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Under frequency load shedding aware unit commitment in island power systems

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Date
2025-09-01
Author
Sarvarizadeh Kouhpaye, Miad
Rajabdorri, Mohammad
Lobato Miguélez, Enrique
Sigrist, Lukas
Kazemtabrizi, Behzad
Troffaes, Matthias
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info:eu-repo/semantics/publishedVersion
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Abstract
 
 
The transition from fossil fuels to renewable energy sources in power systems has resulted in lower system inertia and deteriorated frequency response characteristics. The challenge becomes even more pronounced in island power systems, where low inertia and limited frequency control capacity are already issues. Despite the existing preventive measures to maintain frequency limits, under-frequency load-shedding (UFLS) remains a crucial corrective action. In small isolated power systems, even with high amounts of reserve, big outages will lead to inevitable UFLS due to scarcity of inertia and primary frequency control capacity. Using a data-driven regression tree method, this paper introduces a novel UFLS-aware unit commitment formulation that can accurately estimate the amount of resulting UFLS for every possible outage. The estimations are then utilized to relax the reserve requirement constraint and lower the operation costs. The proposed formulation enables co-optimizing operation costs and UFLS to minimize the total cost. Additionally, the response speed of generating units is considered, ensuring timely reserve delivery. The efficacy of the proposed method is demonstrated through simulations on the model of a Spanish island power system, highlighting potential reductions in operation costs and system security improvements.
 
URI
https:doi.org10.1016j.ijepes.2025.110884
http://hdl.handle.net/11531/103111
Under frequency load shedding aware unit commitment in island power systems
Tipo de Actividad
Artículos en revistas
ISSN
0142-0615
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Under-frequency load-shedding; Unit commitmentIsland power systems; Machine learning; Logistic regressionRegression tree
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