren, E. M. V., Kowalski, J., Bohdanowicz, Z., Łopaciuk-Gonczaryk, B., Biele, C., & Madsen, H. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112605 · Cited by 160
Efficiency and optimal load capacity of E-Fuel-Based energy storage systems
Tsiklios, C., Schneider, S., Hermesmann, M., & Müller, T. E. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100140 · Cited by 48
Lechl, M., Fürmann, T., de Meer, H., & Weidlich, A. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113570 · Cited by 34
Multi-Agent Deep Reinforcement Learning for Scheduling of Energy Storage System in Microgrids
Jung, S.-W., An, Y.-Y., Suh, B., Park, Y., Kim, J., & Kim, K.-I. (2025) · Mathematics · doi:10.3390/math13121999 · Cited by 28
Metadata
- Authors
- Impram, S., Varbak Nese, S., & Oral, B.
- Venue
- Energy Strategy Reviews
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 31
- Pages
- 100539
- ISSN
- 2211-467X (ISSN-L)
- OpenAlex
- W3049476145
- Cited by
- 939
- References
- 43
- FWCI
- 27.93
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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