This paper proposes a combined feedback and feed-forward control system to support the frequency regulation of multi-area interconnected hybrid microgrids considering renewable energy sources (RESs). The proposed control system is based on a fractional-order proportional-integral-derivative-accelerated (FOPIDA) controller in the feed-forward direction and a fractional-order integral-derivative with a low-pass filter compensator (FOIDN) controller in the feedback direction, referred to as a FOPIDA-FOIDN controller. Moreover, the parameters of the proposed FOPIDA-FOIDN controller (i.e., twelve parameters in each area) are optimally tuned using a proposed hybrid of two metaheuristic optimization algorithms, i.e., hybrid artificial gorilla troops optimizer (AGTO) and equilibrium optimizer (EO), and this hybrid is referred to as HGTOEO. The robustness and reliability of the proposed control system are validated by evaluating its performance in comparison to that of other counterparts’ controllers utilized in the literature, such as PID, FOPID, and tilt integral derivative (TID) controller, under the different operating conditions of the studied system. Furthermore, the proficiency of the proposed HGTOEO algorithm is checked against other powerful optimizers, such as the genetic algorithm, Jaya algorithm, improved Jaya algorithm, multi-verse optimizer, and cost-effective multi-verse optimizer, to optimally design the PID controller for the load frequency control of the studied two-area interconnected microgrid. The MATLAB simulation results demonstrate the viability and dependability of the proposed FOPIDA-FOIDN controller based on the HGTOEO algorithm under a variety of load perturbations and random production of RESs.

Frequency instability in microgrids (MGs) resulted from the use of Inverter-based generators which lack natural inertia. To overcome this difficulty, this study presents a progressed virtual inertia controller (VIC) that is integrated with a battery energy storage system and has both damping and virtual inertia control loops. With the mounting penetration of renewable energy, an innovative approach that influences Progressive VIC in conjunction with controlled plug-in electric vehicles (PEVs) is obtainable. PEVs' battery storage capacity is used to supply extra frequency regulation services, serving as dispersed energy storage resources to lessen grid frequency variations. The proposed approach employs an Active Disturbance Rejection Controller (ADRC) and uses the Electric Eel Foraging Optimization (EEFO) technique for optimal parameter determination. A frequency control model for microgrids using this progressive VIC was evaluated under numerous operating circumstances and disturbances to reflect variable load and renewable energy generation. The outcomes validate that the ADRC outperforms traditional controllers such as PID, FO-PID, TID, and fuzzy PID in maintaining frequency stability. Also, enhanced VIC and PEVs improve the MG's stability compared to other methods considered. This innovative approach offers a viable remedy for upcoming microgrid designs by improving microgrid frequency stability and lowering the risk of frequency instability.

Modeling the photovoltaic (PV) generating unit is one of the most important and crucial tasks when assessing the accurate performance of the PV system in power systems. The modeling of the PV system refers to the assigning of the optimal parameters of the PV’s equivalent circuit. Identifying these parameters is considered to be a complex optimization problem, especially with the deviation of the solar irradiance and the ambient temperature. In this regard, this paper proposes a novel hybrid multi-population gorilla troops optimizer and beluga whale optimization (HMGTO-BWO) model to evaluate the optimal parameters of the PV cell/panel; it is based on a multi-population strategy to improve its diversity and to avoid the stagnation of the conventional GTO. The BWO explorative and exploitative powers, which are based on synchronized motion and Lévy flight, are used. The suggested HGTO-BWO is implemented to minimize the root mean square error (RMSE) between the simulated and measured data for each cell/panel represented by a double diode model (DDM) and triple diode model (TDM). The proposed HGTO-BWO is investigated according to the standard and CEC-2019 benchmark functions, and the obtained results are compared with seven other optimization techniques in terms of statistical analysis, convergence characteristics, boxplots, and the Wilcoxon rank sum test. The minimum obtained RMSE values of the PVW 752 cell were 2.0886 × 10⁻⁴ and 1.527 × 10⁻⁴ for the DDM and TDM, respectively. Furthermore, the minimum fetched fitness value for the STM6-40/36 modules was 1.8032 × 10⁻³. The obtained results proved the effectiveness and preference of the suggested HGTO-BWO in estimating the parameters of the PV modules.

Recently, renewable distributed generation (DG) systems have emerged to improve the power system security and reliability while reducing the environmental incompatibility of fossil fuel power plants. DG could operate either as a standalone and/or as a grid‐connected power system. The interfacing circuits of DG and their controls have to comply with utility quality regulations and afford a seamless transition from online to offline and vice versa. This article advises a simple and robust control scheme for a modular multilevel converter (MMC) that interfaces with a photovoltaic (PV) generator. The proposed control method ensures high‐quality output voltage/current waveform in online and/or offline operating modes. Moreover, the proposed control provides a seamless and swift transition from standalone to grid‐connected and vice versa. The exhibited control scheme is simulated with MATLAB/Simulink. Real‐time simulator OPAL‐RT OP4510 is used to verify the displayed simulation results. The simulation results determine the accomplishment of the proposed control technique for a seamless transition from the grid‐connected mode to the standalone mode and vice versa via MMC.