Analytic Deep Learning-Based Surrogate Model for Operational Planning With Dynamic TTC Constraints

The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time monitoring need. This paper develops a more comput...

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Bibliographic Details
Published in:IEEE transactions on power systems 2021-07, Vol.36 (4), p.3507-3519
Main Authors: Qiu, Gao, Liu, Youbo, Zhao, Junbo, Liu, Junyong, Wang, Lingfeng, Liu, Tingjian, Gao, Hongjun
Format: Article
Language:English
Subjects:
analytic surrogates-assisted solver
Computational efficiency
Computational modeling
Computing time
Constraints
Corridors
Deep learning
Hessian matrices
interior point method
learning algorithms
operation planning
Optimization
Power system planning
Strategic planning
System effectiveness
Total transfer capability
Transient analysis
Transient stability
Wind power
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Summary:The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time monitoring need. This paper develops a more computationally efficient approach to address that challenge via the analytical deep learning-based surrogate model. The key idea is to resort to deep learning for developing a computationally cheap surrogate model to replace the original time-consuming differential-algebraic constraints related to TTC. However, the deep learning-based surrogate model introduces implicit rules that are difficult to handle in the optimization process. To this end, we derive the Jacobian and Hessian matrices of the implicit surrogate models and finally transfer them into an analytical formulation that can be easily solved by the interior point method. Surrogate modeling and problem reformulation allow us to achieve significantly improved computational efficiency and the yielded solutions can be used for operational planning. Numerical results carried out on the modified IEEE 39-bus and 68-bus systems demonstrate the effectiveness of the proposed method in dealing with complicated TTC constraints while balancing the computational efficiency and accuracy.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3041866