A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Optimizing Multi-purpose Reservoir Operation Using Particle Swarm Intelligence
Abstract Developing an efficient multi-objective reservoir operation policy is one of the most challenging water resources tasks due to a big number of involved parameters, in addition to the question of proper system representation. Therefore, the aim of the paper is to apply Particle Swarm Optimization (PSO) technique to optimal operation of the Atbara River basin reservoir system. An objective function is defined to minimize the squared difference between targeted and generated hydropower as well as irrigation demands to be solved using a set of tuned (PSO), and then applied in Girba multi-purpose reservoir. The results showed a tangible improvement in power production, up to 15 % increase, satisfying at the same time the irrigation demands. Three performance measures of reliability, resilience, and vulnerability have been applied based on model outputs and required release values that satisfy the system’s demands. All measures indicated an improvement of the system’s performance, exhibiting the reliability increase to 97 %, instead of current 86 %.
Optimizing Multi-purpose Reservoir Operation Using Particle Swarm Intelligence
Abstract Developing an efficient multi-objective reservoir operation policy is one of the most challenging water resources tasks due to a big number of involved parameters, in addition to the question of proper system representation. Therefore, the aim of the paper is to apply Particle Swarm Optimization (PSO) technique to optimal operation of the Atbara River basin reservoir system. An objective function is defined to minimize the squared difference between targeted and generated hydropower as well as irrigation demands to be solved using a set of tuned (PSO), and then applied in Girba multi-purpose reservoir. The results showed a tangible improvement in power production, up to 15 % increase, satisfying at the same time the irrigation demands. Three performance measures of reliability, resilience, and vulnerability have been applied based on model outputs and required release values that satisfy the system’s demands. All measures indicated an improvement of the system’s performance, exhibiting the reliability increase to 97 %, instead of current 86 %.
Optimizing Multi-purpose Reservoir Operation Using Particle Swarm Intelligence
Abdallah, Mohammed (author) / Stamm, Jürgen (author)
2016-01-01
12 pages
Article/Chapter (Book)
Electronic Resource
English
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