scholarly journals Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2961
Author(s):  
Oveis Abedinia ◽  
Mehdi Bagheri
Keyword(s):  
Power System ◽  
Power Distribution ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Test System ◽  
Multi Objective ◽  
Bee Colony ◽  
Crowding Distance ◽  
Proposed Model ◽  
Pareto Criterion

In this article, a novel dynamic economic load dispatch with emission based on a multi-objective model (MODEED) considering demand side management (DSM) is presented. Moreover, the investigation and evaluation of impacts of DSM for the next day are considered. In other words, the aim of economical load dispatch is the suitable and optimized planning for all power units considering different linear and non-linear constrains for power system and generators. In this model, different constrains such as losses of transformation network, impacts of valve-point, ramp-up and ramp-down, the balance of production and demand, the prohibited areas, and the limitations of production are considered as an optimization problem. The proposed model is solved by a novel modified multi-objective artificial bee colony algorithm (MOABC). In order to analyze the effects of DSM on the supply side, the proposed MODEED is evaluated on different scenarios with or without DSM. Indeed, the proposed MOABC algorithm tries to find an optimal solution for the existence function by assistance of crowding distance and Pareto theory. Crowding distance is a suitable criterion to estimate Pareto solutions. The proposed model is carried out on a six-unit test system, and the obtained numerical analyses are compared with the obtained results of other optimization methods. The obtained results of simulations that have been provided in the last section demonstrate the higher efficiency of the proposed optimization algorithm based on Pareto criterion. The main benefits of this algorithm are its fast convergence and searching based on circle movement. In addition, it is obvious from the obtained results that the proposed MODEED with DSM can present benefits for all consumers and generation companies.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI)

2012 ◽  
Vol 61 (2) ◽  
pp. 239-250 ◽  
Author(s):  
M. Kumar ◽  
P. Renuga
Keyword(s):  
Power System ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Stability Index ◽  
Test System ◽  
Unified Power Flow Controller ◽  
Voltage Profile ◽  
Voltage Stability Index

Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI)Transmission line loss minimization in a power system is an important research issue and it can be achieved by means of reactive power compensation. The unscheduled increment of load in a power system has driven the system to experience stressed conditions. This phenomenon has also led to voltage profile depreciation below the acceptable secure limit. The significance and use of Flexible AC Transmission System (FACTS) devices and capacitor placement is in order to alleviate the voltage profile decay problem. The optimal value of compensating devices requires proper optimization technique, able to search the optimal solution with less computational burden. This paper presents a technique to provide simultaneous or individual controls of basic system parameter like transmission voltage, impedance and phase angle, thereby controlling the transmitted power using Unified Power Flow Controller (UPFC) based on Bacterial Foraging (BF) algorithm. Voltage stability level of the system is defined on the Fast Voltage Stability Index (FVSI) of the lines. The IEEE 14-bus system is used as the test system to demonstrate the applicability and efficiency of the proposed system. The test result showed that the location of UPFC improves the voltage profile and also minimize the real power loss.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

Impact of Distributed Generation on the Fault Current in Power Distribution System

2017 ◽  
Vol 6 (2) ◽  
pp. 357
Author(s):  
Zuhaila Mat Yasin ◽  
Izni Nadhirah Sam’ón ◽  
Norziana Aminudin ◽  
Nur Ashida Salim ◽  
Hasmaini Mohamad
Keyword(s):  
Power System ◽  
Distributed Generation ◽  
Power Distribution ◽  
Distribution System ◽  
Test System ◽  
Optimal Location ◽  
Fault Current ◽  
Double Line ◽  
Power Distribution System ◽  
The Impact

<p>Monitoring fault current is very important in power system protection. Therefore, the impact of installing Distributed Generation (DG) on the fault current is investigated in this paper. Three types of fault currents which are single line-to-ground, double line-to-ground and three phase fault are analyzed at various fault locations. The optimal location of DG was identified heuristically using power system simulation program for planning, design and analysis of distribution system (PSS/Adept). The simulation was conducted by observing the power losses of the test system by installing DG at each load buses. Bus with minimum power loss was chosen as the optimal location of DG. In order to study the impact of DG to the fault current, various locations and sizes of DG were also selected. The simulations were conducted on IEEE 33-bus distribution test system and IEEE 69-bus distribution test system. The results showed that the impact of DG to the fault current is significant especially when fault occurs at busses near to DG location.</p>

scholarly journals Presentation of Multi-Skill Workforce Scheduling Model and Solving the Model Using Meta-Heuristic Algorithms

2016 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Iman Fozveh ◽  
Hooman Salehi ◽  
Kamran Mogharehabed
Keyword(s):  
Heuristic Algorithms ◽  
Night Shift ◽  
Workforce Scheduling ◽  
Multi Objective ◽  
Bee Colony ◽  
De Algorithm ◽  
Scheduling Model ◽  
Proposed Model ◽  
Bee Colony Optimization ◽  
Quality Dispersion

<span lang="EN-US">In the present article, a multi-objective mathematical model for scheduling multi-skilled multi-objective workforce has been proposed with the aims of minimizing the number of night-shift engineers, minimizing the total cost of workforce and maximizing the number of engaged workforce. To solve the proposed model for scheduling workforce, bee colony optimization algorithm and DE algorithm have been employed, and in order to investigate the efficiency of these two algorithms, the results have been compared with each other in terms of quality, dispersion and uniformity factors. In order to solve the model three sample problems (40, 70 and 280 workforce) were designed and then solved by the two mentioned algorithms. Bee algorithm is able to find higher-quality answers. Also the results of the comparison of dispersion and uniformity index indicate that bee colony algorithm is able to find answers with more dispersion and more homogeneous than DE algorithm. The comparison of solution time of both algorithms indicate that bee colony algorithm is faster than DE algorithm and needs less time to reach quality, dispersed and homogenous answers.</span>

An Improved Adaptive Evolutionary Algorithm for Multi-Objective Optimization

2013 ◽  
Vol 303-306 ◽  
pp. 1494-1500
Author(s):  
Jian Wei Wang ◽  
Jian Ming Zhang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Relative Fitness ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fitness Value ◽  
Crossover Probability ◽  
Optimal Solution Set ◽  
Adaptive Evolutionary Algorithms

Aiming at effectively overcoming the disadvantages of traditional evolutionary algorithm which converge slowly and easily run into local extremism, an improved adaptive evolutionary algorithms is proposed. Firstly, in order to choose the optimal objective fitness value from the population in every generation, the absolute and relative fitness are defined. Secondly, fuzzy technique is adopted to adjust the weights of objective functions, crossover probability, mutation probability, crossover positions and mutation positions during the iterative process. Finally, three classical test functions are given to illustrate the validity of improved adaptive evolutionary algorithm, simulation results show that the diversity and practicability of the optimal solution set are better by using the proposed method than other multi-objective optimization methods.

scholarly journals PERFORMANCE ANALYSIS OF OPTIMIZATION METHODS FOR SOLVING TRAVELING SALESMAN PROBLEM

2021 ◽  
pp. 69-75
Author(s):  
Chandra Agung ◽  
Natalia Christine
Keyword(s):  
Approximate Method ◽  
Traveling Salesman Problem ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Traveling Salesman ◽  
Exact Method ◽  
Problem Size ◽  
Exact Methods ◽  
Approximate Methods ◽  
Bee Colony

The subject of this research is distance and time of several city tour problems which known as traveling salesman problem (tsp). The goal is to find out the gaps of distance and time between two types of optimization methods in traveling salesman problem: exact and approximate. Exact method yields optimal solution but spends more time when the number of cities is increasing and approximate method yields near optimal solution even optimal but spends less time than exact methods. The task in this study is to identify and formulate each algorithm for each method, then to run each algorithm with the same input and to get the research output: total distance, and the last to compare both methods: advantage and limitation.  Methods used are Brute Force (BF) and Branch and Bound (B&B) algorithms which are categorized as exact methods are compared with Artificial Bee Colony (ABC), Tabu Search (TS) and Simulated Annealing (SA) algorithms which are categorized as approximate methods or known as a heuristics method. These three approximate methods are chosen because they are effective algorithms, easy to implement and provide good solutions for combinatorial optimization problems. Exact and approximate algorithms are tested in several sizes of city tour problems: 6, 9, 10, 16, 17, 25, 42, and 58 cities. 17, 42 and 58 cities are derived from tsplib: a library of sample instances for tsp; and others are taken from big cities in Java (West, Central, East) island. All of the algorithms are run by MATLAB program. The results show that exact method is better in time performance for problem size less than 25 cities and both exact and approximate methods yield optimal solution. For problem sizes that have more than 25 cities, approximate method – Artificial Bee Colony (ABC) yields better time which is approximately 37% less than exact and deviates 0.0197% for distance from exact method. The conclusion is to apply exact method for problem size that is less than 25 cities and approximate method for problem size that is more than 25 cities. The gap of time will be increasing between two methods when sample size becomes larger.

scholarly journals Implementation of Hybrid ABC-PSO Algorithm for Directional Overcurrent Relays Coordination Problem

2020 ◽  
Vol 9 (7) ◽  
pp. 721-727
Keyword(s):  
Power System ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Fast Response ◽  
Vital Role ◽  
Coordination Problem ◽  
Bee Colony ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Protective Relays

In modern power system, protective relays are playing a vital role for protection of the whole system. The efficiency and reliability of whole protection system depends upon the combined and coordinated operation of protective devices such as relays, circuit breakers etc. Moreover, both types of relays viz., primary and backup relays have been used for smooth and reliable operation of the power system from years. A primary directional over current relay (DOCR) is setup for the fast response of any faulty condition. If it fails, then backup relay perform the same task after some time gap. Three different setting such as plug-setting multiplier (PSM), pickup current settings and time multiplier setting (TMS) are required of performing the operation. In this paper, three very popular swarm based meta-heuristic such as particle swarm optimization (PSO), artificial bee colony (ABC) and a recent hybridization of both, i.e., hybrid ABC-PSO have been implemented for the calculation of optimal coordination problem. This coordination problem is treated for continuous settings optimization for TMS and pickup current. An IEEE 8 bus system without grid has been opted for validation of the results. It is evident from the study that the hybrid ABC-PSO based proves to generate optimal solution providing better convergence rate as compared to individual PSO and ABC algorithm.

scholarly journals Cost-Effective Outage Management in Smart Grid under Single, Multiple, and Critical Fault Conditions through Teaching–Learning Algorithm

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6205
Author(s):  
Thiruvenkadam S ◽  
In-Ho Ra ◽  
Hyung-Jin Kim
Keyword(s):  
Power System ◽  
Power Distribution ◽  
Distribution System ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Cost Effective ◽  
Second Phase ◽  
Service Restoration ◽  
Teaching Learning ◽  
Continuous Power

A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System.

scholarly journals Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2470 ◽  
Author(s):  
Alamaniotis ◽  
Gatsis
Keyword(s):  
Smart Grid ◽  
Optimality Theory ◽  
Power Distribution ◽  
Distribution System ◽  
Electric Energy ◽  
Optimal Solution ◽  
Nsga Ii ◽  
Power Distribution System ◽  
Multi Objective ◽  
Grid Partition

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition.

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