期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 13, 期 1, 页码 231-240出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2021.3107848
关键词
Convolutional neural networks; Wind power generation; Matrix decomposition; Security; Upper bound; Uncertainty; State of charge; Deep learning; security-constrained unit commitment
资金
- Research Grants Council of Hong Kong [14208017]
- National Natural Science Foundation of China [62101336]
This paper proposes a new model for scenario-based security-constrained unit commitment (SCUC) with BESSs and solves it using a mixed-integer programming and convolutional neural network algorithm. The algorithm eliminates the need for explicitly considering the scenario-based security constraints, greatly reducing computational complexity and achieving promising results.
This paper proposes a new model of scenario-based security-constrained unit commitment (SCUC) with BESSs. By formulating such a model as a mixed-integer programming (MIP) problem, we can obtain the optimal control strategy of units and BESSs to reduce the operating cost. To solve this MIP with the proposed model, we propose a new learning-based approach to tackle the SCUC problem. The proposed convolutional neural network (CNN)-based SCUC algorithm (CNN-SCUC) has two main stages. First, CNN-SCUC trains a CNN to obtain solutions to the binary variables corresponding to unit commitment decisions. Then, the continuous variables corresponding to unit power outputs are solved by a small-scale convex optimization problem. In contrast to existing work, CNN-SCUC eliminates the need of explicitly considering the scenario-based security constraints in the optimization problem, and thus greatly reduces the computational complexity. The average gap to the optimal solution is as small as 0.0267%. The algorithm is also scalable in the sense that the computational time is reduced from about 1236.32 seconds to 0.8379 seconds in a 10-unit and 200-scenario system. Besides, the computation time remains almost constant when the number of scenarios increases. Case studies show that compared with the traditional scenario-based SCUC model, more than 4.70% operating cost reduction is achieved by incorporating BESSs in the system.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据