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Title:	Reservoir optimization using artificial bee colony (ABC) and gravitational search algorithm (GSA) with evaluation for reservoir storage expansion
Authors:	Asmadi Ahmad @ Hasan (P65718)
Supervisor:	Siti Fatin Mohd. Razali, Dr.
Keywords:	Dams Algorithms
Issue Date:	23-Feb-2018
Description:	Reservoirs and dams have been built around the world for centuries. Their purpose is to assist with irrigation, provide water supply for domestic and industrial use, generate hydropower, and mitigate against floods. However, after several years in operation, a reservoir might be forced to operate at the limit of its standard operating procedure (SOP) because of climate change and increasing water demand. This problem can be alleviated by increasing the reservoir storage capacity and optimizing the present operation of the reservoir. Nevertheless, these approaches should be evaluated for its effectiveness. The main objectives of this study are to present the application of two newly developed algorithms, artificial bee colony (ABC) and gravitational search algorithm (GSA), in reservoir optimization. The ABC method is an algorithm based on the foraging behaviour of bees, whereas GSA imitates the gravitational processes. These algorithms were used to minimize the irrigation release deficit for Timah Tasoh Dam, located in the northern part of Peninsular Malaysia. The dam is presently being raised to increase the reservoir capacity from 40 million cubic meters (MCM) to 64.4 MCM because of the increasing water demand. The proposed release policies were developed for two case studies: 1) present water demand with the existing reservoir capacity, and 2) the new water demand with a larger reservoir capacity. Simulations were conducted based on the historical inflow to replicate the reservoir operation, based on the release policy proposed by ABC and GSA for existing and new reservoir capacity. The performance of ABC and GSA was evaluated by reliability, resiliency and vulnerability performance indexes. From the study ABC approach performed well in solving the objective function of reservoir optimization better than GSA. ABC showed faster convergence rate at 60 iteration compared to 300 iteration for GSA, higher accuracy in the fitness function and 3 times faster in computational times than GSA. From simulation results for current storage ABC release policy achieved a better performance on the vulnerability index by 6.20% as compared to GSA, whereas GSA showed a better result for resiliency by 30.00% compared to ABC. Both ABC and GSA periodical reliability results showed better performance which are 53.80% and 50.00% respectively compared to present SOP (41.3%). Increasing 61% storage capacity and 26% annual demand from existing reservoir might improve the reliability of GSA from 50.00% to 59.78%, while ABC show a decreasing trend from 53.80% to 52.17%. Both vulnerability indexes of ABC and GSA are 33.73% and 43.78% respectively. ABC shows a better result in resiliency, i.e.15.38% increment as compared to GSA which only improved about 11.76% for new storage condition. These result showed that by increasing the storage volume, it will improved the reliability and resiliency indexes of the reservoir operation but the vulnerability index will decrease.,Ph.D.
Pages:	159
Call Number:	QA76.9.A43A845 2018 3 tesis
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

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