Abstract
Large scale wind power integration into the power grid will pose a serious threat to the frequency control of power system. If only Control Performance Standard (CPS) index is used as the evaluation standard of frequency quality, it will easily lead to short-term centralized frequency crossing, which will affect the effect of intelligent Automatic Generation Control (AGC) on frequency quality. In order to solve this problem, a multi-objective collaborative reward function is constructed by introducing a collaborative evaluation mechanism with multiple evaluation indexes. In addition, Negotiated W-Learning strategy is proposed to globally optimize the solution of the objective function from multi dimensions, it avoids the poor learning efficiency of the traditional Greedy strategy. The AGC control model simulation of standard two area interconnected power grid shows that the proposed intelligent strategy can effectively improve the frequency control performance and improve the frequency quality of the system in the whole-time scale.
Highlights
Automatic Generation Control (AGC) is an important means to realize the balance of active powerload supply and demand in the power system
In response to the above problems, this paper proposes an intelligent frequency control strategy for collaborative evaluation of multi-dimensional control standards
It is found that the curve almost remains at a stable and acceptable value in the later stage, which shows that the Negotiated W-Learning algorithm has approached the optimal CPS1 control strategy
Summary
Automatic Generation Control (AGC) is an important means to realize the balance of active powerload supply and demand in the power system. The North American Electric Reliability Council (NERC) proposed a new frequency evaluation performance index named Balancing Authority ACE Limits (BAAL), which is used to ensure the short-term frequency quality of the system by constraining the mean value of the frequency difference fluctuates in any 30 min not to exceed the limit. In response to the above problems, this paper proposes an intelligent frequency control strategy for collaborative evaluation of multi-dimensional control standards. This strategy constructs and introduces a collaborative reward function that considers the CPS index and the BAAL index in the multi-objective reinforcement learning algorithm. Simulation examples show that the proposed intelligent control strategy can effectively improve the overall frequency performance quality of the power system
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