The performance of load frequency control (LFC) for isolated multiple sources of electric power-generating units with a proportional integral derivative (PID) controller is presented. A thermal, hydro, and gas power-generating unit are integrated into the studied system. The PID controller is proposed as a subordinate controller to stabilize system performance when there is a sudden demand on the power system. The particle swarm optimization (PSO) algorithm is used to obtain optimal gain values of the proposed PID controller. Various cost functions, mainly integral time absolute error (ITAE), integral absolute error (IAE), integral squared error (ISE), and integral time squared error (ITSE) were used to optimize controller gain parameters. Furthermore, the enhancement of the PSO technique is proven by the performance comparison of conventional, differential evolution (DE) algorithm- and genetic algorithm (GA)-based PID controllers for the same system. The results show the PSO-PID controller delivers a faster settled response and the percentage improvement of the proposed technique over the conventional method is 79%, over GA is 55%, and over DE is 24% in an emergency in a power system.

This paper proposes the particle swarm optimization (PSO) technique-based proportional integral derivative (PID) controller suggested for frequency regulation of a micro grid (MG) system. MG system integrates with thermal power generating units, renewable energy sources (RES) like photovoltaic (PV), wind energy generators (WTG), and Energy storage systems (ESS) such as fuel cell (FC) and battery energy storage system (BESS). Indentifying the supremacy of proposed technique-based controller and supremacy is examined with three objective functions (integral absolute error [IAE], integral time absolute error [ITAE], and integral squared error [ISE]). The results of the system are compared with conventional PID controller results. From the comparison, it is clearly evident that PSO-PID controller gives better performance over conventional methods in terms of various time domain specific parameters such as settling time, peak overshoot, and undershoot. In both methods, ITAE objective function used controller produce more effective response in MG under sudden load demand situation.

In this artwork, an application of the metaheuristic optimization approach designed Proportional - Integral - Double Derivative (PIDD) controller is recommended to implement Automatic Generation Control (AGC) / Load Frequency Control (LFC). It consists of two areas integrated thermal power plants with renewable energy sources. The Ant Colony Optimization (ACO) approach is recommended to reap the benefit parameters of the controller inclusive of Proportional gain (Kp), Integral advantage (KI), and Double- Derivative gain (KDD). The Integral of Time Absolute Error (ITAE) is considered as the objective function for ACO. Performance of the proposed controller (ACO - PIDD) with ACO-PID and Conventional PID is in comparison and it proves the improvement as for as time-domain parameters in the proposed framework.