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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]

A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize the physical phenomenon of rime-ice growth. It simulates the hard-rime and soft-rime processes, constructing the mechanisms of hard-rime puncture and soft-rime search. In this study, an enhanced version, termed Modified RIME (MRIME), is introduced, integrating a Polynomial Differential Learning Operator (PDLO). The incorporation of PDLO introduces non-linearities to the RIME algorithm, enhancing its adaptability, convergence speed, and global search capability compared to the conventional RIME approach. The proposed MRIME algorithm is designed to identify photovoltaic (PV) module characteristics by considering diverse equivalent circuits, including the One-Diode Model (ONE-DM) and Two-Diode Model TWO-DM, to determine the unspecified parameters of the PV. The MRIME approach is compared to the conventional RIME method using two commercial PV modules, namely the STM6-40/36 module and R.T.C. France cell. The simulation results are juxtaposed with those from contemporary algorithms based on published research. The outcomes related to recent algorithms are also compared with those of the MRIME algorithm in relation to various existing studies. The simulation results indicate that the MRIME algorithm demonstrates substantial improvement rates for the STM6-40/36 module and R.T.C. France cell, achieving 1.16% and 18.45% improvement for the ONE-DM, respectively. For the TWO-DM, it shows significant improvement rates for the two modules, reaching 1.14% and 50.42%, respectively. The MRIME algorithm, in comparison to previously published results, establishes substantial superiority and robustness. [ABSTRACT FROM AUTHOR]

This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO's primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO's success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively. [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 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]

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]

Parameter estimation for solar photovoltaic (PV) models is a challenging issue due to the complex nonlinear multivariable of the current‐voltage and power‐voltage characteristics. In this article, an improved heap‐based algorithm (IHBA) is proposed to improve the performance of a recently published algorithm called heap‐based algorithm (HBA). The HBA's performance is enhanced by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The proposed IHBA and the standard HBA are developed considering the practical limits of the electrical parameters of PV models to minimize the root mean square error (RMSE) between the experimental and simulated results. The numerical analyses for the PVM‐752GaAs PV module including single‐diode model (SDM), double‐diode model (DDM) and triple‐diode model (TDM) are investigated to estimate five, seven, and nine electrical parameters of these models, respectively. Besides, different recent optimization techniques are simulated with fair comparisons, which are forensic‐based investigation (FBI), equilibrium optimizer (EO), Jellyfish Search (JFS), HBA, Marine Predator Algorithm (MPA) and Enhanced MPA (EMPA), and are compared with the proposed IHBA. Several separate runs are illustrated for the proposed IHBA in comparison with others, whereas the whisker box plot and T‐tests are activated to evaluate their effectiveness metrics. The simulation results derive higher superiority of the proposed IHBA with the minimum RMSE objective and standard deviation of (0.000228 and 3.7 × 106), (0.000184 and 1.93 × 105) and (0.000017 and 2.11 × 105) for SDM, DDM, and TDM, respectively, with respect to the standard HBA, other recent and reported optimization techniques. Additionally, the P‐indicator is applied in this study to illustrate the evidence for the alternative hypothesis. Small values of the P‐indicator for the proposed IHBA are obtained compared to the standard HBA and other recent optimization techniques, where IHBA achieves p‐value of 1.0336 × 1017, 4.037 7× 1011 and 2.1490 × 1010 for SDM, DDM and TDM, respectively. The H‐value is always one which elucidates that the hypothesis probability is not more than 5% for all algorithms for SDM, DDM and TDM. Moreover, the proposed IHBA demonstrates higher efficiency as it provides the first ranks in the confidence interval values compared with other algorithms. Furthermore, the proposed IHBA is significantly employed for the SQ_150 PV module considering the TDM with diverse solar irradiance and temperatures, where significant closeness between the emulated and experimental P–V curves is evidently demonstrated. [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]