Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911760838033408 |
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| author | Wang, Xiaoting Liu, Jingyu Wang, Xiaozhe |
| author_facet | Wang, Xiaoting Liu, Jingyu Wang, Xiaozhe |
| contents | This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation. The proposed method can efficiently and accurately estimate the probabilistic characteristics of AC-OPF solutions. An adaptive domain partition strategy and expansion coefficient calculation algorithm are integrated to enhance its performance. Numerical studies on a 9-bus system demonstrate that the proposed ASSE method offers accurate and fast evaluations compared to the Monte Carlo simulations. A comparison with a sparse polynomial chaos expansion method, an existing surrogate model, further demonstrates its efficacy in accurately assessing the responses with strongly local behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10498 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model Wang, Xiaoting Liu, Jingyu Wang, Xiaozhe Systems and Control This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation. The proposed method can efficiently and accurately estimate the probabilistic characteristics of AC-OPF solutions. An adaptive domain partition strategy and expansion coefficient calculation algorithm are integrated to enhance its performance. Numerical studies on a 9-bus system demonstrate that the proposed ASSE method offers accurate and fast evaluations compared to the Monte Carlo simulations. A comparison with a sparse polynomial chaos expansion method, an existing surrogate model, further demonstrates its efficacy in accurately assessing the responses with strongly local behaviors. |
| title | Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2401.10498 |