期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 32, 期 6, 页码 2483-2493出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.3006080
关键词
Automatic generation control; Power grids; Power system stability; Decision making; Heuristic algorithms; Neural networks; Power system dynamics; Automatic generation control (AGC); deep reinforcement learning (RL); incentive heuristic updating; optimized bias
类别
资金
- National Natural Science Foundation of China [51707102]
This article proposes a deep-reinforcement-learning-based three-network double-delay actor-critic control strategy for improving power grid control performance and achieving optimal coordinated control. Simulation studies demonstrate the strategy's excellent stability and learning ability.
As the conventional automatic generation control (AGC) is inadequate to deal with the strong random disturbance issues induced by the ever-increasing penetration of renewable energy to the power grids, this article proposes a deep-reinforcement-learning-based three-network double-delay actor-critic (TDAC) control strategy for AGC to handle the above problem, which is mainly developed by multiple neural networks to fit the system action strategies and evaluate the value. The proposed strategy can increase the exploration efficiency and the quality of AGC and improve the system control performance using the modified actor-critic (AC) method with incentive heuristic mechanism, while a novel iterative way of the value function is also used to reduce the bias of optimization effectively for achieving optimal coordinated control of the power grid. The simulations are provided in the work to show the control performance of the strategy. Compared with other smart methods, the simulation study demonstrates that TDAC has excellent exploratory stability and learning ability. Meanwhile, it also can improve the dynamic performance of the power system and achieve the regional optimal coordinated control.
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