Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 11, Issue 4, Pages 2417-2426Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2019.2958361
Keywords
Automatic generation control; Power grids; Heuristic algorithms; Real-time systems; Power system dynamics; Frequency control; Neural networks; Deep reinforcement learning; automatic gene-ration control; action discovery strategy; multi-agent DDQN-AD method
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Funding
- National Natural Science Foundation of China [51707102]
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The widely adoption of distributed renewable energy sources (DREs) effectively reduces carbon emission and beat atmospheric haze in developing countries. However, random disturbance issues emerge in power grids with DREs when applying traditional centralized automatic generation control (AGC) strategies. Therefore, a multi-agent distributed control strategy is proposed for AGC in this article, which is mainly based on the concept of deep reinforcement learning, and developed by the strategy of action discovery. Moreover, area control error and the amount of carbon emission are employed in reward functions to obtain optimal solutions in the implementing process of the proposed strategy. Simulations are provided in the work to show the effectiveness of the strategy, while comparisons are also offered, where the simulating results obtained by two other intelligent AGC algorithms are used as references, according to which, the superiority of the proposed strategy is confirmed.
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