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The organic Rankine cycle solar–thermal power system is a promising concept for the distributed energy market. The present work investigates the automatic generation control of an autonomous hybrid energy system based on organic Rankine cycle solar–thermal technology. Any mismatch between the active power generation and consumption in the hybrid energy system manifests itself as a frequency deviation from the nominal system frequency. To confront the frequency excursion problem, the present work considers automatic generation control, in which the controllers’ parameters are optimized by using particle swarm optimization (PSO). The performance of each controller is compared with its genetic algorithm (GA)‐optimized counterpart with respect to its ability to capture the frequency variation in the proposed hybrid energy system. The simulation results reveal that the performance of the PSO‐optimized controller is better than its GA‐optimized counterpart in terms of settling time and overshoot in deviation of frequency.

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The aim of this paper is the tuning of a PI controller using PSO techniques for autonomous hybrid energy generation/energy storage system. The autonomous hybrid generation system consisting of wind turbine generators (WTG), solar photovoltaic (PV), diesel engine generators (DEG), fuel cells (FC), battery energy storage system (BESS), ultra capacitors ( UC) and aqua electrolyzer (AE) has been considered for simulation studies. The power system frequency deviates for sudden changes in load or generation or the both. The comparative performance of the controllers installed to alleviate this frequency deviation for different hybrid systems, is carried out using time domain simulation. In practice, PI controller is tuned manually which is difficult and time consuming. The computational intelligence has opened paths to a new generation of advanced process control. Here, PSO is used for optimization of controller gains of the proposed hybrid system. The simulation results demonstrate the effectiveness of the PSO based controller in terms of reduced settling time, overshoot and oscillations. The results are compared with conventional controller.