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Abstract In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

Abstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

Abstract The advancement in technology has proceeded exceptionally that the stability of the power system becomes a major challenge. The mismatch of generation and demand creates frequency perturbation that need to be curbed under the limits to avoid power breakdown. Owing to this, the paper focuses to counter the ever‐increasing challenges to the power system much like load and fault disruption that causes frequency deterioration and breakdown. A novel master‐slave (MS) control design is formulated for a hybrid interconnected power system in which fractional‐order‐proportional‐integral‐derivative (FOPID) controller act as a master and model predictive controller (MPC) act as a slave controller. The gain parameters of the master controller are tuned by salp swarm algorithm (SSA) which act as a secondary loop controller, while slave act as a primary loop controller. The MS formation has resultantly removed the steady‐state error associated with MPC and help to mitigate the LFC problem. Furthermore, the superiority of the controller is tested under load and fault disruption in a multi‐area power system. The designed controller is tested under four states (1) abrupt load variation, (2) sinusoidal load deviation, (3) sensitivity analysis and (4) IEEE 39‐bus test system analysis. The disclosed simulation results depicts that the proposed control scheme has a dynamic performance as compared to other controllers.