期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 34, 期 2, 页码 1653-1656出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2018.2881359
关键词
Continuous action search; deep reinforcement learning; load frequency control; stochastic power system
资金
- Singapore Ministry of Education under an Academic Research Fund Tier 1 Project
- Nanyang Assistant Professorship from Nanyang Technological University
This letter proposes a data-driven, model-free method for load frequency control (LFC) against renewable energy uncertainties based on deep reinforcement learning (DRL) in continuous action domain. The proposed method can nonlinearly derive control strategies to minimize frequency deviation with faster response speed and stronger adaptability for unmolded system dynamics. It consists of offline optimization of LFC strategies with DRL and continuous action search, and online control with policy network where features are extracted by stacked denoising auto-encoders. Numerical simulations verify the effectiveness and advantages of proposed method over existing approaches.
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