A Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration

2012 ◽  
pp. 130-142
Author(s):  
Armin Ebrahimi Milani ◽  
Mahmood Reza Haghifam
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Fuzzy Set ◽  
Distribution Network ◽  
Heuristic Rules ◽  
Multi Objective ◽  
Operation Process ◽  
Distribution Feeder Reconfiguration ◽  
The Status ◽  
Linear Objective Functions

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. This paper presents an algorithm for network recon-figuration based on the heuristic rules and fuzzy multi objective approach where each objective is normalized with inspiration from fuzzy set to cause optimization more flexible and formulized as a unique multi objective function. Also, the genetic algorithm is used for solving the suggested model, in which there is no risk of non-linear objective functions and constraints. The effectiveness of the proposed method is demonstrated through several examples in this paper.

A Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration

2010 ◽  
Vol 1 (2) ◽  
pp. 60-73 ◽  
Author(s):  
Armin Ebrahimi Milani ◽  
Mahmood Reza Haghifam
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Set ◽  
Objective Functions ◽  
Suggested Model ◽  
Heuristic Rules ◽  
Multi Objective ◽  
Operation Process ◽  
Distribution Feeder Reconfiguration ◽  
The Status ◽  
Linear Objective Functions

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. This paper presents an algorithm for network recon-figuration based on the heuristic rules and fuzzy multi objective approach where each objective is normalized with inspiration from fuzzy set to cause optimization more flexible and formulized as a unique multi objective function. Also, the genetic algorithm is used for solving the suggested model, in which there is no risk of non-linear objective functions and constraints. The effectiveness of the proposed method is demonstrated through several examples in this paper.

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.     

scholarly journals Investigation of Optimal PV Allocation to Minimize System Losses and Improve Voltage Stability for Distribution and Transmission Networks Using MATLAB and DigSilent

2021 ◽  
Vol 9 ◽  
Author(s):  
Mohamed Abdul Rasheed ◽  
Renuga Verayiah
Keyword(s):  
Objective Function ◽  
Radial Distribution ◽  
Power Factor ◽  
Analytical Method ◽  
Distribution Network ◽  
Computation Time ◽  
Transmission Network ◽  
Voltage Profile ◽  
Multi Objective ◽  
Test Systems

Electricity generation from renewable energy sources such as solar energy is an emerging sustainable solution. In the last decade, this sustainable source was not only being used as a source of power generation but also as distributed generation (DG). Many literatures have been published in this field with the objective to minimize losses by optimizing the DG size and location. System losses and voltage profile go hand-in-hand; as a result, when system losses are minimized, eventually the voltage profile improves. With improvement in inverter technologies, PV-DG units do not have to operate at a unity power factor. The majority of proposed algorithms and methods do not consider power factor optimization as a necessary optimization. This article aims to optimize the size, location, and power factor of PV-DG units. The simulations are performed on the IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. The methodologies developed in this article are divided into two sections. The first section aims to optimize the PV-DG size and location. A multi-objective function is developed by using system losses and a voltage deviation index. Genetic algorithm (GA) is used to optimize the multi-objective function. Next, analytical processes are developed for verification. The second section aims to further enhance PV-DG by optimizing the power factor of PV-DG. The simulation is performed for static load in both systems, which are the IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. A mathematical analytical method was developed, and it was found to be sufficient to optimize the power factor of the PV-DG unit. The results obtained show that voltage stability indices help minimize the computation time by determining the optimal locations for DG placement in both networks. In addition, the GA method attained faster convergence than the analytical method and hence is the best optimal sizing for both test systems with minimum computation time. Additionally, the optimization of the power factor for both test systems has demonstrated further improvement in the voltage profile and loss minimization. In conclusion, the proposed methodology has shown promising results for both transmission and distribution networks.

Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

2022 ◽  
pp. 014233122110667
Author(s):  
Tufan Dogruer ◽  
Mehmet Serhat Can
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Pid Controller ◽  
Robustness Analysis ◽  
Optimal Solution ◽  
Voltage Regulator ◽  
Fuzzy Pid ◽  
Multi Objective ◽  
Fuzzy Pid Controller ◽  
Automatic Voltage Regulator

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

scholarly journals Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives

2019 ◽  
Vol 9 (1) ◽  
pp. 2827-2831
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Parallel Machines ◽  
Optimization Techniques ◽  
Computational Time ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Mutation Operators ◽  
Crossover Probability ◽  
Wide Range

Optimization of multi objective function gain the importance in the scheduling process. Many classical techniques are available to address the multi objective functions but the solutions yield the unsatisfactory results when the problem becomes complex and large. Evolutionary algorithm would be the solution for such problems. Genetic algorithm is adaptive heuristic search algorithms and optimization techniques that mimic the process of natural evolution. Genetic algorithms are a very effective way of obtaining a reasonable solution quickly to a complex problem. The genetic algorithm operators such as selection method, crossover method, crossover probability, mutation operators and stopping criteria have an effect on obtaining the reasonably good solution and the computational time. Partially mapped crossover operators are used to solve the problem of the traveling salesman, planning and scheduling of the machines, etc., which are having a wide range of solutions. This paper presents the effect of crossover probability on the performance of the genetic algorithm for the bi-criteria objective function to obtain the best solution in a reasonable time. The simulation on a designed genetic algorithm was conducted with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, found that results were converging for the crossover probability of 0.6 with the computational time of 3.41 seconds.

scholarly journals A complexity task of optimization in logistic distribution: A new approach to the green multi-objective vehicle routing problem

2022 ◽  
Vol 38 (1) ◽  
Author(s):  
Ferreira J. ◽  
Steiner M.
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Logistic Distribution ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
The Difference ◽  
Stage 1

Logistic distribution involves many costs for organizations. Therefore, opportunities for optimization in this respect are always welcome. The purpose of this work is to present a methodology to provide a solution to a complexity task of optimization in Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP). The methodology, illustrated using a case study (employee transport problem) and instances from the literature, was divided into three stages: Stage 1, “data treatment”, where the asymmetry of the routes to be formed and other particular features were addressed; Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II); and, finally, Stage 3, “analysis of the results”, with a comparison of the algorithms. Using the same parameters as the current solution, an optimization of 5.2% was achieved for Objective Function 1 (OF{\displaystyle _{1}}; minimization of CO{\displaystyle _{2}} emissions) and 11.4% with regard to Objective Function 2 (OF{\displaystyle _{2}}; minimization of the difference in demand), with the proposed CWNSGA-II algorithm showing superiority over the others for the approached problem. Furthermore, a complementary scenario was tested, meeting the constraints required by the company concerning time limitation. For the instances from the literature, the CWNSGA-II and CWTSNSGA-II algorithms achieved superior results.

scholarly journals Design optimization of water distribution networks: real-world case study with penalty-free multi-objective genetic algorithm using pressure-driven simulation

Water SA ◽  
2020 ◽  
Vol 46 (3 July) ◽  
Author(s):  
Tiku T Tanyimboh ◽  
Alemtsehay G Seyoum
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Real World ◽  
Distribution Systems ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Systems ◽  
Active Constraint ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Water distribution systems are an integral part of the economic infrastructure of modern-day societies. However, previous research on the design optimization of water distribution systems generally involved few decision variables and consequently small solution spaces; piecemeal-solution methods based on pre-processing and search space reduction; and/or combinations of techniques working in concert. The present investigation was motivated by the desire to address the above-mentioned issues including those associated with the lack of high-performance computing (HPC) expertise and limited access in developing countries. More specifically, the article’s aims are, firstly, to solve a practical water distribution network design optimization problem and, secondly, to develop and demonstrate a generic multi-objective genetic algorithm capable of achieving optimal and near-optimal solutions on complex real-world design optimization problems reliably and quickly. A multi-objective genetic algorithm was developed that applies sustained and extensive exploration of the active constraint boundaries. The computational efficiency was demonstrated by the small fraction of 10-245 function evaluations relative to the size of the solution space. Highly competitive solutions were achieved consistently, including a new best solution. The water utility’s detailed distribution network model in EPANET 2 was used for the hydraulic simulations. Therefore, with some additional improvements, the optimization algorithm developed could assist practitioners in day-to-day planning and design.

Distribution Network Restoration Reconfiguration Based on an PSO-ACO Hybrid Approach

2013 ◽  
Vol 313-314 ◽  
pp. 1327-1330
Author(s):  
Ming Guang Zhang ◽  
Yun Yun Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Distribution Network ◽  
Hybrid Approach ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Network Restoration ◽  
Multi Objective ◽  
Searching Strategy

This paper introduces the particle swarm Optimization (PSO) algorithm and the ant colony optimization (ASO) algorithm to solve the optimal problem of distribution network restoration reconfiguration. In the solving process, propose a multi objective function which contains the minimize line losses and minimize number of switching operations. In the time, simplify distribution and received the feasible searching strategy improved the searching speed. Because parameters has an important influence on ACO algorithm, this method use the PSO optimizate the parameters α and β of ACO. Take the parameters as the position of PSO. This method overcomed the parameters on the influence of the ACO and improved the global searching capability. Simulation on a typical 33 node case is conducted, the results show this method has a good performance of global searching.

scholarly journals MULTI-OBJECTIVE OPTIMISATION MODEL OF SHUTTLE-BASED STORAGE AND RETRIEVAL SYSTEM

Transport ◽  
2016 ◽  
Vol 32 (2) ◽  
pp. 120-137 ◽  
Author(s):  
Matej Borovinšek ◽  
Banu Y. Ekren ◽  
Aurelija Burinskienė ◽  
Tone Lerher
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Retrieval System ◽  
Early Stage ◽  
Optimization Procedure ◽  
Design Concept ◽  
Nsga Ii ◽  
Flexible Tool ◽  
Multi Objective ◽  
Storage And Retrieval

This paper presents a multi-objective optimisation solution procedure for the design of the Shuttle-Based Storage and Retrieval System (SBS/RS). An efficient SBS/RS design should take into account multi-objectives for optimization. In this study, we considered three objective functions in the design concept which are the minimization of average cycle time of transactions (average throughput time), amount of energy (electricity) consumption and total investment cost. By also considering the amount of energy consumption as an objective function for minimization, we aimed to contribute to an environmentally friendly design concept. During the optimization procedure, we considered seven design variables as number of aisles, number of tiers, number of columns, velocities of shuttle carriers, acceleration/deceleration of shuttle carriers, velocity of the elevators lifting tables and acceleration/deceleration of the elevators lifting tables. Due to the non-linear property of the objective function, we utilized the Non-Dominated Sorting Genetic Algorithm II (NSGA II) genetic algorithm for facilitating the solution. Lastly, we searched Pareto optimal solutions to find out the optimum results. We believe that this study provides a useful and a flexible tool for warehouse planners and designers, while choosing a particular type of SBS/RS at the early stage of the warehouse design.

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