This paper proposes a reliable approach-based Harris Hawks Optimizer (HHO) to evaluate the optimal parameters of the Proportional–Integral (PI) controller simulating load frequency control (LFC) incorporated in a multi-interconnected system with renewable energy sources (RESs). During the optimization process, the integral time absolute error (ITAE) of the frequency and tie-line power is handled as the objective function. The HHO is selected due to its ease and requirement of less controlling parameters. Two different interconnected power systems are investigated as test systems to demonstrate the robustness of the proposed controller based HHO by comparing to other optimizers and traditional controller. The considered two systems include, one comprises two interconnected area of thermal and photovoltaic (PV) plants while the other system has four plants of PV, wind turbine (WT), and two thermal plants considering governor dead-band (GDB) and generation rate constraint (GRC). Different cases of load disturbance are studied, and the obtained results via the HHO are compared to Sine Cosine Algorithm (SCA), Multi-verse optimizer (MVO), Antlion Optimizer (ALO), and Grey wolf optimizer (GWO) as well as traditional controller. Moreover, sensitivity analysis is performed by changing the system parameters in a range of $\pm$ 10% and the performance of the proposed HHO-LFC is evaluated. The obtained results confirmed the reliability and superiority of the proposed approach based HHO in designing LFC for the considered systems.

本文提出了一种基于可靠方法的哈里斯·霍克斯优化器（HHO），用于评估比例积分（PI）控制器的最优参数，该控制器模拟了包含可再生能源（RESs）的多互连系统中的负载频率控制（LFC）。在优化过程中，将频率和联络线功率的积分时间绝对误差（ITAE）视为目标函数。选择HHO的原因是其简便易行，并且需要较少的控制参数。研究了两个不同的互连电源系统作为测试系统，以通过与其他优化器和传统控制器进行比较来证明所提出的基于控制器的HHO的鲁棒性。所考虑的两个系统包括：一个由两个相互连接的热电厂和光伏（PV）厂区组成，另一个系统由四个PV厂，风力涡轮机（WT）和两个考虑调速器死区（GDB）和发电率约束（GRC）的热电厂组成。研究了不同情况下的负载扰动，并将通过HHO获得的结果与Sine Cosine算法（SCA），Multi-verse优化器（MVO），Antlion优化器（ALO）和Gray Wolf优化器（GWO）以及传统方法进行了比较控制器。此外，通过在以下范围内更改系统参数来执行灵敏度分析。多版本优化器（MVO），Antlion优化器（ALO）和灰太狼优化器（GWO）以及传统控制器。此外，通过在以下范围内更改系统参数来执行灵敏度分析。多版本优化器（MVO），Antlion优化器（ALO）和灰太狼优化器（GWO）以及传统控制器。此外，通过在以下范围内更改系统参数来执行灵敏度分析。$\pm$10％，并评估了拟议的HHO-LFC的性能。获得的结果证实了所提出的基于HHO的方法在为所考虑的系统设计LFC时的可靠性和优越性。