Search

Microgrids are a type of restricted power distribution systems in which electricity is generated, transmitted, and distributed within a small geographic region. They are used to ensure that renewable energy sources are used to their full potential. Microgrids provide further benefits, such as lowering transmission losses and the expenses associated with them. This research compares and contrasts the aims of economic dispatch, emission dispatch, fractional programing based combined economic emission dispatch, and environmental restricted economic dispatch (ECED). A low‐voltage microgrid system is investigated for three different scenarios. As a study optimization tool, an innovative, resilient, and strong hybrid swarm‐intelligence optimization algorithm is utilised, which is based on combining the properties of the traditional grey‐wolf optimiser, sine‐cosine algorithm, and crow search algorithm. The employment of a time‐of‐use energy market pricing approach instead of a fixed pricing plan resulted in a 15% decrease in generation costs throughout the course of the research. When ECED was assessed with a 15%–20% demand side management based restructured load demand model for the microgrid system, the generation costs were reduced even further. [ABSTRACT FROM AUTHOR]

The assimilation of exible AC transmission (FACTS) controllers to the existing power network outweigh the numerous alternatives in enhancing the damping behavior for the inter-area/intra-area system oscillations of a power network. This paper provides a rigorous analysis in damping of oscillations in a power network. It utilizes a shunt connected voltage source converter (VSC) based FACTS device to enhance the system operating characteristics. A comprehensive system mathematical modelling has been developed for demonstrating the system behavior under different loading conditions. A novel hybrid augmented grey wolf optimization-particle swarm optimization (AGWO-PSO) is proposed for the coordinated design of controllers static synchronous compensator (STATCOM) and power system stabilizers (PSSs). A multi-objective function, comprising damping ratio improvement and drifting the real part to the left-hand side of S-plane of the system poles, has been developed to achieve the objective and the effectiveness of the proposed algorithms have been analyzed by monitoring the system performance under different loading conditions. Eigenvalue analysis and damping nature of the system states under perturbation have been presented for the proposed algorithms under different loading conditions, and the performance evaluation of the proposed algorithms have been done by means of time of execution and the convergence characteristics. [ABSTRACT FROM AUTHOR]

Optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in‐depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self‐clearing fault conditions. A multi‐objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO‐CS and BMO‐PSO), and harris hawks optimizer. [ABSTRACT FROM AUTHOR]

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for the system states also presented with the PSS parameters obtained by the proposed algorithms. • Amendments were made by hybridizing GWO with PSO, SCA and CSA and recommended for the electrical power system stability enhancement. • Realization of these amended algorithms was done on 23 benchmark functions. • Algorithms mentioned above were utilized to tune traditional power system stabilizer considering system uncertainties. • Eigenvalue analysis and damping characteristics of system states have been presented under system uncertainties. • Non-parametric statistical analysis was performed for the amended GWO algorithms. [ABSTRACT FROM AUTHOR]

The improvement of power system stability by damping oscillations in the system states is presented in this paper. An antlion optimization (ALO) algorithm is adopted to tune the damping device controller parameters. The ALO algorithm performance is investigated on the benchmark functions, and the same has been compared with the traditional particle swarm optimization (PSO) technique. The statistical analysis on the fitness achieved with the considered algorithms has been demonstrated using best, worst, mean, and SD values on the benchmark functions over 30 individual runs. Here power system stabilizer (PSS) and static synchronous compensator are considered as oscillation damping devices in the test power network. The analysis has been carried out in a sample power system network, and the proposed technique is compared with the conventional PSS and PSO. The results obtained from the designed test system recommends the superior performance characteristics of ALO in applying to the practical power system. [ABSTRACT FROM AUTHOR]

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows' memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper. [ABSTRACT FROM AUTHOR]

This paper presents binary particle swarm optimization (BPSO) technique for the optimal allocation of phasor measurement units (PMUs) for the entire observability of connected power network. Phasor measurement units are considered as one of the most important measuring devices in the prospect of connected power network. PMUs function may be incorporated to the wide-area connected power networks for monitoring and controlling purposes. The optimal PMU placement (OPP) problem provides reference to the assurance of the minimal number of PMUs and their analogous locations for observability of the entire connected power networks. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of OPP problem. The efficacy and robustness of the proposed method has been tested on the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, IEEE 118-bus and Northern Regional Power Grid (NRPG) 246-bus test system. The results obtained by proposed approach are compared with other standard methods and it is observed that this BPSO based placement of phasor measurement units is found to be the best among all other techniques discussed. [ABSTRACT FROM AUTHOR]

Reactive power source planning problem has significant role for secure and economic operation of power system. Reactive power planning problem is nothing but the proper coordination of existing Var sources which lead to loss minimization and cost economic operation of the system. In the proposed approach several bio-inspired algorithms like Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), Adaptive Particle Swarm Optimization (APSO), Hybrid Particle Swarm Optimization (HPSO) and Bacterial Foraging Algorithm (BFA) are used for the reactive power planning problem. Finally a comparison of all these techniques are made on the basis of the results obtained when applied to different standard test system. [ABSTRACT FROM AUTHOR]

In this paper, Gravitational Search Algorithm (GSA) is used as optimization method in reactive power planning using FACTS (Flexible AC transmission system) devices. The planning problem is formulated as a single objective optimization problem where the real power loss and bus voltage deviations are minimized under different loading conditions. GSA based optimization algorithm and particle swarm optimization techniques (PSO) are applied on IEEE 30 bus system. Results show that GSA can also be a very effective tool for reactive power planning. [ABSTRACT FROM AUTHOR]

Highlights: [•] Fuzzy based evolutionary algorithm. [•] FACTS devices for reactive power optimization. [•] FACTS devices for minimum operating cost of a power system. [Copyright &y& Elsevier]

Abstract—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods. [ABSTRACT FROM PUBLISHER]

In this paper Genetic Algorithm (GA) is used as an evolutionary techniques for the optimal placement of flexible AC transmission systems (FACTS) devices in an interconnected power system. Here two types of FACTS devices has been discussed namely, Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensator (SVC) for the economic operation and to reduce the transmission loss. Reactively loading of the system is taken from base to 200 % of base loading and the system performance is observed without and with FACTS devices. Optimal placement of FACTS devices in the system is determined by calculating active and reactive power flow in lines. FACTS devices along with reactive generation of generators and transformer tap setting are used for the power transfer capacity using GA. The proposed approach is applied on IEEE 14 and IEEE 30-bus test systems. Finally the effectiveness of the proposed GA based method of placement of FACTS devices is established by comparing the results with another standard method of optimization like Particle Swarm Optimization (PSO) technique. [ABSTRACT FROM AUTHOR]

This paper presents Particle Swarm Optimization (PSO) based approach for the allocation & coordinated operation of multiple FACTS (Flexible AC Transmission System) devices for the economic operation as well as to increase power transfer capacity of an interconnected power system under different loading condition. The PSO based approach is applied on IEEE 30-bus system. The system is reactively loaded starting from base to 200 % of base load. FACTS devices are installed in the different locations of the power system and system performance is noticed with and without FACTS devices. First, the locations, where the FACTS devices to be placed is determined by calculating active and reactive power flows in the lines. A Particle Swarm Optimization based algorithm is then applied to find the amount of magnitudes of the FACTS devices. This Particle Swarm Optimization based approach for the placement of FACTS devices Yields promising result both in terms of performance and economy which is clearly observed from the result obtained. [ABSTRACT FROM AUTHOR]