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There is a growing interest in green hydrogen, with researchers, institutions, and countries focusing on its development, efficiency improvement, and cost reduction. This paper explores the concept of green hydrogen and its production process using renewable energy sources in several leading countries, including Australia, the European Union, India, Canada, China, Russia, the United States, South Korea, South Africa, Japan, and other nations in North Africa. These regions possess significant potential for "green" hydrogen production, supporting the transition from fossil fuels to clean energy and promoting environmental sustainability through the electrolysis process, a common method of production. The paper also examines the benefits of green hydrogen as a future alternative to fossil fuels, highlighting its superior environmental properties with zero net greenhouse gas emissions. Moreover, it explores the potential advantages of green hydrogen utilization across various industrial, commercial, and transportation sectors. The research suggests that green hydrogen can be the fuel of the future when applied correctly in suitable applications, with improvements in production and storage techniques, as well as enhanced efficiency across multiple domains. Optimization strategies can be employed to maximize efficiency, minimize costs, and reduce environmental impact in the design and operation of green hydrogen production systems. International cooperation and collaborative efforts are crucial for the development of this technology and the realization of its full benefits. [ABSTRACT FROM AUTHOR]

The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the "chattering" phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for drawing a cryptographically strong substitution box using the Lorenz system and quantum genetic algorithm techniques. We used the chaotic function to generate an initial random sequence of bits and the quantum crossover to provide a new and improved substitution box with increased non-linearity. The aim of the proposed algorithm was to generate a strong and secure substitution box that can be utilized in symmetric cryptosystems. The use of chaotic Boolean functions, genetic algorithm techniques, and the quantum crossover helped achieve this goal, and statistical tests further confirmed the randomness and efficiency of the generated substitution box. The results of the statistical test suite showed that the substitution box produced by the proposed algorithm is a "pass" in terms of randomness and has strong cryptographic properties. The tests include a frequency (monobit) test, a frequency test within a block, a linear complexity test, an approximate entropy test, and a cumulative sums test among others. The p-values obtained in the tests indicate that the randomness of the generated substitution box meets the requirements of a cryptographically secure substitution box. [ABSTRACT FROM AUTHOR]

Power dispatch has become an important issue due to the high integration of Wind Power (WP) in power grids. Within this context, this paper presents a new Particle Swarm Optimization (PSO) based strategy for solving the stochastic Economic Emission Dispatch Problem (EEDP). This problem was solved considering several constraints such as power balance, generation limits, and Valve Point Loading Effects (VPLEs). The power balance constraint is described by a chance constraint to consider the impact of WP intermittency on the EEDP solution. In this study, the chance constraint represents the tolerance that the power balance constraint cannot meet. The suggested framework was successfully evaluated on a ten-unit system. The problem was solved for various threshold tolerances to study further the impact of WP penetration. [ABSTRACT FROM AUTHOR]

This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Searchbased Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques. [ABSTRACT FROM AUTHOR]

Power system planning faces various issues related to reliability and quality evaluation. The power system network planning is by nature a complex, huge-scale, and mixed-objective optimization problem, especially when concerning its non-linear behavior and the requirements of future unknown loads. In this regard, the electric power utilities attempt to maintain a balance between the generation energy, the transmission capacity, and the needed demand. The main purpose of the current paper is to utilize modern modeling techniques and computational procedures, including the advanced deficit transmission system evaluation method and sparse-matrix network analysis algorithms, in order to evaluate, with sufficient accuracy, the deficit and reliability levels in practical real-life large-scale power systems. The new evaluation methodology is based on three quantities representing the relationship between the generation push in the grid, the maximum limitation of the transmission capacity, and the needed load. The main contribution of the paper is assessing the deficit transmission system index with novel formulas. [ABSTRACT FROM AUTHOR]

Along with economic dispatch, emission dispatch has become a key problem under market conditions. Thus, the combination of the above problems in one problem called economic emission dispatch (EED) problem became inevitable. However, due to the dynamic nature of today’s network loads, it is required to schedule the thermal unit outputs in real-time according to the variation of power demands during a certain time period. Within this context, this paper presents an elitist technique, the second version of the non-dominated sorting genetic algorithm (NSAGII) for solving the dynamic economic emission dispatch (DEED) problem. Several equality and inequality constraints, such as valve point loading effects, ramp rate limits and prohibited operating zones (POZ), are taken into account. Therefore, the DEED problem is considered as a nonconvex optimization problem with multiple local minima with higher-order non-linearities and discontinuities. A fuzzy-based membership function value assignment method is suggested to provide the best compromise solution from the Pareto front. The effectiveness of the proposed approach is verified on the standard power system with ten thermal units. [ABSTRACT FROM AUTHOR]

Appropriate modeling and accurate parameter identification of solar cells are crucial in the optimization of photovoltaic (PV) systems. The single-diode model (SDM), consisting of an ideal current source, an ideal diode, a shunt resistor and a series resistor, is widely used to simulate the behavior of PV cells/panels. In this article, a hybrid approach for identification of solar cell SDM parameters is presented. This approach uses the inverse of the slope of the I-V curve under short-circuit and open-circuit conditions and combines numerical and analytical solutions. Indeed, knowing that numerical methods require appropriate initial values, the main idea of the proposed approach is to provide these solutions by analytical methods. The comparison of obtained results with experimental ones, based on manufacturer's datasheet, proves that the algorithm thus obtained requires less information from the manufacturer and improves significantly the parameter identification accuracy. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the extended version of the conventional DEED to overcome the ramp rate violations when its optimal solutions for one period (normally one day) are implemented repeatedly and periodically over consequent dispatch periods to meet the periodic load demands. This dynamic dispatch problem, which is referred to as EDEED, is a multi-objective optimization problem which simultaneously minimizes both fuel cost and pollutants emission while satisfying a set of constraints. A multi-objective particle swarm optimization (MOPSO) method has been applied in this article for solving EDEED problem. The performance of the proposed method has been evaluated on the 10-unit test system with non-smooth fuel cost and emission level functions in comparison with those methods reported in the literature. [ABSTRACT FROM AUTHOR]