Optimal placement of TCSC for congestion management and power loss reduction using multi-objective genetic algorithm

© 2020 by the authors. Electricity demand has been growing due to the increase in the world population and higher energy usage per capita as compared to the past. As a result, various methods have been proposed to increase the efficiency of power systems in terms of mitigating congestion and minimizing power losses. Power grids operating limitations result in congestion that specifies the final capacity of the system, which decreases the conventional power capabilities between coverage areas. Flexible AC Transmission Systems (FACTS) can help to decrease flows in heavily loaded lines and lead to lines loadability improvements and cost reduction. In this paper, total power loss reduction and line congestion improvement are assessed by determining the optimal locations and compensation rates of Thyristor-Controlled Series Compensator (TCSC) devices using the Multi-Objective Genetic Algorithm (MOGA). The results of applying the proposed method on the IEEE 30-bus test system confirmed the efficiency of the proposed procedure. In addition, to check the performance, applicability, and effectiveness of the proposed method, different heuristic algorithms, such as the multi-objective Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, and Mixed-Integer Non-Linear Program (MINLP) technique, are used for comparison. The obtained results show the accuracy and fast convergence of the proposed method over the other heuristic techniques

Bounded Distributed Flocking Control of Nonholonomic Mobile Robots

There have been numerous studies on the problem of flocking control for multiagent systems whose simplified models are presented in terms of point-mass elements. Meanwhile, full dynamic models pose some challenging problems in addressing the flocking control problem of mobile robots due to their nonholonomic dynamic properties. Taking practical constraints into consideration, we propose a novel approach to distributed flocking control of nonholonomic mobile robots by bounded feedback. The flocking control objectives consist of velocity consensus, collision avoidance, and cohesion maintenance among mobile robots. A flocking control protocol which is based on the information of neighbor mobile robots is constructed. The theoretical analysis is conducted with the help of a Lyapunov-like function and graph theory. Simulation results are shown to demonstrate the efficacy of the proposed distributed flocking control scheme

Hopfield lagrange network based method for economic emission dispatch problem of fixed-head hydro thermal systems

This paper proposes a Hopfield Lagrange Network (HLN) based method (HLNM) for economic emission dispatch of fixed head hydrothermal systems. HLN is a combination of Lagrange function and continuous Hopfield neural network where the Lagrange function is directly used as the energy function for the continuous Hopfield neural network. In the proposed method, HLN is used to find a set of non-dominated solutions and a fuzzy based mechanism is then exploited to determine the best compromise solution among the obtained ones. The proposed method has been tested on four hydrothermal systems and the obtained results in terms of total fuel cost, emission, and computational time have been compared to those other methods in the literature. The result comparisons have indicated that the proposed method is favorable for solving the economic emission dispatch problem of fixed-head hydrothermal systems

The role of information technology in STEM education

The ubiquity of IT (Information technology) for teaching at large is a reality that can be observed, including STEM education, which is the field of study of this research. In view of this situation, this work is intended to determine the role of IT in STEM (Science, Technology, Engineering, Mathematics) education. It was decided to conduct a systematic review based on PRISMA model and adding information obtained from the analysis of fugitive literature. The literature review was carried out on a total of 16 articles. The main inclusion criteria were a temporal selection from 2015 to March 2023, the inclusion of the terms IT and STEM in the title, abstract or keywords of the articles. The main results show an increasing tendency of this topic, especially in English research. Most relevant conclusions of the systematic review evidence a positive relationship between IT and STEM education, although some negative aspects are also highlighted as there is still a lack of resources and teacher training, leading to ineffective application of IT in STEM classes. The research results have important practical implications, it motivates teachers to research, propose and implement measures to enhance the role of IT in STEM education, while minimizing the limitations that have been identified

Power Loss Minimization by Optimal Placement of Distributed Generation Considering the Distribution Network Configuration Based on Artificial Ecosystem Optimization

Power loss in the Distribution System (DS) is often higher than that of other parts of the power system because of its low voltage level. Therefore, reducing losses is always an important task in de- sign and operation of the DS. This paper aims to apply a new approach based on Artificial Ecosystem Optimization (AEO) for the Distributed Generation Placement (DGP) and combination of DGP and net- work REConfiguration (DGP-REC) problems to reduce power loss of the DS to satisfy the technical constraints including power balance, radial topology, voltage and current bounds, and DG capacity limit. The AEO is a recent algorithm that has no special control parame- ters, inspired from the behaviours of living organisms in the ecosystem including production, consumption, and decomposition. The efficiency of the AEO is eval- uated on two test systems including the 33-node and 119-node systems. The numerical results validated on the 33-node and 119-node systems show that DGP-REC is a more effective solution for reducing power loss com- pared to the DGP solution. In addition, evaluation re- sults on small and large systems also indicate that AEO is an effective approach for the DGP and DGP-REC problems

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints

Optimal solutions for fixed head short-term hydrothermal system scheduling problem

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems

Modified cuckoo search algorithm for solving nonconvex economic load dispatch problems

This paper presents the application of modified cuckoo search algorithm (MCSA) for solving economic load dispatch (ELD) problems. The MCSA method is developed to improve the search ability and solution quality of the conventional CSA method. In the MCSA, the evaluation of eggs has divided the initial eggs into two groups, the top egg group with good quality and the abandoned group with worse quality. Moreover, the value of the updated step size in MCSA is adapted as generating a new solution for the abandoned group and the top group via the Levy flights so that a large zone is searched at the beginning and a local zone is foraged as the maximum number of iterations is nearly reached. The MCSA method has been tested on different systems with different characteristics of thermal units and constraints. The result comparison with other methods in the literature has indicated that the MCSA method can be a powerful method for solving the EL