Optimal Operating Rules for Joint System of Water Supply Reservoir and Seawater Desalination Plant using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@MastersThesis{oai:repository.ust.hk:1783.1-81383,

• author = "Yi Yang",
• title = "Optimal Operating Rules for Joint System of Water Supply Reservoir and Seawater Desalination Plant using Genetic Programming",
• year = "2015",
• school = "HKUST",
• type = "M.Phil",
• address = "Hong Kong",
• month = jul,
• keywords = "genetic algorithms, genetic programming, water-supply, saline water conversion, water resources development, water reuse",
• bibsource = "OAI-PMH server at repository.ust.hk",
• language = "English",
• oai = "oai:repository.ust.hk:1783.1-81383",
• URL = "https://doi.org/10.14711/thesis-b1514746",
• URL = "http://repository.ust.hk/ir/bitstream/1783.1-81383/1/th_redirect.html",
• size = "115 pages",
• abstract = "Due to climate change, population growth and industrial development, there is increasing scarcity of freshwater resources amidst rising demands. In view of this, many coastal places are resorting to seawater desalination as a means of supplementing existing supplies from reservoirs. However, doing so introduces a tradeoff between water supply reliability and cost, as seawater desalination is relatively expensive because of its high energy consumption. Although some studies have been done to combine seawater desalination with other options like reservoir and wastewater reuse for supplying high water demand, they either emphasize too much on economic cost rather than system operation, or lack quantitative investigation into the operation of an integrated or joint system. Thus, a comprehensive model for the operation of a joint system to systematically optimise both water supply reliability and economic efficiency is required. In this study, an optimisation model for the operation of a joint system of a single reservoir and seawater desalination plant was developed for urban water supply. The model aimed to maximise water supply reliability while constraining cost. Taking into account the existing storage of water in the reservoir, the demand for water by various sectors, and current and forecast future inflows to the reservoir, two operating rules that interact with each other were optimised for guiding the operation of the reservoir and seawater desalination plant. Upon attaining the optimal functions, both operational cost and capital cost were calculated on an annual basis for analysis. To solve the above operation model, a genetic programming (GP) iterative tool was designed for the joint system. Using the GPLAB toolbox in MATLAB, genetic programming was applied in an iterative fashion to generate optimal operational rules to govern the releases from the reservoir and water production rates of the desalination plant. In this manner, GP was empowered to optimise the two rules simultaneously, which would not be possible if using GP in a conventional way. Results were obtained for a semi-hypothetical case study in California and analysed to prove the advantage of the joint system and applicability of genetic programming for the purposes of this study. The fitness value was found to have improved by 33percent after 83 iterations in the baseline case. It was demonstrated that due to the assumption that the volume data of current inflows and demands were affected by their volume data one and two time periods before thus forecasting information might be indirectly incorporated into the functions by the incorporation of these variables into the functions. The complex functions generated by the model can be easily calculated using computer programs. The capital cost consisted of 1/3 of the total cost with an equilibrium point at around 500 million dollars per month when it was allocated to each month. But the water demands were too high to be fully met (70percent met), leading to large budget carry overs. In terms of the reservoir performance, the reservoir storage was drawn down before every inflow peak. If the budget was not enough for this expensive way of desalinating water, it had to depend more on releasing water from the reservoir, whose inflows fluctuated in all time periods. As the capacity of desalination plant increases, demand plays a more important role in deciding how much water to be released from the reservoir. The scale expansion of the existing seawater desalination plant could be a very effective but costly way to solve water scarcity problems in coastal city water supply cases, while increasing the reservoir capacity is the most efficient way to reducing water shortages. And the fitness value kept increasing by 83.05percent when the reservoir capacity went up from 3000 million m$^{3}$ in the baseline case to 5000 million m$^{3}$. But still future work needs to be done to incorporate more scenarios to prove the advantage of the joint operation model together with the GP iterative tool.",
• notes = "oai:repository.ust.hk:1783.1-81383",

}

Genetic Programming entries for Yi Yang

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