Optimization of Water Distribution Network: A Comparison using Genetic Algorithm and Particle Swarm Optimization

2014 ◽  
Author(s):  
Jayrani Cheeneebash ◽  
Reshma Rughooputh ◽  
Ashvin Gopaul ◽  
Khojeswaree Chamilall ◽  
Jovesh Naggea
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Distribution Network ◽  
Particle Swarm ◽  
Water Distribution Network ◽  
Swarm Optimization

Análise de transitórios hidráulicos decorrentes da operação otimizada de estações elevatórias

2020 ◽  
pp. 17-26
Author(s):  
Gustavo Meirelles ◽  
◽  
Aloysio Saliba ◽  
Jorge Tarqui ◽  
Edna Viana ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Particle Swarm ◽  
Water Distribution Network ◽  
Pressure Variation ◽  
Swarm Optimization ◽  
Transient Model ◽  
Demand Pattern ◽  
Steady State Model ◽  
Hydraulic Equipment

Neste trabalho são avaliados os transitórios hidráulicos decorrentes da operação otimizada de uma estação elevatória de uma rede de distribuição de água e os procedimentos operacionais que podem reduzir este problema para assegurar a confiabilidade do sistema. A operação otimizada é obtida utilizando o algoritmo Particle Swarm Optimization (PSO) e simulações em regime permanente, considerando que as bombas estarão operando com sua velocidade de rotação nominal ou desligadas. Em seguida, as manobras de arranque e paragem definidas são utilizadas num modelo em regime transitório para avaliar as variações de pressão decorrentes da operação otimizada. Os resultados obtidos demonstram que as variações de pressão não são elevadas, mas que, a longo prazo, podem ser significativos na redução da vida útil dos equipamentos hidráulicos. Além disso, observou-se que a variação da demanda num modelo transitório pode causar erros significativos, sendo necessária uma modelação cautelosa neste aspeto. In this work, the hydraulic transients resulting from the optimized operation of a pumping station in a water distribution network are studied and operational procedures to reduce this problem and ensure the reliability of the system are evaluated. An optimal pumping scheduling is obtained using the Particle Swarm Optimization (PSO) and a steady state model considering pumps operating only at their nominal rotational speed or switched off. Then, the pumps schedules are used in a transient model to evaluate the pressure surges of the optimized operation. The results showed that the pressure variation is not high but can be relevant in the reduction of service life of the hydraulic equipment. In addition, it was observed that the demand pattern in the transient model can cause significant errors, and its modeling has to be carefully handled.

scholarly journals Electric Vehicle Charging Coordination on Distribution Network by using Particle Swarm Optimization and Genetic Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 5673-5676
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Electric Vehicles ◽  
Distribution Network ◽  
Particle Swarm ◽  
Combustion Method ◽  
Swarm Optimization ◽  
Electric Vehicle Charging ◽  
Electric Distribution Network ◽  
Line Overload

The Electric Vehiclesbecoming very popular in the recent years. Typically, Electric Vehicles propulsion systems come from one or more electrical motors built inside the vehicles. This motor used electricity as energy combustion method. Due to the limited energy storage capacity, Electric Vehicles need to replenish by plugging into an electrical source. The problems appear during multiple Electric Vehicles perform charging process in an Electric Distribution Network. This process willbe causing line overload and efficiency degradation of Distribution Network. In performance to evaluate the potential of different of charging coordination, a classification has been made. The new coordinated process may consider minimum power losses and acceptable voltage limit. The process also needs to define the optimal uncoordinated and coordinated charging point. Therefore, a simulation-based framework will be performed, that use two algorithms which are Particle Swarm Optimization and Genetic Algorithm.

scholarly journals Power System Reconfiguration in Distribution Network for Improving Reliability Using Genetic Algorithm and Particle Swarm Optimization

2021 ◽  
Vol 11 (7) ◽  
pp. 3092
Author(s):  
Omar Kahouli ◽  
Haitham Alsaif ◽  
Yassine Bouteraa ◽  
Naim Ben Ali ◽  
Mohamed Chaabene
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Distribution System ◽  
Distribution Network ◽  
Mathematical Formulation ◽  
Particle Swarm ◽  
Network Reconfiguration ◽  
Power Losses ◽  
Swarm Optimization ◽  
Power Distribution System

This paper presents an optimal method for optimizing network reconfiguration problems in a power distribution system in order to enhance reliability and reduce power losses. Network reconfiguration can be viewed as an optimization problem involving a set of criteria that must be reduced when adhering to various constraints. The energy not supplied (ENS) during permanent network faults and active power losses are the objective functions that are optimized in this study during the reconfiguration phase. These objectives are expressed mathematically and will be integrated into various optimization algorithms used throughout the study. To begin, a mathematical formulation of the objectives to be optimized, as well as all the constraints that must be met, is proposed. Then, to solve this difficult combinatorial problem, we use the exhaustive approach, genetic algorithm (GA), and particle swarm optimization (PSO) on an IEEE 33-bus electrical distribution network. Finally, a performance evaluation of the proposed approaches is developed. The results show that optimizing the distribution network topology using the PSO approach contributed significantly to improving the reliability, node voltage, line currents, and calculation time.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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