Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning

Rajabdorri, Mohammad; Kazemtabrizi, Behzad; Troffaes, Matthias; Sigrist, Lukas; Lubato, Enrique

Abstract

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First, a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.

Citation

Rajabdorri, M., Kazemtabrizi, B., Troffaes, M., Sigrist, L., & Lubato, E. (2023). Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning. Sustainable Energy, Grids and Networks, 36, Article 101161. https://doi.org/10.1016/j.segan.2023.101161

• Journal Article Type: Article
• Acceptance Date: Aug 24, 2023
• Online Publication Date: Sep 4, 2023
• Publication Date: 2023-12
• Deposit Date: Sep 11, 2023
• Publicly Available Date: Sep 11, 2023
• Journal: Sustainable Energy, Grids and Networks
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 36
• Article Number: 101161
• DOI: https://doi.org/10.1016/j.segan.2023.101161
• Public URL: https://durham-repository.worktribe.com/output/1726296