Literature DB >> 27844241

Application of PSO algorithm in short-term optimization of reservoir operation.

Kazem SaberChenari1, Hirad Abghari2, Hossein Tabari3.   

Abstract

The optimization of the operation of existing water systems such as dams is very important for water resource planning and management especially in arid and semi-arid lands. Due to budget and operational water resource limitations and environmental problems, the operation optimization is gradually replaced by new systems. The operation optimization of water systems is a complex, nonlinear, multi-constraint, and multidimensional problem that needs robust techniques. In this article, the practical swarm optimization (PSO) was adopted for solving the operation problem of multipurpose Mahabad reservoir dam in the northwest of Iran. The desired result or target function is to minimize the difference between downstream monthly demand and release. The method was applied with considering the reduction probabilities of inflow for the four scenarios of normal and drought conditions. The results showed that in most of the scenarios for normal and drought conditions, released water obtained by the PSO model was equal to downstream demand and also, the reservoir volume was reducing for the probabilities of inflow. The PSO model revealed a good performance to minimize the reservoir water loss, and this operation policy can be an appropriate policy in the drought condition for the reservoir.

Entities:  

Keywords:  Iran; Mahabad dam; PSO model; Particle swarm optimization; Reservoir operation

Mesh:

Year:  2016        PMID: 27844241     DOI: 10.1007/s10661-016-5689-1

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  1 in total

1.  Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models.

Authors:  Mohammed Falah Allawi; Othman Jaafar; Firdaus Mohamad Hamzah; Sharifah Mastura Syed Abdullah; Ahmed El-Shafie
Journal:  Environ Sci Pollut Res Int       Date:  2018-04-03       Impact factor: 4.223

  1 in total

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