Deep Learning to Optimize: Security-Constrained Unit Commitment With Uncertain Wind Power Generation and BESSs
| Autor: | Tong Wu, Shuoyao Wang, Ying Jun Zhang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Mathematical optimization
Optimization problem Artificial neural network Computational complexity theory Renewable Energy Sustainability and the Environment business.industry Computer science Deep learning Optimal control Power system simulation Convex optimization Artificial intelligence business Operating cost |
| Zdroj: | IEEE Transactions on Sustainable Energy. 13:231-240 |
| ISSN: | 1949-3037 1949-3029 |
| Popis: | 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 solve the security-constrained unit commitment (SCUC) problem. The proposed deep neural network (DNN)-based SCUC algorithm (DSCUC) has two main stages. First, DSCUC trains a DNN to obtain the binary unit commitment decisions. Then, the continuous variables corresponding to unit power outputs are solved by a small-scale convex optimization problem. In contrast to the existing work, the DSCUC algorithm 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 0.0964%. The algorithm is scalable in the sense that the computational time is reduced from about 1300 seconds to about 0.7 second in a 10-unit and 200-scenario system. Besides, the computation time remains almost constant when the number of scenarios increases. More than 4.70% operating cost is reduced by incorporating BESSs in the system. |
| Databáze: | OpenAIRE |
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