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The reaction of the appropriate α-haloketones or α-keto-hydrazonoyl chlorides with the corresponding bis(hydrazinecarbothioamide) in EtOH at reflux in the presence of a few drops of TEA produced good yields of novel bis-thiazoles linked to diphenyl ether as novel hybrid molecules. Furthermore, some novel bis-thiazoles were obtained in high yields by reacting 1,1′-(oxybis(4,1-phenylene))bis(2-bromoethanone) with the appropriate thioamide. Furthermore, by reacting bis(2-bromoethanone) with the appropriate 4-amino-3-mercapto-1,2,4-triazole derivatives in ethanol in the presence of TEA, novel bis(5,6-dihydro-s-triazolo[3,4-b]thiadiazines) linked to diphenyl ether moiety were obtained in good yields. Moreover, by reacting bis(2-bromoethanone) with the appropriate pyridinethiones in ethanolic sodium ethoxide at reflux, good yields of bis(thienopyridines) linked to diphenyl ether moiety were obtained. The structures of new compounds were confirmed using elements and spectral data. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced intelligent application of Enhanced Artificial Gorilla Troops (EAGT) optimizer for parameters extraction of three different PV modules. The proposed EAGT optimizer is inspired by gorilla group behaviors, in which different methods are replicated, including migration to a new location, migrating to other gorillas, migration toward a designated spot, following the silverback, and competing for adult females. The EAGT is improved by supporting the exploration phase involving a fitness-based crossover (FBC) strategy. Not only that, but also it is by supporting the exploitation phase involving a periodic Tangent Flight (TF) operator. The effectiveness of the proposed EAGT is demonstrated using numerical assessments for the Kyocera KC200GT and STM6-40/36 PV modules using the Triple-Diode Model (TDM). In addition, the proposed EAGT is compared to the results of contemporary algorithms such as jellyfish search optimizer, forensic-based investigation optimizer, heap optimizer, equilibrium optimizer, and marine predator's optimizer. Also, the proposed EAGT is effectively applied on the SP70 PV module subjected to varied levels of sun irradiances and temperatures. The EAGT optimizer's efficacy and superiority are signified by fitness function standard deviations that indicate that TDM are less than 1 × 10−7, and compared to current and reported findings by others. • An Enhanced Artificial Gorilla Troops optimizer for parameters extraction by TDM is proposed. • The proposed method's effectiveness is demonstrated for three different PV modules. • The proposed method is compared to the results of contemporary algorithms. • The proposed method is effectively applied on varied levels of sun irradiances and temperatures. [ABSTRACT FROM AUTHOR]

• An economic environmental operation in hybrid AC-MTDC power systems is introduced. • An Improved Artificial Hummingbird Optimizer (IAHO) is developed to minimize fuel costs, environmental pollution, and losses in AC-MTDC systems. • The proposed IAHO includes a variety of territorial foraging tactics and a linear control process that improve both local and universal searching capabilities. • The simulation findings declare high economic, environmental, and technical benefits via the proposed IAHO with significant outperformance over several reported algorithms. Innovative technologies of multi-terminal high voltage direct current (HVDC), often known as "MTDC," represent additional features for better active and reactive power control when hybridized with traditional AC power systems. In this sequence, optimal operation in hybrid AC and MTDC power systems becomes highly economically and environmentally necessary. This paper presents an Improved Artificial Hummingbird Optimizer (IAHO) technique for reducing total generation fuel costs, environmental pollution, and whole power losses in AC-MTDC power systems. The proposed IAHO includes a variety of territorial foraging tactics (TFTs) and a Linear Control Process (LCP) which improve both local and universal searching capabilities. The proposed IAHO and AHO are performed on modified IEEE 57-bus and 30-bus hybrid AC and multi-terminal HVDC power systems. The simulation findings declare high economic, environmental, and technical benefits based on the proposed IAHO. A significant reduction in fuel costs, emissions, and losses with 22%, 59.51%, and 71.83% compared to the initial case for the IEEE 57-bus hybrid AC-MTDC power system. Also, the IEEE 30-bus hybrid AC-MTDC power system significantly reduced fuel costs, emissions, and losses by 17.01%, 16.04%, and 28.49% compared to the initial case. Moreover, the significant outperformance of the proposed IAHO is demonstrated over several reported algorithms. [ABSTRACT FROM AUTHOR]

A gradient-based optimizer (GBO) is a recently inspired meta-heuristic technique centered on Newton's gradient-based approach. In this paper, an advanced developed version of the GBO is merged with a crossover operator (GBOC) to enhance the diversity of the created solutions. The merged crossover operator causes the solutions in the next generation to be more random. The proposed GBOC maintains the original Gradient Search Rule (GSR) and Local Escaping Operator (LEO). The GSR directs the search to potential areas and aids in its convergence to the optimal answer, while the LEO aids the searching process in avoiding local optima. The proposed GBOC technique is employed to optimally place and size the distribution static VAR compensator (D-SVC), one of the distribution flexible AC transmission devices (D-FACTS). It is developed to maximize the yearly energy savings via power losses concerning simultaneously different levels of the peak, average, and light loadings. Its relevance is tested on three distribution systems of IEEE 33, 69, and 118 nodes. Based on the proposed GBOC, the outputs of the D-SVCs are optimally varying with the loading level. Furthermore, their installed ratings are handled as an additional constraint relating to two compensation levels of 50% and 75% of the total reactive power load to reflect a financial installation limit. The simulation applications of the proposed GBOC declare great economic savings in yearly energy losses for the three distribution systems with increasing compensation levels and iterations compared to the initial case. In addition, the effectiveness of the proposed GBOC is demonstrated compared to several techniques, such as the original GBO, the salp swarm algorithm, the dwarf mongoose algorithm, differential evolution, and honey badger optimization. [ABSTRACT FROM AUTHOR]

Multi-Dimensional Optimal Power Flow (MDOPF) is a fundamental task in power systems engineering aimed at optimizing the operation of electrical networks while considering various constraints such as power generation, transmission, and distribution. The mathematical model of MDOPF involves formulating it as a non-linear, non-convex optimization problem aimed at minimizing specific objective functions while adhering to equality and inequality constraints. The objective function typically includes terms representing the Fuel Cost (FC), Entire Network Losses (ENL), and Entire Emissions (EE), while the constraints encompass power balance equations, generator operating limits, and network constraints, such as line flow limits and voltage limits. This paper presents an innovative Improved Kepler Optimization Technique (IKOT) for solving MDOPF problems. The IKOT builds upon the traditional KOT and incorporates enhanced local escaping mechanisms to overcome local optima traps and improve convergence speed. The mathematical model of the IKOT algorithm involves defining a population of candidate solutions (individuals) represented as vectors in a high-dimensional search space. Each individual corresponds to a potential solution to the MDOPF problem, and the algorithm iteratively refines these solutions to converge towards the optimal solution. The key innovation of the IKOT lies in its enhanced local escaping mechanisms, which enable it to explore the search space more effectively and avoid premature convergence to suboptimal solutions. Experimental results on standard IEEE test systems demonstrate the effectiveness of the proposed IKOT in solving MDOPF problems. The proposed IKOT obtained the FC, EE, and ENL of USD 41,666.963/h, 1.039 Ton/h, and 9.087 MW, respectively, in comparison with the KOT, which achieved USD 41,677.349/h, 1.048 Ton/h, 11.277 MW, respectively. In comparison to the base scenario, the IKOT achieved a reduction percentage of 18.85%, 58.89%, and 64.13%, respectively, for the three scenarios. The IKOT consistently outperformed the original KOT and other state-of-the-art metaheuristic optimization algorithms in terms of solution quality, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]

This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives. [ABSTRACT FROM AUTHOR]

Multi-area power systems (MAPSs) are highly complex non-linear systems facing a fundamental issue in real-world engineering problems called frequency stability problems (FSP). This paper develops an enhanced slime mold optimization algorithm (ESMOA) to optimize the tuning parameters for a cascaded proportional derivative-proportional integral (PD-PI) controller. The novel ESMOA proposal includes a new system that combines basic SMO, chaotic dynamics, and an elite group. The motion update incorporates the chaotic technique, and the exploitation procedure is enhanced by searching for a select group rather than merely the best solution overall. The proposed cascaded PD-PI controller based on the ESMOA is employed for solving the FSP in MAPSs with two area non-reheat thermal systems to keep the balance between the electrical power load and the generation and provide power system security, reliability, and quality. The proposed cascaded PD-PI controller based on the ESMOA is evaluated using time domain simulation to minimize the integral time-multiplied absolute error (ITAE). It is evaluated in four different test situations with various sets of perturbations. For tuning the cascaded PD-PI controller, the proposed ESMOA is compared to the golden search optimizer (GSO) and circle optimizer (CO), where the proposed ESMOA provides the best performance. Furthermore, the findings of the proposed cascaded PD-PI controller based on the ESMOA outperform previous published PID and PI controllers adjusted using numerous contemporary techniques. [ABSTRACT FROM AUTHOR]

The optimal power flow is considered as a crucial tool in the power systems' operation and planning. To demonstrate, it aims at minimizing the operational costs of energy production and transmission by adjusting control variables with maintaining economic, operational, and environmental constraints. This article proposes and scrutinizes an Improved Heap-based Optimization Algorithm as a novel technique that successfully enhances the performance of a recently algorithm, namely Heap-based Optimization algorithm to address the optimal power flow problem. In the improved optimizer, an effective exploitation feature is emerged with the conventional version to improve its performance by enhancing the searching around the leader position. This enhancement can avoid being trapped in a local optimum and increase its global search capabilities. For the sake of practicality, the optimizers are developed with diverse objectives of the optimal power flow problem with minimization of fuel cost, emission amount, and transmission power losses with additional restrictions in real power systems that include valve-point effect and security constraints. A proposed multi-objective, improved heap optimization algorithm is investigated to solve multiobjective cases studied. The proposed multi-objective is developed based on the Pareto concept. Three standard systems: IEEE 57-bus system, a large scale 118-bus system, and a practical System are utilized to reveal the suitability and performance of the proposed technique in solving the optimal power flow problem. To illustrate the effectiveness of the proposed optimizer in handling non-convex and diverse scale optimization problems, a comparative analysis has been illustrated with those in the literature. • An Improved Heap-based Optimization Algorithm addresses the OPF problem. • A proposed multi-objective improved heap optimization algorithm is investigated. • Three standard systems are utilized to reveal the suitability and performance of the proposed. • The effectiveness of the proposed optimizer has been illustrated compared with others. • Significant reductions, in the technical economic point of views for single and multiobjective frameworks. [ABSTRACT FROM AUTHOR]

One of the critical optimization issues in the economic management of power and heat systems is the Combined heat and power economic dispatch (CHPED). The valve-point effects of thermal units as well as the interdependency of CHP outputs make the nonlinearity and non-convexity in optimization and dispatch models. A novel Amalgamated Heap-based and JellyFish Optimizer (AHJFO) is proposed, in this paper, to improve the efficiency of two newly developed techniques, Heap-based Optimizer (HO) and Jellyfish Optimizer (JFO). The proposed AHJFO incorporates an adjustment strategy function (ASF) to increase the explorative characteristic at the beginning of iterations by upgrading the produced solutions using HO. Further, as iterations go, it improves the exploitative characteristic by expanding the created solutions using JFO. The proposed AHJFO provides higher effectiveness compared to HO and JFO to obtain the solution of CHPED problem for medium 24 and large 96-unit systems. The simulation results show that the proposed AHJFO based on ASF aids in the avoidance of premature convergence and improves solution accuracy. Besides, the proposed AHJFO is successfully applied to the CHPED issue involving (N-1) unit outages. A re-dispatch strategy based on AHJFO is presented and the impact of outages of all units is analyzed after (N-1) unit outages. Also, further applications of the proposed AHJFO for ED problem are carried out with/without additional constraint models of power flow considering the IEEE 30-bus system. The simulation results show the superiority of the proposed AHJFO compared to HO, JFO and other algorithms for solving the CHPED and ED issues. Moreover, feasibility study is demonstrated of the solutions obtained for the CHPED and ED issues with additional constraint models. • Combined heat and power economic dispatch problem is solved by AHJFO Optimizer. • The AHJFO provides higher effectiveness for medium 24 and large 96-unit systems. • The simulation results are applied to the CHPED issue involving (N-1) outages. • Applications of the AHJFO for ED problem are carried out with/without additional constraint models. • Feasibility study is demonstrated of the solutions obtained for the CHPED and ED issues. [ABSTRACT FROM AUTHOR]

An enhanced multi-objective Quasi-Reflected Jellyfish Search Optimizer (MOQRJFS) is presented in this article for solving multi-dimensional Optimal Power Flow (MDOPF) issue with diverse objectives which display the minimization of economic fuel cost, total emissions, and the active power loss with satisfying operational constraints. Despite the simple structure of JFS with control of exploitation and exploration, searching capability of the JFS requires more support. Hence, two modifications are performed on the standard JFS algorithm. The first modification is that a cluster with a random size has been proposed which illustrates the social community that can share the data in the cluster and are dissimilar from one to another. The second modification is that a quasi-opposition-based learning is emerged in JFS to support the exploration phase. As selection criteria for the best solutions, a fuzzy decision-making strategy is joint into MOQRJFS optimizer. Additionally, the Pareto optimality concept is added to extract the non-dominated solutions. The superiority of the MOQRJFS is proved throughout application on IEEE 30-bus system, IEEE 57-bus system, the West Delta Region System of 52 bus (WDRS-52) in Egypt, and a large scale 118-bus system. Thirteen cases with economic, environmental, and technical objectives of MDOPF are included in this study. The outcomes of the proposed MOQRJFS have been compared with the conventional MOJFS and the reported techniques in the literature. It is clearly observed that the MOQRJFS give the minimum values compared with these techniques which reveals its robustness, effectiveness, and superiority when handling MDOPF among other techniques. • An enhanced multi-objective Quasi-Reflected Jellyfish Search Optimizer is presented. • Two modifications are performed on the standard JFS algorithm. • The superiority is proved on four test systems. • Thirteen cases with economic, environmental, and technical objectives are considered. • The outcomes of the proposed optimizer have been compared with others. [ABSTRACT FROM AUTHOR]