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This work proposes the adoption of Enhanced Gradient-Based Optimizer (EGBO) as a new approach to the Load Frequency Control (LFC) problem in a two-area interconnected power system. The importance of determining the optimal parameters for the controllers for the LFC problem cannot be overstated, and the fact that estimating these parameters require complex and nonlinear computations makes the optimization procedure even more unique and challenging. Consequently, application of an efficient optimization algorithm to successfully attain optimal controller parameters is critical. To accomplish this task, the proposed EGBO algorithm is compared to the fundamental Gradient-Based Optimizer (GBO), Chimp Optimization Algorithm (ChOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) for optimizing an Integral-Time-multiplied-Absolute-Error (ITAE) based objective function. The relevant findings show that the EGBO algorithm is competitively superior in terms of resilience, precision, and latency when compared to other optimization methods. Lastly, the statistical comparison further strengthens the outcome of the study. To be published in Engineering Applications of Modern Metaheuristics under Studies in Computational Intelligence series

this paper, an evolutionary algorithm-Invasive Weed Optimization (IWO) based power system stabilizer (PSS) is proposed for multi-machine power system. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. Owing to its superior performance in comparison with many other existing meta-heuristics, it has used to search for optimal settings of PSS parameters. Eigen-value based objective function is considered to enhance system damping of electromechanical mode. The performance of proposed IWO- based PSS is tested and demonstrated under different loading conditions and disturbances for a four machine example power system. The Eigen value analysis and non-linear simulation results prove the effectiveness of the proposed IWO-based PSS design. The robustness of the design method is confirmed by testing the IWO based PSS performance under varying load conditions.

Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency. However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids. Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA. Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system. A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software. From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Based on a new metaheuristic known as the Artificial hummingbird algorithm (AHA), this paper provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.

In this paper, two evolutionary algorithms- Invasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a derivative-free and flexible optimizer which is powered by the behavior of organism, such as bird flocking. Eigen-value based objective function is considered for the tuning of PSSs to enhance system damping of electromechanical mode. The performance of proposed IWO-based PSS and PSO-based PSS is tested and demonstrated under different disturbances for a four machine example power system. The Eigen value analysis and non-linear time domain simulation results shows that both IWO-based PSS and PSO-based design can successfully damp out the oscillations and thus improve the stability of the system. However, the abilities like faster convergence and greater shifting of critical modes to the left of s-plane keeps the choice of IWO based design in front of PSO based design for the system under consideration.

This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.

The critical challenge for an efficient islanding operation of a distribution system having Distributed Generation (DG) is preserving the frequency and voltage stability. Contemporary load shedding schemes are inefficient and do not adequately assess the optimum amount of load to shed which results in either excessive or inadequate load shedding. Hence, this paper presents an optimal load shedding technique using Chaotic Slime Mould Algorithm (CSMA) with sinusoidal map in order to achieve greater efficiency. A constrained function with static voltage stability margin (VSM) index and total remaining load after load shedding was applied to accomplish the evaluation. A total of three islanding scenarios of IEEE 33 bus and IEEE 69 bus radial distribution systems were used as test systems to assess the efficacy of the proposed load shedding approach using MATLAB software. To identify performance enhancements, the developed method was compared to Backtrack Search Algorithm (BSA) and the original SMA. According to the results, CSMA outperforms both BSA and SMA in terms of remaining load and voltage stability margin index values in all the test systems.