Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.

Several issues have been risen due to the recent vast installations of renewable energy sources (RESs) instead of fossil fuel sources in addition to the replacement of electric vehicles (EVs) for fuel-powered vehicles. Mitigating frequency deviations and tie-line power fluctuations has become driving challenge for the control design of interconnected power systems. RESs represent continuously varying power generators due to their nature and dependency on the environmental conditions. In this context, this article presents a new modified hybrid fractional order controller for load frequency and EVs control in interconnected power systems. The new controller combines the benefits of two widely employed fractional order controllers, including the FOPID and TID controllers. In addition, a new practical application of recent artificial ecosystem optimization (AEO) method has been proposed in this article for determining simultaneously the optimum controller parameters. The proposed controller and optimization method are validated on two areas interconnected power system with different types of RESs and with considering the natural characteristics of sources, EVs and load variations. Obtained simulation results verify the superior performance of the proposed controller and optimization method for achieving high mitigation of frequency fluctuations and tie-line power deviations, increased robustness, enhanced system stability over a wide range of parameters uncertainty and fast response during transients.INDEX TERMS Artificial ecosystem optimization, electric vehicles (EVs), fractional order controller, load frequency control, renewable energy sources.

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Energy transition from traditional generation sources into new renewable energy generation has become essential for facing climate changes. However, increased penetration levels of renewable energy sources (RESs) make power systems subjected to low inertia problems. Moreover, the continuously growing numbers of electric vehicles (EVs) have made the substantial need for their contribution in power systems. Therefore, this paper proposes a combined fractional-order controller using the parallel combination of tiltintegral-derivative with filter (TIDF) and hybrid fractional-order (HybFO) controllers for robust frequency regulation in interconnected power systems. The proposed controller is advantageous in combining the merits of two fractional-order controllers that result in more robust and effective load frequency control (LFC) at wide range and different types of disturbances. Furthermore, a new application of marine predator optimization algorithm (MPA) is proposed for simultaneously determining the optimum controller parameters in the different power system areas. The existing EVs contribute in performing additional functionality in power systems. EVs help in damping out the frequency and tie-line power oscillations in the proposed work. The two-area interconnected power system is selected as a case study with the installed photovoltaic (PV), and wind generations in addition to distributed EVs among areas. The obtained results show the superiority and suitability of the proposed controller over the traditional controllers in the literature. Additionally, the effectiveness of the MPA is validated and compared with recent meta-heuristic optimization algorithms.INDEX TERMS Electric vehicles (EVs), interconnected power systems, load frequency control (LFC), marine predator optimization algorithm (MPA), renewable energy sources (RESs).