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  • https://doi.org/10.1109/ddcls55054.2022.9858443Copy DOI Icon

Learning Optimization for Dispatch of Interregional Power Grid under Uncertain Environment

  • Aug 3, 2022
  • Hao Tang +3 more
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Abstract

This study investigates the dispatch problem in interregional power grids with uncertain renewable energy sources and loads. A knowledge-transfer-based hierarchical learning optimization method is designed to search for the optimal dispatch strategy. First, the model of various sources, loads, and tie-lines are established according to each components characteristics in the system. The dispatch process of an interregional power grid is correspondingly divided into tie-line dispatch in the upper layer and both generator units and flexible load dispatch in the lower layer. The dispatch optimization problems of both layers are formulated with a hierarchical Markov decision process model. Finally, a learning optimization method is adopted to obtain the dispatch strategy, in which a pre-learning technique is incorporated. A case study is performed to validate the effectiveness of the proposed method, and the simulation results show that the pre-learning technique improves the learning speed and reduces the cost of relearning.

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