Machine Learning-assisted Dynamics-Constrained Day-Ahead Energy Scheduling
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866908886411247616 |
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| author | Tuo, Mingjian Li, Xingpeng Van Hentenryck, Pascal |
| author_facet | Tuo, Mingjian Li, Xingpeng Van Hentenryck, Pascal |
| contents | TThe rapid expansion of inverter-based resources, such as wind and solar power plants, will significantly diminish the presence of conventional synchronous generators in fu-ture power grids with rich renewable energy sources. This transition introduces in-creased complexity and reduces dynamic stability in system operation and control, with low inertia being a widely recognized challenge. However, the literature has not thoroughly explored grid dynamic performance associated with energy scheduling so-lutions that traditionally only consider grid steady-state constraints. This paper will bridge the gap by enforcing grid dynamic constraints when conducting optimal energy scheduling; particularly, this paper explores locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate substantial inertia reductions. This paper introduces a machine learning-assisted RoCoF-constrained unit commit-ment (ML-RCUC) model designed to ensure RoCoF stability after the most severe generator outage while maintaining operational efficiency. A graph-informed NN (GINN)-based RoCoF predictor is first trained on a high-fidelity simulation dataset to track the highest locational RoCoF, which is then reformulated as mixed-integer linear programming constraints that are integrated into the unit commitment model. Case studies, by solving the optimization problem ML-RCUC and validating its solutions with time-domain simulations, demonstrate that the proposed method can ensure loca-tional RoCoF stability with minimum conservativeness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_02650 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine Learning-assisted Dynamics-Constrained Day-Ahead Energy Scheduling Tuo, Mingjian Li, Xingpeng Van Hentenryck, Pascal Systems and Control TThe rapid expansion of inverter-based resources, such as wind and solar power plants, will significantly diminish the presence of conventional synchronous generators in fu-ture power grids with rich renewable energy sources. This transition introduces in-creased complexity and reduces dynamic stability in system operation and control, with low inertia being a widely recognized challenge. However, the literature has not thoroughly explored grid dynamic performance associated with energy scheduling so-lutions that traditionally only consider grid steady-state constraints. This paper will bridge the gap by enforcing grid dynamic constraints when conducting optimal energy scheduling; particularly, this paper explores locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate substantial inertia reductions. This paper introduces a machine learning-assisted RoCoF-constrained unit commit-ment (ML-RCUC) model designed to ensure RoCoF stability after the most severe generator outage while maintaining operational efficiency. A graph-informed NN (GINN)-based RoCoF predictor is first trained on a high-fidelity simulation dataset to track the highest locational RoCoF, which is then reformulated as mixed-integer linear programming constraints that are integrated into the unit commitment model. Case studies, by solving the optimization problem ML-RCUC and validating its solutions with time-domain simulations, demonstrate that the proposed method can ensure loca-tional RoCoF stability with minimum conservativeness. |
| title | Machine Learning-assisted Dynamics-Constrained Day-Ahead Energy Scheduling |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2309.02650 |