Distributed Stochastic ACOPF Based on Consensus ADMM and Scenario Reduction
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916467100876800 |
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| author | Yang, Shan Zhu, Yongli |
| author_facet | Yang, Shan Zhu, Yongli |
| contents | This paper presents a Consensus ADMM-based modeling and solving approach for the stochastic ACOPF. The proposed optimization model considers the load forecasting uncertainty and its induced load-shedding cost via Monte Carlo sampling. The sampled scenarios are reduced using a clustering method combined with simultaneous backward reduction techniques to reduce the computational complexity. The proposed approach is tested on two IEEE systems, achieving about 2% cost reduction and more than 15 times lower reliability index in stochastic load settings compared to the baseline approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed Stochastic ACOPF Based on Consensus ADMM and Scenario Reduction Yang, Shan Zhu, Yongli Systems and Control This paper presents a Consensus ADMM-based modeling and solving approach for the stochastic ACOPF. The proposed optimization model considers the load forecasting uncertainty and its induced load-shedding cost via Monte Carlo sampling. The sampled scenarios are reduced using a clustering method combined with simultaneous backward reduction techniques to reduce the computational complexity. The proposed approach is tested on two IEEE systems, achieving about 2% cost reduction and more than 15 times lower reliability index in stochastic load settings compared to the baseline approach. |
| title | Distributed Stochastic ACOPF Based on Consensus ADMM and Scenario Reduction |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2411.02159 |