It is already established that the renewable integration effects to the power system are nonzero and become more important with large penetrations. Thereby, the impacts of renewable energy sources (RESs) after integration are studied in this work to stabilize grid frequency of the studied test power system model. Initially, the two-area power system model is studied as the test system. The purpose is to show the tuning efficiency of non-conventional quasi-oppositional dragonfly algorithm (QODA) algorithm as compared to conventional way of tuning technique. It is showed that QODA algorithm is quite effective to find the optimal parameters of proportional–integral–derivative (PID) controller in load frequency control performance. Further, the three-area power system model integrated with RESs is studied. The work done here is to study the impacts of wind turbine generation, solar thermal power generation and solar photovoltaic on system frequency oscillations. The PID controller is employed as the supplementary control task, and its parameters are tuned by QODA algorithm. The integral of time absolute error is chosen as the objective function, and further performance indices are determined at the end of the execution of the program to examine the performance of the designed QODA-based PID controller. Following the integration of RESs, the impacts on frequency deviation through simulation results are also presented. The simulation results showed that the RESs are quite effective in regulating the power system frequency deviation understudied.

Hybridising of different optimisation techniques provides a scope to improve global exploration capability of the resulting method. In this study, an enhanced differential evolution (DE) algorithm, called hybrid Taguchi‐differential evolution (TDE) algorithm is proposed to solve power system state estimation problem as an optimisation problem. TDE combines the positive properties of the Taguchi's method to the classical DE algorithm for improving the accuracy and reliability of state estimation problem. The systematic reasoning ability of the Taguchi method is incorporated after crossover operation of DE algorithm to obtain the potential chromosome, better convergence rate and subsequently, to improve the robustness of the results. The proposed method is tested on IEEE test bus systems along with two ill‐conditioned systems under different simulated conditions. The results reveal that solutions yield towards global optimum and it compares far better than conventional DE, particle swarm optimisation, gravitational search algorithm and weighted least square based state estimation techniques in terms of optimisation performance, solution quality and the statistical error analysis.