Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911151373156352 |
|---|---|
| author | Javadi, Amir Bahador Kargarian, Amin Naraghi-Pour, Mort |
| author_facet | Javadi, Amir Bahador Kargarian, Amin Naraghi-Pour, Mort |
| contents | The increasing penetration of renewable energy sources introduces significant uncertainty in power system operations, making traditional deterministic unit commitment approaches computationally expensive. This paper presents a machine learning surrogate modeling approach designed to reformulate the feasible design space of the two-stage stochastic unit commitment (TSUC) problem, reducing its computational complexity. The proposed method uses a support vector machine (SVM) to construct a surrogate model based on the governing equations of the learner. This model replaces the original 2|L| * |S| transmission line flow constraints, where |S| is the number of uncertainty scenarios and |L| is the number of transmission lines with |S| much less than |L|, with a significantly reduced set of 1 * |S| linear inequality constraints. The approach is theoretically grounded in the polyhedral structure of the feasible region under the DC power flow approximation, enabling the transformation of 2|L| line flow limit constraints into a single linear constraint. The surrogate model is trained using data generated from computationally efficient DC optimal power flow simulations. Simulation results on the IEEE 57-bus and 118-bus systems demonstrate SVM halfspace constraint accuracy of 99.72% and 99.88%, respectively, with TSUC computational time reductions of 46% and 31% and negligible generation cost increases (0.63% and 0.88% on average for IEEE 57- and 118-bus systems, respectively). This shows the effectiveness of the proposed approach for practical power system operations under renewable energy uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Javadi, Amir Bahador Kargarian, Amin Naraghi-Pour, Mort Systems and Control The increasing penetration of renewable energy sources introduces significant uncertainty in power system operations, making traditional deterministic unit commitment approaches computationally expensive. This paper presents a machine learning surrogate modeling approach designed to reformulate the feasible design space of the two-stage stochastic unit commitment (TSUC) problem, reducing its computational complexity. The proposed method uses a support vector machine (SVM) to construct a surrogate model based on the governing equations of the learner. This model replaces the original 2|L| * |S| transmission line flow constraints, where |S| is the number of uncertainty scenarios and |L| is the number of transmission lines with |S| much less than |L|, with a significantly reduced set of 1 * |S| linear inequality constraints. The approach is theoretically grounded in the polyhedral structure of the feasible region under the DC power flow approximation, enabling the transformation of 2|L| line flow limit constraints into a single linear constraint. The surrogate model is trained using data generated from computationally efficient DC optimal power flow simulations. Simulation results on the IEEE 57-bus and 118-bus systems demonstrate SVM halfspace constraint accuracy of 99.72% and 99.88%, respectively, with TSUC computational time reductions of 46% and 31% and negligible generation cost increases (0.63% and 0.88% on average for IEEE 57- and 118-bus systems, respectively). This shows the effectiveness of the proposed approach for practical power system operations under renewable energy uncertainty. |
| title | Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.10246 |