Levy Flight and Fitness Distance Balance-Based Coyote Optimization Algorithm for Effective Automatic Generation Control of PV-Based Multi-Area Power Systems

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 Delta f(1), Delta f(2) and Delta P-tie under varying load conditions and perturbed system parameters.