This paper proposes an intelligent Deep Learning (DL) based approach for data-drivensecurity-constrainedunitcommitment (DD-SCUC) decision-making. The proposed approach includes data pre-processing and a two-stage d...
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This paper proposes an intelligent Deep Learning (DL) based approach for data-drivensecurity-constrainedunitcommitment (DD-SCUC) decision-making. The proposed approach includes data pre-processing and a two-stage decision-making process. Firstly, historical data is accumulated and pre-processed. Then, the DD-SCUC model is created based on the Gated Recurrent unit-Neural Network (GRU-NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two-stage decision-making process outputs the decision results based on various applications and scenarios. This approach has self-learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118-bus test system and a real power system from China showed that compared with deterministic Physical-Model-driven (PMD)-SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.
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