Graduation Year

2013

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Kalanithy Vairavamoorthy

Keywords

Decentralized Water Distribution Systems, Decision Making Under Uncertainty, Flexibility Optimization, Genetic Algorithm, Uncertainties

Abstract

With increasing global change pressures such as urbanization and climate change, cities of the future will experience difficulties in efficiently managing scarcer and less reliable water resources. However, projections of future global change pressures are plagued with uncertainties. This increases the difficulty in developing urban water systems that are adaptable to future uncertainty.

A major component of an urban water system is the distribution system, which constitutes approximately 80-85% of the total cost of the water supply system (Swamee and Sharma, 2008). Traditionally, water distribution systems (WDS) are designed using deterministic assumptions of main model input variables such as water availability and water demand. However, these deterministic assumptions are no longer valid due to the inherent uncertainties associated with them. Hence, a new design approach is required, one that recognizes these inherent uncertainties and develops more adaptable and flexible systems capable of using their active capacity to act or respond to future alterations in a timely, performance-efficient, and cost-effective manner.

This study develops a framework for the design of flexible WDS that are adaptable to new, different, or changing requirements. The framework consists of two main parts.

The first part consists of several components that are important in the pre and post--processing of the least-cost design methodology of a flexible WDS. These components include: the description of uncertainties affecting WDS design, identification of potential flexibility options for WDS, generation of flexibility through optimization, and a method for assessing of flexibility. For assessment a suite of performance metrics is developed that reflect the degree of flexibility of a distribution system. These metrics focus on the capability of the WDS to respond and react to future changes. The uncertainties description focuses on the spatial and temporal variation of future demand.

The second part consists of two optimization models for the design of centralized and decentralized WDS respectively. The first model generates flexible, staged development plans for the incremental growth of a centralized WDS. The second model supports the development of clustered/decentralized WDS. It is argued that these clustered systems promote flexibility as they provide internal degrees of freedom, allowing many different combinations of distribution systems to be considered. For both models a unique genetic algorithm based flexibility optimization (GAFO) model was developed that maximizes the flexibility of a WDS at the least cost.

The efficacy of the developed framework and tools are demonstrated through two case study applications on real networks in Uganda. The first application looks at the design of a centralized WDS in Mbale, a small town in Eastern Uganda. Results from this application indicate that the flexibility framework is able to generate a more flexible design of the centralized system that is 4% - 50% less expensive than a conventionally designed system when compared against several future scenarios. In addition, this application highlights that the flexible design has a lower regret under different scenarios when compared to the conventionally designed system (a difference of 11.2m3/US$). The second application analyzes the design of a decentralized network in the town of Aura, a small town in Northern Uganda. A comparison of a decentralized system to a centralized system is performed, and the results indicate that the decentralized system is 24% - 34% less expensive and that these cost savings are associated with the ability of the decentralized system to be staged in a way that traces the urban growth trajectory more closely. The decentralized clustered WDS also has a lower regret (a difference of 17.7m3/US$) associated with the potential future conditions in comparison with the conventionally centralized system and hence is more flexible.

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