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This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller's steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure. [ABSTRACT FROM AUTHOR]

In this article, a fractional-order proportional-integral-differential (FOPID) controller and its modified structure, called a MFOPID controller, are presented. To guarantee optimal system performance, the gains of the proposed FOPID and MFOPID controllers are well-tuned, employing the Jellyfish Search Optimizer (JSO), a novel and highly effective bioinspired metaheuristic approach. The proposed controllers are assessed in a hybrid system with two domains, where each domain contains a hybrid of conventional (gas, reheat, and hydro) and renewable generation sources (solar and wind). For a more realistic analysis, the presented system model includes practical limitations with nonlinear characteristics, such as governor dead zone/band (GDZ/GDB), boiler dynamics, generation rate limitation/constraint (GRL/GRC), system uncertainties, communication time delay (CTD), and load changes. The suggested methodology outperforms some newly developed heuristic techniques, including fitness-dependent optimizer (FDO), sine-cosine algorithm (SCA), and firefly algorithm (FA), for the interconnected power system (PS) of two regions with multiple generating units. Furthermore, the proposed MFOPID controller is compared with JSO-tuned PID/FOPID and PI controllers to ascertain its superiority. The results signify that the presented control method and its parametric optimization significantly outperforms the other control strategies with respect to minimum undershoot and peak overshoot, settling times, and ITSE in the system's dynamic response. The sensitivity analysis outcomes imply that the proposed JSO-MFOPID control method is very reliable and can effectively stabilize the load frequency and interconnection line in a multi-area network with interconnected PS. [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]

Existing interconnected power systems (IPSs) are being overloaded by the expansion of the industrial and residential sectors together with the incorporation of renewable energy sources, which cause serious fluctuations in frequency, voltage, and tie-line power. The automatic voltage regulation (AVR) and load frequency control (LFC) loops provide high quality power to all consumers with nominal frequency, voltage, and tie-line power deviation, ensuring the stability and security of IPS in these conditions. In this paper, a proportional integral derivative (PID) controller is investigated for the effective control of a four-area IPS. Each IPS area has five generating units including gas, thermal reheat, hydro, and two renewable energy sources, namely wind and solar photovoltaic plants. The PID controller was tuned by a meta-heuristic optimization algorithm known as a gradient-based optimizer (GBO). The integral of time multiplied by squared value of error (ITSE) was utilized as an error criterion for the evaluation of the fitness function. The voltage, frequency, and tie-line power responses of GBO-PID were evaluated and compared with integral–proportional derivative (GBO-I-PD), tilt integral derivative (GBO-TID), and integral–proportional (GBO-I-P) controllers with 5% step load perturbation (SLP) provided in each of the four areas. Comprehensive comparisons between GBO-PID and other control methodologies revealed that the proposed GBO-PID controller provides superior voltage, frequency, and tie-line power responses in each area. The reliability and efficacy of GBO-PID methodology were further validated with variations in the turbine time constant and speed regulation over a range of  ± 25%. It is evident from the outcomes of the sensitivity analysis that the proposed GBO-PID control methodology is very reliable and can successfully stabilize the deviations in terminal voltage, load frequency, and tie-line power with a shorter settling time in a four-area IPS. [ABSTRACT FROM AUTHOR]

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers. [ABSTRACT FROM AUTHOR]