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Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover, hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context, this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava, Czech Republic's northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an off-grid system, lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen, electricity, and wastewater treatment. The simulated model considers the capital, operating, and replacement costs for system components. In the proposed system, 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit, respectively. Accordingly, the power station annually generates 6,997,990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis, the project's NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system, which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore, emerging electrocoagulation studies produce hydrogen through wastewater treatment, increasing hydrogen production and lowering LCH. Therefore, this study is able to provide practicable methodology support for optimal sizing of the power station components, which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabilities, making it well-suited for tuning the controller. The proposed approach utilizes a master–slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control system's performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a comprehensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD2 controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness.

Abstract In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.