Author(s): Md. Shabbir Hossain, Lariyah Mohd Sidek
Linked Author(s): Md Shabbir Hossain
Keywords: Reservoir release policy, optimization, swarm intelligences, PSO, water resources management
Abstract: Particle swarm optimization is a very well-known method as it has a strong background in optimization field to solve different non-linear and complex problems. This study made a fine tuning in the particle updating process of standard PSO algorithm. The updated algorithm is used to develop and optimize a reservoir release policy for monthly basis. The historical data of inflow to the dam/reservoir has categorized in three different categories (high, medium and low). The problem formation has done on the basis of release and storage constraints. The objective function which was aimed to be minimized has been considered as the water deficit from the release. The monthly releases are taken as the main objective variables and are essentially control the water deficit of the process. The standard form of PSO is then compared with the updated version and the results are analyzed by adopting different performance measuring indicators such as reliability, vulnerability and resilience. These performance measuring indices are calculated from the outcome of the simulation process by feeding the optimization model with the actual historical data of inflow. From the results of the simulation and the value of the indicators, the study shows updated PSO algorithm performs significantly better in optimizing reservoir releases policy
Year: 2017