Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method.

中文翻译：

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高参与度可再生能源和储能系统的电网基于双目标优化的频率调节

连接到电网的大规模可再生能源给电力系统的自动发电控制（AGC）带来了新的问题和挑战。为了提高AGC的动态响应性能，建立了具有高参与度的储能资源（ESR）的双目标互补控制（BOCC），同时将总功率偏差最小化并将调节里程支付减至最小。为了解决这个问题，采用强度帕累托进化算法快速获取BOCC的高质量帕累托前沿。基于熵权法（EWM），设计了灰色目标决策理论，以选择一种兼顾运营经济性和电能质量的折衷方案。终于，