A Fast Power Market Clearing Method Based on Active Constraints Identification by Deep Learning
The day-ahead power market clearing needs to solve the security-constrained economic dispatch (SCED) problem. Although the SCED problem is a linear programming (LP) model, the model size is too large to be effectively solved because the massive security constraints in the N–1 scenarios need to be co...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2020-09-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202005028 |
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| Summary: | The day-ahead power market clearing needs to solve the security-constrained economic dispatch (SCED) problem. Although the SCED problem is a linear programming (LP) model, the model size is too large to be effectively solved because the massive security constraints in the N–1 scenarios need to be considered. Therefore, this paper proposes a fast clearing method for day-ahead power market based on the deep neural network. Firstly, a computation framework for SCED model based on deep neural network is designed, and embeds deep learning technology into the existing day-ahead power market clearing framework, which can effectively improve the solving speed of the SCED model without compromising precision. Secondly, a deep learning strategy is proposed for identification of active constraint sets, which can provide technical support for deep neural networks to effectively identify the active constraints of SCED from two aspects: feature vector design and efficient processing of the results of deep neural network. Finally, the effectiveness of the proposed method is verified in the IEEE 30 standard test system with renewable energy sources. The deep neural network is used to pre-identify the active constraints of the SCED model, which is beneficial to reduce the complexity of the model and improve the calculation efficiency of market clearing. |
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| ISSN: | 1004-9649 |