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In this paper, Levy flight and Fitness Distance Balance (FDB)-based coyote optimization algorithm (LRFDBCOA) is proposed to improve the performance of automatic generation control (AGC) of three different PV-based interconnected power systems. First, the achievement of the proposed algorithm is evaluated on the two-area PV-reheat thermal power system with PI controller, which is widely used in the literature, and the results are compared with the results obtained using genetic algorithm (GA), firefly algorithm (FA), and modified whale optimization algorithm (M-WOA) introduced in previous studies. Then, the control performance of the proposed algorithm is demonstrated on the newly constructed two-area PV-non-reheat thermal power system with governor dead band (GDB) and two-area PV-multi-source power system with electrolyser and fuel cell. As the costs of solar energy systems decrease, the microgrids containing these resources increase. Therefore, such a study will make an important contribution to the literature. For the AGC of these two systems, proportional–integral–derivative (PID), tilt-integral-derivative (TID) and fractional order PID (FOPID) controllers are used. Thus, both the performance of the proposed algorithm on AGC and the compatibility of the two newly tested systems with PID, TID and FOPID controllers are revealed. As a result, the proposed LRFDBCOA algorithm outperforms algorithms such as GA, FA, M-WOA and arithmetic optimization algorithm (AOA) in interconnected two-area power systems controlled by PID, TID and FOPID, and it shows superior control performance in terms of settling time, maximum/minimum peak value of Δ f 1 , Δ f 2 and Δ P tie under varying load conditions and perturbed system parameters. [ABSTRACT FROM AUTHOR]

In recent years, fractional order proportional-integral-derivative (FOPID) controllers have been applied in different areas in the academy due to their superior performance over conventional proportional-integral-derivative (PID) controllers. When the literature is reviewed, it has been observed that lack of studies that use swarm-based and multi-objective optimization algorithms together with FOPID controllers in frequency control of micro-grid. The load frequency control (LFC) problem is considered as two objectives in order to eliminate the complications that occur when only the frequency deviation is minimized. In our study, a method called MOGOA-FOPID in which both the frequency deviation and the control signal are minimized together for the frequency control in the microgrid is proposed. By using the multi-objective grasshopper optimization algorithm (MOGOA), both the frequency deviation and the control signal are minimized together, and thus, it is aimed to limit the battery capacity, reduce the flywheel jerk and avoid high diesel fuel consumption as well as an effective frequency control. In order to obtain a more realistic system, not only the photovoltaic (PV) solar and wind power but also the load demand is taken in a stochastic structure. Then, the results of the proposed MOGOA-FOPID are compared with the results of multi-objective genetic algorithm (MOGA)-based PID/FOPID and MOGOA-PID and its superiority is demonstrated. Finally, robustness tests of the system are performed under the perturbed parameters and outperform of MOGOA-FOPID over other methods is seen. [ABSTRACT FROM AUTHOR]