Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02159 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916467100876800 |
|---|---|
| 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 |