The main target of this paper is to allow renewable energy resources (RES) to participate effectively within hybrid micro grids via an optimal proportional integral- derivative (PID) controller. This paper proposes two techniques of optimal PID controllers in a hybrid renewable generation energy system. These techniques are particle swarm optimization (PSO) and lightning attachment procedure optimization (LAPO). The hybrid renewable generation energy system in this study includes a photovoltaic source, wind turbine, and battery storage, which are connected to a point of common coupling via DC/DC boost converters. The controller at the inverter consists of a current controller and voltage source controller, which results in three PID gains at each controller. In order to obtain the PID gains, a time domain objective function is formulated in terms of the voltage, and current errors. The obtained results with the individual advanced optimization LAPO and PSO algorithm are compared. The results display that the developed LAPO algorithms give better results compared to the conventional PSO at the input and output current, voltage, and power. All the results have been taken under several operating conditions of wind turbine (wind speed) and solar sun (changing irradiance and temperature).

本文的主要目标是允许可再生能源 (RES) 通过最佳比例积分微分 (PID) 控制器有效地参与混合微电网。本文提出了混合可再生能源发电系统中优化 PID 控制器的两种技术。这些技术是粒子群优化 (PSO) 和闪电附着程序优化 (LAPO)。本研究中的混合可再生能源系统包括光伏电源、风力涡轮机和电池存储，它们通过 DC/DC 升压转换器连接到公共耦合点。逆变器上的控制器由电流控制器和电压源控制器组成，这会在每个控制器上产生三个 PID 增益。为了获得PID增益，根据电压和电流误差制定时域目标函数。将获得的结果与单独的高级优化 LAPO 和 PSO 算法进行了比较。结果表明，与传统 PSO 相比，开发的 LAPO 算法在输入和输出电流、电压和功率方面提供了更好的结果。所有结果都是在风力涡轮机（风速）和太阳太阳（变化的辐照度和温度）的几种运行条件下获得的。