Dynamic Optimized Allocation Policy for AGC Regulation Capacity Based on Reinforcement Q-Learning and Fuzzy Multiple Objectives
The unit characteristics can't be fully described in the multi-objective optimized allocation of AGC regulation capacity; therefore,this paper proposes the optimal allocation approach based on the reinforcement Q-learning.With AGC system,the "uncertain stochastic system",combined with the ACE adjusting dead zone and CPS evaluation standard,the Markov decision process (MDP) model is established for optimized allocation of AGC regulation power; the Q-learning approach is introduced to study MDP's optimal value function.The simulation results show that the proposed allocation strategy can adapt to the changing requirement of grid environment.
AGCreinforcement Q-learning algorithmdynamic optimized allocation of regulation capacity
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