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Over the last decade, dual active bridge (DAB) converters have become critical components in high-frequency power conversion systems. Recently, intensive efforts have been directed at optimizing DAB converter design and control. In particular, several strategies have been proposed to improve the performance of DAB control systems. For example, fractional-order (FO) control methods have proven potential in several applications since they offer improved controllability, flexibility, and robustness. However, the FO controller design process is critical for industrializing their use. Conventional FO control design methods use frequency domain-based design schemes, which result in complex and impractical designs. In addition, several nonlinear equations need to be solved to determine the optimum parameters. Currently, metaheuristic algorithms are used to design FO controllers due to their effectiveness in improving system performance and their ability to simultaneously tune possible design parameters. Moreover, metaheuristic algorithms do not require precise and detailed knowledge of the controlled system model. In this paper, a hybrid algorithm based on the chaotic artificial ecosystem-based optimization (AEO) and manta-ray foraging optimization (MRFO) algorithms is proposed with the aim of combining the best features of each. Unlike the conventional MRFO method, the newly proposed hybrid AEO-CMRFO algorithm enables the use of chaotic maps and weighting factors. Moreover, the AEO and CMRFO hybridization process enables better convergence performance and the avoidance of local optima. Therefore, superior FO controller performance was achieved compared to traditional control design methods and other studied metaheuristic algorithms. An exhaustive study is provided, and the proposed control method was compared with traditional control methods to verify its advantages and superiority. [ABSTRACT FROM AUTHOR]

The transition from traditional to renewable energy sources is a critical issue in current energy-generation systems, which aims to address climate change and the increased demand for energy. This shift, however, imposes additional burdens on control systems to maintain power system stability and quality within predefined limits. Addressing these challenges, this paper proposes an innovative Modified Hybrid Fractional-Order (MHFO) automatic voltage regulator (AVR) equipped with a fractional-order tilt integral and proportional derivative with a filter plus a second-order derivative with a filter FOTI-PDND2N2 controller. This advanced controller combines the benefits of a (FOTI) controller, known for enhancing dynamic performance and steady-state response, with a (PDND2N2) controller to improve system robustness and adaptability. The proposed MHFO controller stands out with its nine tunable parameters, providing more extensive control options than the conventional three-parameter PID controller and the five-parameter FOPID controller. Furthermore, a recent optimization approach using a growth optimizer (GO) has been formulated and applied to optimally adjust the MHFO controller's parameters simultaneously. The performance of the proposed AVR based on the MHFO-GO controller is scrutinized by contrasting it with various established and developed optimization algorithms. The comparative study shows that the AVR based on the MHFO-GO controller surpasses other AVR controllers from the stability, robustness, and dynamic response speed points of view. [ABSTRACT FROM AUTHOR]

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller. [ABSTRACT FROM AUTHOR]

Renewable energy systems (RESs) have introduced themselves as vital solutions for energy supply in remote regions, wherein main utility supply systems are not available. The construction of microgrid (MG) systems is useful candidate for proper control and management with hybrid RESs. However, RESs-based MGs face reduced power system inertia due to the dependency of RESs on power electronic converter systems. Accordingly, preserving nominal operating frequency and reduced deviations in tie-line power are crucial elements for proper operation of interconnected MGs in remote areas. To overcome this problem, load frequency control (LFC) systems have proven featured solutions. Therefore, this paper proposes a new non-integer LFC method based on the fractional order (FO) control theory for LFC in interconnected MGs in remote areas. The proposed control is based on the three degree of freedom (3DoF) cascaded 1+proportional-integral-derivative-accelerated (PIDA) controller with FOPI controller, namely 3DoF 1+PIDA/FOPI LFC scheme. The proposed 3DoF 1+PIDA/FOPItakes the advantages of the accelerated term of PIDA control to improve power system transients, regarding maximum overshoot/undershoot and settling times. Additionally, it employs outer loop to reduce errors and faster inner loop to mitigate disturbances effects. The contribution of plug-in controlled electric vehicles (EVs) are considered to enhance the frequency regulation functions. An optimized design of the proposed 3DoF 1+PIDA/FOPI LFC scheme is proposed using the newly developed hybrid equilibrium optimizer (EO)-slime mould optimization (SMA) algorithm (namely EOSMA optimizer). The EOSMA combines the features of the EO and SMA powerful optimization algorithms. A two interconnected MGs in remote areas with RESs and EVs inclusions with high penetration levels is selected to verify the proposed 3DoF 1+PIDA/FOPI LFC scheme and the EOSMA optimizer. The results show high ability of the proposed controller and design scheme to minimize MGs' frequency and tie-line power fluctuations and to preserve frequency stability and security. [ABSTRACT FROM AUTHOR]

Reducing the emissions of greenhouse gases has directed energy sectors toward using renewable energy sources (RESs) and decreasing the dependency on conventional energy sources. Recently, developing efficient load frequency control (LFC) schemes has become essential to face the reduced inertia due to RESs installations. This paper presents a modified tilt fractional order (FO) integral–tilt FO derivative with a fractional filter (TFOI-TFODFF or namely TI λ -TD μ FF) LFC method. Although the proposed controller uses the same elements of standard controllers, it adopts FO control capabilities and flexibilities, including the tilt, FO integral, FO derivative, and FO filter. Thence, a new control structure is obtained, merging the advantages of both controllers. Moreover, the proposed TFOI-TFODFF controller employs two control loops to be able to mitigate low-frequency as well as high-frequency disturbances in power grids. Additionally, a new modified marine predator algorithm (MMPA) is proposed for optimally tuning the parameters of the proposed TFOI-TFODFF LFC method. The performance of the MMPA is enhanced in terms of initialization and exploitation phases using the chaotic maps and weighting factor. A two-area interconnected power system case study is implemented with wind and photovoltaic RESs and electric vehicles (EVs) contribution. The proposed TFOI-TFODFF LFC is compared with the FOPID, TID, TI-DF, and FOTPID controllers, wherein the proposed TFOI-TFODFF has offered superior performance of the proposed controller. Moreover, the proposed modified MPA is compared with the original MPA and other competitive optimization algorithms, and statistical analyses are carried out through parametric and nonparametric tests. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing concerns over bifacial PV (BPV) modules over the conventional monofacial PV (MPV) modules owing to their potential to add extra electrical energy from their rear-side irradiance. However, adding the rear-side irradiance to the front-side irradiance results in the increased nonlinearity of the BPV modules compared to MPV modules. Such nonlinearity makes the conventional methods unable to accurately extract the BPV module parameters. In this context, the precise determination of the BPV module parameters is a crucial issue for establishing energy yield estimations and for the proper planning of BPV installations as well. This paper proposes a new model for the BPV modules based on the MPV modeling, in which a new parameter is added to the MPV model to adjust the value of the model series resistance in order to provide a generic model for BPV modules in both monofacial and bifacial operating regions. Moreover, a new determination method for optimizing BPV model parameters using the recently developed enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE method, is applied. The determination process of the model parameters is adapted using a two-stage optimization scheme to model the full operating range of BPV modules. The accuracy of the obtained parameters using the proposed model is compared with the conventional single-diode and double-diode models of the BPV. The obtained results using the proposed model of the BPV module show the performance superiority and accuracy of the LSHADE method over the existing methods in the literature. Furthermore, the LSHADE method provides the successful and accurate extraction of the global optimized parameters to model MPV and BPV modules. Therefore, the proposed method can provide an accurate model for the whole operating range of BPV that would be beneficial for further studies of their economic and technical feasibility for wide installation plans. [ABSTRACT FROM AUTHOR]