scholarly journals Optimal Power Flow through Artificial Intelligence Techniques

Tecnura ◽  
2021 ◽  
Vol 25 (69) ◽  
pp. 150-170
Author(s):  
Cesar Hernandez ◽  
William Sánchez Huertas ◽  
Víctor Gómez
Keyword(s):  
Genetic Algorithm ◽  
Energy Distribution ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Ant Colony Algorithm ◽  
Optimization Methods ◽  
Electric Power System ◽  
Ant Colony ◽  
Flow Through

Context: The integration of optimization methods into the various processes carried out by an electric power system seeking energy efficiency have led to satisfying results in the reduction of consumption as well as in terms of technical losses, security increase and system reliability. Objective: The purpose of this article is to identify a method offering the best optimization outcome for the power flow of an energy distribution system with 10 nodes at 13.2 kV. Methodology: The results of voltage profiles are presented for a 10-node energy distribution system using the Newton Raphson method. Afterward, the system is optimized using genetic and ant colony algorithms. Results: Their implementation determined that the sum of the potential differences of distribution lines is notably reduced with the genetic algorithm. However, the ant colony optimization code takes less time to run and has a lower number of iterations. Conclusions: The most efficient optimization is achieved with the genetic algorithm since the evolution of the population shows better optimization levels in comparison to the ant colony algorithm. Financing: Universidad Francisco José de Caldas and Colciencias

scholarly journals GENERATOR FUEL COST OPTIMIZATION USING ANT COLONY ALGORITHM

2019 ◽  
Vol 1 (1) ◽  
pp. 1-4
Author(s):  
Indri Suryawati ◽  
Sagita Rochman
Keyword(s):  
Power Flow ◽  
Power Plants ◽  
Optimal Power Flow ◽  
Ant Colony Algorithm ◽  
Fitness Function ◽  
Cost Optimization ◽  
Ant Colony ◽  
Fuel Cost ◽  
Ant Behavior ◽  
Shortest Distance

Ant Colony Algorithm (ACA) is an optimization algorithm was inspired by ant behavior when searching for the shortest distance from the food center. In this study, ACA is used for power plants with a fuel cost fitness function. ACA can search destinations faster than conventional methods such as Lagrange. In this study ACA used the optimal power flow of six power plants in the Java Bali 500 KV system, the optimization results reduced fuel costs by 23% and Lagrange 17.4% compared to real conditions.  

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

Closure on "optimal power flow by enhanced genetic algorithm"

2003 ◽  
Vol 18 (3) ◽  
pp. 1219-1220 ◽  
Author(s):  
A.G. Bakirtzis ◽  
P.N. Biskas ◽  
C.E. Zoumas ◽  
V. Petridis
Keyword(s):  
Genetic Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Power

scholarly journals Smart charging for electric vehicles to minimise charging cost

2017 ◽  
Vol 231 (6) ◽  
pp. 526-534 ◽  
Author(s):  
Yue Wang ◽  
David Infield ◽  
Simon Gill
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Low Voltage ◽  
Wholesale Price ◽  
Heuristic Method ◽  
Domain Model ◽  
Smart Charging

This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system are available. Based on this information, an aggregator with access to this data can be employed to minimise electric vehicles charging costs to the owner whilst maintaining acceptable distribution system voltages. In this study, electric vehicle charging is assumed to take place only in the home. A single-phase Low Voltage (LV) distribution network is investigated where the local electric vehicles penetration level is assumed to be 100%. Electric vehicle use patterns have been extracted from the UK Time of Use Survey data with a 10-min resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of electric vehicles charging. A simple and effective heuristic method is proposed to minimise the electric vehicles’ charging cost whilst satisfying the requirement of state of charge for the electric vehicles’ battery. A simulation in OpenDSS over a period of 24 h has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimisation results are compared with those obtained using dynamic optimal power flow.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

Simulation of English text recognition model based on ant colony algorithm and genetic algorithm

2021 ◽  
pp. 1-12
Author(s):  
Fei Long
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Text Recognition ◽  
English Text ◽  
Recognition Model ◽  
Central Node ◽  
Model Based ◽  
Characteristic Points

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.

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