Dissertations / Theses on the topic 'Water distribution systems; multiobjective optimization'

Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Aral, Mustafa; Committee Member: Guan, Jiabao; Committee Member: Jang, Wonyong; Committee Member: Kim, Seong-Hee; Committee Member: Uzer, Turgay. Part of the SMARTech Electronic Thesis and Dissertation Collection.

O dimensionamento otimizado de sistemas de distribuição de águas tem originado centenas de trabalhos científicos nas últimas quatro décadas. Vários pesquisadores têm buscado encontrar uma metodologia capaz de dimensionar essas redes considerando diversos aspectos e incertezas características desse tipo de projeto. No entanto, os resultados da maioria das metodologias desenvolvidas não podem ser aplicados na prática. O objetivo deste trabalho é elaborar uma metodologia de dimensionamento de redes de distribuição de água considerando um enfoque multiobjetivo. A metodologia desenvolvida considera três aspectos referentes ao projeto desses sistemas: custo; confiabilidade e perdas por vazamentos. Para tanto, empregou-se um método de otimização multiobjetivo baseado em algoritmos genéticos para a geração do conjunto de soluções não-dominadas e um método multicriterial para escolha da alternativa final. Para representar os objetivos do problema, foram testadas nove funções: custo, vazamentos, entropia, resiliência, tolerância à falha, expansibilidade, efeito do envelhecimento e resilientropia, sendo que sete destas são específicas para a representação da confiabilidade. Para se avaliar as alternativas geradas foi desenvolvido um modelo de análise hidráulica que fosse capaz de trabalhar com vazamentos e com demandas dependente da pressão. Os métodos escolhidos foram o Híbrido de Nielsen e o Gradiente. Das funções testadas, a resilientropia, proposta originalmente neste trabalho, foi a que melhor se ajustou ao conceito formal de confiabilidade, representado pela função tolerância. Os resultados encontrados pela metodologia mostraram-se promissores, uma vez esta foi capaz de encontrar redes eficientes ao final das simulações.
The topic \"Optimized design of water distribution systems\" has generated hundreds of scientific publications in the last four decades. Several researchers have searched for a technology which would take into account a variety of aspects and uncertainties innate to the design of such networks. However, the results of most methodologies developed are not practical. The objective of this work is to develop a methodology for water distribution systems design that has a multi-objective focus. The methodology developed focuses in three aspects of the design of such systems: cost, reliability and losses by leaking. A multiobjective optimization method based on generic algorithms, generating a set of non-defined solutions, and a multi-criteria method for choosing the final alternative, was employed. Nine functions representing the objectives of the problem (method) were tested: cost, leakages, entropy, resilience, failure tolerance, expansibility, aging effect and resilienthropy, seven of which are specific to representing reliability. In order to evaluate the generated alternatives, a hydraulic analysis model, that could handle leakages and pressure dependent demands, was developed. The chosen methods were Nielsen\'s Hybrid, and the Gradient. Of all tested functions, resilientropy, originally proposed in this work, proved to be the one best adjusted to the formal concept of reliability, represented by the tolerance function. The results obtained by this methodology are promising, as they produced efficient distribution networks at the end of the simulations performed.

The burdensome capital cost of urban water distribution systems demands the use of efficient optimization methods capable of finding a relatively inexpensive design that guarantees a minimum functionality under all conditions of operation. The combinatorial and nonlinear nature of the optimization problem involved accepts no definitive method of solution. Adaptive search methods are well fitted for this type of problem (to which more formal methods cannot be applied), but their computational requirements demand the development and implementation of additional heuristics to find a satisfactory solution. This work seeks to employ adaptive search methods to enhance the search process used to find the optimal design of any water distribution system. A first study presented here introduces post-optimization heuristics that analyze the best design obtained by a genetic algorithm--arguably the most popular adaptive search method--and perform an ordered local search to maximize further cost savings. When used to analyze the best design found by a genetic algorithm, the proposed post-optimization heuristics method successfully achieved additional cost savings that the genetic algorithm failed to detect after an exhaustive search. The second study herein explores various ways to improve artificial neural networks employed as fast estimators of computationally intensive constraints. The study presents a new methodology for generating any large set of water supply networks to be used for the training of artificial neural networks. This dataset incorporates several distribution networks in the vicinity of the search space in which the genetic algorithm is expected to focus its search. The incorporation of these networks improved the accuracy of artificial neural networks trained with such a dataset. These neural networks consistently showed a lower margin of error than their counterparts trained with conventional training datasets populated by randomly generated distribution networks.

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.

The application of optimization to Water Distribution Systems encompasses the use of computer-based techniques to problems of many different areas of system design, maintenance and operational management. As well as laying out the configuration of new WDS networks, optimization is commonly needed to assist in the rehabilitation or reinforcement of existing network infrastructure in which alternative scenarios driven by investment constraints and hydraulic performance are used to demonstrate a cost-benefit relationship between different network intervention strategies. Moreover, the ongoing operation of a WDS is also subject to optimization, particularly with respect to the minimization of energy costs associated with pumping and storage and the calibration of hydraulic network models to match observed field data. Increasingly, Evolutionary Optimization techniques, of which Genetic Algorithms are the best-known examples, are applied to aid practitioners in these facets of design, management and operation of water distribution networks as part of Decision Support Systems (DSS). Evolutionary Optimization employs processes akin to those of natural selection and “survival of the fittest” to manipulate a population of individual solutions, which, over time, “evolve” towards optimal solutions. Such algorithms are characterized, however, by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of water networks incurs significant computational overheads, can limit the applicability and scalability of this technology in this domain. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to Water Distribution Systems. A number of new procedures are presented for improving the performance of such algorithms when applied to complex engineering problems. These techniques approach the problem of minimising the impact of the inherent computational complexity of these problems from a number of angles. A novel genetic representation is presented which combines the algorithmic simplicity of the classical binary string of the Genetic Algorithm with the performance advantages inherent in an integer-based representation. Further algorithmic improvements are demonstrated with an intelligent mutation operator that “learns” which genes have the greatest impact on the quality of a solution and concentrates the mutation operations on those genes. A technique for implementing caching of solutions – recalling the results for solutions that have already been calculated - is demonstrated to reduce runtimes for Genetic Algorithms where applied to problems with significant computation complexity in their evaluation functions. A novel reformulation of the Genetic Algorithm for implementing robust stochastic optimizations is presented which employs the caching technology developed to produce an multiple-objective optimization methodology that demonstrates dramatically improved quality of solutions for given runtime of the algorithm. These extensions to the Genetic Algorithm techniques are coupled with a supporting software library that represents a standardized modelling architecture for the representation of connected networks. This library gives rise to a system for distributing the computational load of hydraulic simulations across a network of computers. This methodology is established to provide a viable, scalable technique for accelerating evolutionary optimization applications.

This study gives a description about the development of a computer model, RealPipe, which relates genetic algorithm (GA) to the well known problem of least-cost design of water distribution network. GA methodology is an evolutionary process, basically imitating evolution process of nature. GA is essentially an efficient search method basically for nonlinear optimization cases. The genetic operations take place within the population of chromosomes. By means of various operators, the genetic knowledge in chromosomes change continuously and the success of the population progressively increases as a result of these operations. GA optimization is also well suited for optimization of water distribution systems, especially large and complex systems. The primary objective of this study is optimization of a water distribution network by GA. GA operations are realized on a special program developed by the author called RealPipe. RealPipe optimizes given water network distribution systems by considering capital cost of pipes only. Five operators are involved in the program algorithm. These operators are generation, selection, elitism, crossover and mutation. Optimum population size is found to be between 30-70 depending on the size of the network (i.e. pipe number) and number of commercially available pipe size. Elitism rate should be around 10 percent. Mutation rate should be selected around 1-5 percent depending again on the size of the network. Multipoint crossover and higher rates are advisable. Also pressure penalty parameters are found to be much important than velocity parameters. Below pressure penalty parameter is the most important one and should be roughly 100 times higher than the other. Two known networks of the literature are examined using RealPipe and expected results are achieved. N8.3 network which is located in the northern side of Ankara is the case study. Total cost achieved by RealPipe is 16.74 percent lower than the cost of the existing network
it should be noted that the solution provided by RealPipe is hydraulically improved.

ABSTRACT DESIGN OF WATER DISTRIBUTION SYSTEM BY OPTIMIZATION USING RELIABILITY CONSIDERATIONS Akdogan, Tevfik Department of Civil Engineering Supervisor : Assoc. Prof. Dr. Nuri Merzi April 2005, 91 pages In spite of a wide research, design of water distribution networks are not realized using optimization techniques. One reason for this fact is, design of water distribution networks is evaluated, mostly, as a least-cost optimization problem where pipe diameters being the only decision variables. The other motivation for preferring the traditional modeling practice is that, existing optimization algorithms are not presented to the user as friendly as it should be. In fact, water distribution systems are very complex systems such that it is not easy to obtain least-cost design systems considering other constraints such as reliability, in addition to classical constraints related to hydraulic feasibility, satisfaction of nodal demands and requirement of nodal pressures. This study presents a user-friendly package concerning the design of water distribution networks by optimization using reliability considerations
this works employs the algorithm proposed by Goulter and Coals (1986). At the end, a skeletonized network design is offered
various costs are estimated in regard to the degree of reliability.

Interruption to water services and low water pressure conditions are commonly observed problems in water distribution systems (WDSs). Of particular concern are the unplanned events, such as pipe bursts. The current regulation in the UK requires water utilities to provide reliable water service to consumers resulting in as little as possible interruptions and of as short possible duration. All this pushes water utilities toward developing and using smarter responses to these events, based on advanced tools and solutions. All with the aim to change network management style from reactive to a proactive, and reduce water losses, optimize energy use and provide better services for consumers. This thesis presents a novel methodology for efficient and effective operational, short time response to an unplanned failure event (such as pipe burst) in a WDS. The proposed automated, near real-time operational response methodology consists of isolating the failure event followed by the recovery of the affected system area by restoring the flows and pressures to normal conditions. The isolation is typically achieved by manipulating the relevant on/off valves that are located closely to the event location. The recovery involves selecting an optimal combination of suitable operational network interventions. These are selected from a number of possible options with the aim to reduce the negative impact of the failure over a pre-specified time horizon. The intervention options considered here include isolation valve manipulations, changing the pressure reducing valve’s (PRV) outlet pressure and installation and use of temporary overland bypasses from a nearby hydrant(s) in an adjacent, unaffected part of the network. The optimal mix of interventions is identified by using a multi-objective optimization approach driven by the minimization of the negative impact on the consumers and the minimization of the corresponding number of operational interventions (which acts as a surrogate for operational costs). The negative impact of a failure event was quantified here as a volume of water undelivered to consumers and was estimated by using a newly developed pressure-driven model (PDM) based hydraulic solver. The PDM based hydraulic solver was validated on a number of benchmark and real-life networks under different flow conditions. The results obtained clearly demonstrate its advantages when compared to a number of existing methods. The key advantages include the simplicity of its implementation and the ability to predict network pressures and flows in a consistently accurate, numerically stable and computationally efficient manner under both pressure-deficient and normal-flow conditions and in both steady-state and extended period simulations. The new real-time operational response methodology was applied to a real world water distribution network of D-Town. The results obtained demonstrate the effectiveness of the proposed methodology in identifying the Pareto optimal network type intervention strategies that could be ultimately presented to the control room operator for making a suitable decision in near real-time.

Two of the most important environmental challenges in the 21st century are to protect the quality of fresh water resources and to utilize renewable energy sources to lower greenhouse gas emissions. This study contributes to the solution of the first challenge by providing methodologies for optimal design of real-time water quality monitoring systems and interpretation of data supplied by the monitoring system to identify potential pollution sources in river networks. In this study, the optimal river water quality monitoring network design aspect of the overall monitoring program is addressed by a novel methodology for the analysis of this problem. In this analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. The data collected from these monitoring stations can be used to identify contamination source locations. This study suggests a methodology that utilizes a classification routine which associates the observations on a contaminant spill with one or more of the candidate spill locations in the river network. This approach consists of a training step followed by a sequential elimination of the candidate spill locations which lead to the identification of potential spill locations. In order to contribute the solution of the second environmental challenge, this study suggests utilizing available excess energy in water distribution systems by providing a methodology for optimal design of energy recovery systems. The energy recovery in water distribution systems is possible by using micro hydroelectric turbines to harvest available excess energy inevitably produced to satisfy consumer demands and to maintain adequate pressures. In this study, an optimization approach for the design of energy recovery systems in water distribution networks is proposed. This methodology is based on finding the best locations for micro hydroelectric plants in the network to recover the excess energy. Due to the unsteady nature of flow in water distribution networks, the proposed methodology also determines optimum operation schedules for the micro turbines.

As a result of the increasing emphasis placed on water companies to conform to the stringent performance standards in supplying demands within a constrained financial budget, the application of optimization has inevitably become an integral part of managing a water distribution system (WDS) right from the initial phase of designing a new system to the latter stage of the network where rehabilitation and upgrading works are a necessity. This also includes the on-going operation of the WDS in particular the minimization of energy costs related to pumping and storage. This thesis is concerned with the development and application of a new multi-objective genetic algorithm in optimizing the design, operation and long term rehabilitation and upgrading of the WDS.The novelty and originality of the work done as part of this research are presented next. A seamless, augmented version of the renowned EPANET 2 with pressure dependent analysis (PDA) functionality has been developed. It integrates within the hydraulic engine a continuous nodal pressure-flow function coupled with a line search and backtracking procedure which greatly enhances the algorithm’s overall convergence rate and robustness. The hydraulic simulator is termed “EPANET-PDX” (pressure-dependent extension) herein and is capable of effectively modelling networks under pressure deficient situations which the demand driven analysis based EPANET 2 fails to accurately analyse. In terms of computational efficiency, the performance of EPANET-PDX compares very favourably to EPANET 2. Simulations of real life networks consisting of multiple sources, pipes, valves and pumps were successfully executed with no convergence complications. The simulator depicts excellent modelling performance while analysing both normal and abnormal operating conditions of the WDSs. The accuracy of the generated PDA results has been explicitly validated and verified. An optimization model for the optimal design and upgrading of WDS involving both the operation of multiple pumps and the sizing and location of multiple tanks is developed. The model couples a new boundary convergent multi-objective genetic algorithm to the highly efficient EPANET-PDX simulator which, inherently,automatically accounts for the node pressure constraints as well as the conservation of mass and energy. With accurate PDA, the direct application of the standard extended period simulation enables pump scheduling and tank sizing and siting to be seamlessly incorporated into the optimization without the need for any extraneous methodology or manual intervention. The significant advantage of this model is that it eliminates the need for ad-hoc penalty functions, additional “boundary search” parameters, or special constraint handling procedures. No operator intervention, parameter calibration and trial runs are required. Conceptually, the approach is straightforward and probably the simplest hitherto. The model is applied to several benchmark networks yielding superior results in terms of the initial network construction cost and the number of hydraulic simulations required. The above-mentioned optimization model is extended to form a module for the optimal long term design, upgrading and rehabilitation of WDSs. The multi-criteria problem is set up in a multi-objective frame work i.e. to minimize the capital cost,rehabilitation and upgrading costs, whilst maximizing the network hydraulic performance. A straightforward approach for incorporating reliability measures without further complicating the optimization formulation is utilised and its robustness validated. The effect of deterioration of both the structural integrity and hydraulic capacity of pipes over time is explicitly modelled. The model automatically determines the most cost effective strategy which includes the identification of pipes to be upgraded, the upgrading or rehabilitation options and the timing for the upgrade to be implemented. A real life network in Wobulenzi (Uganda) is used to demonstrate the effectiveness of the model. Results obtained demonstrated major improvements over previous work using the classical linear programming.

Due to its practical importance and inherent complexity, the optimisation of distribution networks for supplying drinking water has been the subject of extensive study for the past 30 years. The optimization is governed by sizing the pipes in the water distribution network (WDN) and / or optimises specific parts of the network such as pumps, tanks etc. or try to analyse and optimise the reliability of a WDN. In this thesis, the author has analysed two different WDNs (Anytown City and Cabrera city networks), trying to solve and optimise a multi-objective optimisation problem (MOOP). The main two objectives in both cases were the minimisation of Energy Cost (€) or Energy consumption (kWh), along with the total Number of pump switches (TNps) during a day. For this purpose, a decision support system generator for Multi-objective optimisation used. Its name is GANetXL and has been developed by the Center of Water System in the University of Exeter. GANetXL, works by calling the EPANET hydraulic solver, each time a hydraulic analysis has been fulfilled. The main algorithm used, was a second-generation algorithm for multi-objective optimisation called NSGA_II that gave us the Pareto fronts of each configuration. The first experiment that has been carried out was the network of Anytown city. It is a big network with a pump station of four fixed speed parallel pumps that are boosting the water dynamics. The main intervention was to change these pumps to new Variable speed driven pumps (VSDPs), by installing inverters capable to diverse their velocity during the day. Hence, it’s been achieved great Energy and cost savings along with minimisation in the number of pump switches. The results of the research are thoroughly illustrated in chapter 7, with comments and a variety of graphs and different configurations. The second experiment was about the network of Cabrera city. The smaller WDN had a unique FS pump in the system. The problem was the same as far as the optimisation process was concerned, thus, the minimisation of the energy consumption and in parallel the minimisation of TNps. The same optimisation tool has been used (GANetXL).The main scope was to carry out several and different experiments regarding a vast variety of configurations, using different pump (but this time keeping the FS mode), different tank levels, different pipe diameters and different emitters coefficient. All these different modes came up with a large number of results that were compared in the chapter 8. Concluding, it should be said that the optimisation of WDNs is a very interested field that has a vast space of options to deal with. This includes a large number of algorithms to choose from, different techniques and configurations to be made and different support system generators. The researcher has to be ready to “roam” between these choices, till a satisfactory result will convince him/her that has reached a good optimisation point.

The requirements to protect public water systems from intentional contamination have expanded in the years following September 11, 2001. The areal extent and non-linear nature of water demand and movement in the distribution system makes efficient location of sampling points difficult. This difficulty is compounded by the fact that contamination conceptually can occur at any point and at any time within the distribution system. Small to mid-sized water systems are especially at a disadvantage in addressing this issue due to limited resources available to them. This paper proposes a heuristic methodology to identify strategic locations within the system that can be established as critical detection points for such occurrences. The process uses off-the-shelf software and is structured to be accessible to small and mid-sized water system managers. This methodology is different from others proposed in the literature in that it uses computer simulations to create a database of water system response to contamination at every node in the system. A process is developed to mine this database systematically after considering concentration thresholds and "time since injection" parameters. Finally, using pivot tables and graphs, a network of monitoring locations is identified to provide efficient coverage of the system under the conditions imposed.

Problemas ocasionados por perturbações na qualidade da energia elétrica (QEE) podem provocar sérios prejuízos, tanto de cunho social, quanto financeiros, aos clientes conectados ao sistema elétrico de potência como um todo. Neste contexto, os clientes que mais sofrem são os clientes industriais, pois estes possuem cargas sensíveis a vários distúrbios associados à falta da QEE. Sendo assim, para adoções de medidas preventivas, ou corretivas, que melhorem os índices de QEE, faz-se necessário um monitoramento dos sistemas elétricos que permita um melhor acompanhamento da ocorrência dos distúrbios. Nesta pesquisa é proposta a modelagem do problema de alocação ótima de monitores de QEE em sistemas de distribuição com múltiplos objetivos, os quais são: minimização do custo do monitoramento, minimização da ambiguidade topológica, maximização do monitoramento das cargas, maximização da quantidade de ramais monitorados, minimização da quantidade de afundamentos não monitorados, e maximização da redundância do monitoramento dos afundamentos. Na resolução do problema foi utilizado o Algoritmo Evolutivo Multiobjetivo com Tabelas (AEMT), adotado por ter boa capacidade de resolução com muitos objetivos. Os resultados obtidos permitiram observar que o AEMT forneceu as fronteiras de Pareto com soluções diversificadas e bem distribuídas ao longo da mesma, mostrando-se de grande relevância para o planejamento de sistemas de monitoramento da QEE em sistemas de distribuição de energia. A principal contribuição desta tese é o fornecimento de um modelo que permite às empresas de energia avaliar os investimentos que farão nos seus sistemas de monitoramento considerando seis critérios distintos, permitindo uma maior flexibilidade no estabelecimento do plano de monitoramento e uma melhor análise do custo/benefício considerando os seis aspectos abordados.
Problems arising from disturbances in power quality (PQ) can cause serious damage, both social, and financial, to customers connected to the electrical power distribution systems as a whole. In this context, the customers who suer most are industrial customers, as they have loads sensitive to various disturbances associated with the lack of PQ. Thus, in order to adopt preventive or corrective measures to improve PQ rates, it is necessary to monitor electrical systems to allow better oversight of the occurrence of disturbances. In this research, the proposal is to model the problem of optimal allocation of power quality monitors in distribution systems with multiple objectives. The multiple objectives are: minimizing the monitoring cost, minimizing ambiguities in topology, maximizing the load monitoring, maximizing the area monitoring, minimizing the voltage sag unmonitored, and maximizing the redundancy in the sag monitoring. In solving the problem, a Multiobjective Evolutionary Algorithm with Tables (MEAT) was adopted due to ability to deal with many objectives. The results show that the AMET finds a set of ecient solutions that are diversified and well-distributed along the Pareto Front, and that they are highly relevant for planning of PQ monitoring systems in electrical power distribution systems. The main contribution of this thesis is to provide a model that allows utilities better evaluate investments that they will make in their monitoring systems comprising six dierent criteria, allowing greater flexibility in establishing the monitoring plan and a better analysis of cost/benefit considering the six aspects.

Orientador: Paulo Morelato França
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Depois da regulamentação do setor elétrico brasileiro, a qualidade no fornecimento de energia ganhou maior importância por parte das concessionárias. Neste contexto, o problema de restauração do serviço tem particular relevância pela relação com a freqüência e duração das interrupções no fornecimento: através de alterações na configuração original da rede, busca-se reduzir a carga não atendida sem deixar de observar as restrições de capacidade dos alimentadores, de queda de tensão nas barras de carga e de radialidade da rede. Considerando o caráter temporário destas manobras, torna-se desejável reduzir o grau de intervenção de modo a facilitar a restauração da configuração original. Nesta tese é considerado o problema multiobjetivo de restauração do serviço que compreende a minimização da carga sem fornecimento e do número de chaves manipuladas. Depois da definição matemática do problema, da revisão da literatura especializada e da descrição de um "framework" para problemas relacionados, são descritas duas heurísticas, uma construtiva e outra de melhoramento. A seguir, apresentam-se duas metaheurísticas para o problema, uma Busca Tabu e um Algoritmo Evolutivo, ambas baseadas em otimização de Pareto. Por fim, por meio de estudos práticos com sistemas de distribuição brasileiros, avalia-se experimentalmente a aplicabilidade das abordagens propostas
Abstract: After the Brazilian electric power market regulation, quality of service became a crucial concern of utilities. In fact, the service restoration has a particular importance since it is closely related to frequency and duration of service interruption: through network reconfigurations, one aims to reduce the non supplied load while respecting constraints like feeder and voltage limits as well as the maintenance of a radial structure. Considering that this emergency state is transitory existing only until the fault is eliminated, it is convenient to reduce the number of switching operations in order to make the return back to the original configuration easy. This work considers the multiobjective service restoration to minimize both the load not supplied and the number of switching operations. After defining the mathematical formulation proposed and presenting the bibliographical survey with the description of a new framework to related problems, two new heuristics are presented, one for constructive search and another one for neighborhood search. Next, two metaheuristics especially developed for the referred problem are described, both based on Pareto optimization. Finally, the effectiveness of these proposed methods are proved in a set of five systems, three of them referring to actual Brazilian systems
Doutorado
Automação
Doutor em Engenharia Elétrica

Reconhecendo-se a importância da água como recurso natural limitado e considerando-se a perspectiva de crescimento do contingente populacional urbano, faz-se necessária uma investigação dos sistemas de distribuição de água para abastecimento, por tratarem-se de infra-estruturas básicas comuns aos núcleos populacionais do mundo todo. O planejamento da reabilitação das redes de distribuição de água torna-se de fundamental importância considerando os recursos financeiros limitados e o comportamento operacional desses sistemas que são alterados ao longo do tempo devido ao processo de deterioração de seus componentes. O presente trabalho representa um esforço no sentido de considerar objetivos mais promissores na análise de reabilitação de redes. Dessa maneira, foram considerados: custo, benefício, vazamentos e confiabilidade. Este trabalho apresenta contribuições às análises multiobjetivo via algoritmos genéticos, propriciando um aprimoramento do algoritmo Multiobjective Genetic Algorithm (MOGA) e realizando investigação dos operadores (recombinação e mutação) e dos métodos Non-dominated Sorting Genetic Algorithm (NSGA), Strength Pareto Evolutionary Algorithm (SPEA) e Elitist Non-Dominated Sorting Genetic Algorithm (NSGA II). Do ponto de vista hidráulico, este trabalho introduz tanto perdas por vazamentos como demanda variável com a pressão, proporcionando uma análise mais realística do problema. Os estudos desenvolvidos para redes hipotéticas e para um sistema real, possibilitaram que soluções satisfatórias fossem obtidas, chegando-se inclusive a uma proposição do conceito de programação dinâmica para o caso multiobjetivo.
Recognizing the importance of water as a limited natural resource and considering the prospect of continued population growth, it is important to investigate water distribution systems which are common to all urban infrastructures. Planning of the water distribution network rehabilitation becomes additionally important given economic constraints and operational behavior these systems which modifies in time due to deterioration of water networks. The present work is an effort to consider the multiple objectives in the water network rehabilitation analyses. Four objectives were considered: cost minimization, benefit maximization, leakage minimization and reliability maximization. In addition, it presents some contributions to multiobjective optimization methodology by genetic algorithms, offering an improvement of Multiobjective Genetic Algorithm (MOGA). A detailed investigation is conducted on genetic operators (recombination and mutation) comparing some existing multiobjective optimization methods (Multiobjective Genetic Algorithm - MOGA, Non-dominated Sorting Genetic Algorithm - NSGA, Strength Pareto Evolutionary Algorithm - SPEA and Elitist Non-Dominated Sorting Genetic Algorithm - NSGA II). As regards the hydraulic analysis, this work introduces both leakages and pressure dependent demands in the simulations, providing a more realistic representation of actual field situations. The present study employs hypothetical networks and a real network obtaining satisfactory solutions. Further, dynamic programming concept is also incorporated into the multiobjective optimization framework.

Distributed infrastructures (e.g., water networks and electric Grids) are difficult to manage due to their scale, lack of accessibility, complexity, ageing and uncertainties in knowledge of their structure. In addition they are subject to loads that can be highly variable and unpredictable and to accidental events such as component failure, leakage and malicious tampering. To support in-field operations and central management of these infrastructures, the availability of consistent and up-to-date knowledge about the current state of the network and how it would respond to planned interventions is argued to be highly desirable. However, at present, large-scale infrastructures are “data rich but knowledge poor”. Data, algorithms and tools for network analysis are improving but there is a need to integrate them to support more directly engineering operations. Current ICT solutions are mainly based on specialized, monolithic and heavyweight software packages that restrict the dissemination of dynamic information and its appropriate and timely presentation particularly to field engineers who operate in a resource constrained and less reliable environments. This thesis proposes a solution to these problems by recognizing that current monolithic ICT solutions for infrastructure management seek to meet the requirements of different human roles and operating environments (defined in this work as field and central sides). It proposes an architectural approach to providing dynamic, predictive, user-centric, device and platform independent access to consistent and up-to-date knowledge. This architecture integrates the components required to implement the functionalities of data gathering, data storage, simulation modelling, and information visualization and analysis. These components are tightly coupled in current implementations of software for analysing the behaviour of networks. The architectural approach, by contrast, requires they be kept as separate as possible and interact only when required using common and standard protocols. The thesis particularly concentrates on engineering practices in clean water distribution networks but the methods are applicable to other structural networks, for example, the electricity Grid. A prototype implementation is provided that establishes a dynamic hydraulic simulation model and enables the model to be queried via remote access in a device and platform independent manner.This thesis provides an extensive evaluation comparing the architecture driven approach with current approaches, to substantiate the above claims. This evaluation is conducted by the use of benchmarks that are currently published and accepted in the water engineering community. To facilitate this evaluation, a working prototype of the whole architecture has been developed and is made available under an open source licence.

Multiobjective optimization is becoming an increasingly important approach for both the design and operation of water distribution systems (WDSs). Given the multiobjective nature of these problems, multiobjective optimization is expected to provide decision makers with increased insight into the tradeoffs between competing objectives and alternative solutions of WDSs, which might benefit the water industry, society and environment. Due to the advances in computing technology and the development of fast multiobjective sorting algorithms, research activities into the application of multiobjective algorithms to WDS design and operation have increased significantly in the past decade. Minimization of economic cost and maximization of network reliability are the two most commonly considered objectives in WDS optimization. In addition, some environment related issues, such as energy conservation, have been incorporated into the optimization of WDSs. However, the leading environmental concern – Greenhouse gas (GHG) emissions – has not yet been addressed directly in the field of WDS optimization. Consequently, this research incorporates GHG emission minimization as an objective directly into the optimal design of WDSs, together with the economic objective of minimizing cost and the hydraulic reliability objective of maximizing surplus power factor via a multiobjective approach. The major research contributions are presented in six journal publications. These publications describe the motivation and methodology to incorporate GHG emission minimization as an objective of WDS optimization; explore the tradeoffs between the traditional objective of minimizing life cycle cost and the environmental objective of minimizing life cycle GHG emissions; investigate the sensitivity of these tradeoffs to a number of factors, including the discount rate, electricity tariffs and emission factors used in the objective function evaluation process, the price of carbon under a potential emissions trading scheme and the use of fixed-speed or variable-speed pumps; and finally examine the impact of the inclusion of the hydraulic reliability objective of maximizing surplus power factor on WDS optimization account for economic cost and GHG emissions. In addition, two technical issues have also been solved in order to achieve the overall research aim. First, an optimization based generic pump power estimation method has been developed in this research to efficiently estimate the size and pump power of the pumps required for different network configurations, thus variable-speed pumps can be incorporated into the optimal design of WDSs. Secondly, a new hydraulic reliability measure based on the concept of surplus power factor has been incorporated into the optimal design of WDSs. The advantage of this hydraulic measure over currently used hydraulic reliability measures is that it can be used for WDSs involving the delivery of water into storage facilities, where other measures have failed. The overall contribution of this research is the incorporation of GHG emission consideration into the design optimization of WDSs together with the traditional economic and reliability objectives via a multiobjective approach. It is anticipated that this research will lead to a new paradigm for the optimization of WDSs in the real world.
Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental & Mining Engineering, 2012

Evolutionary Algorithms (EAs) have been shown to apply well to optimizing the design and operations of water distribution systems (WDS). Recent research in the field has focussed on improving existing EAs and developing new ones so as to obtain better solutions (closer to the global optimum) and/or find solutions more efficiently. The primary aim of this research, however, has been to broaden the scope of optimization to include a number of the many factors that planning engineers need to consider when designing or planning the operations of WDS. Those factors considered here are (1) water quality criteria, (2) real-world, complex systems, and (3) the incorporation of data uncertainty. Incorporating each of these factors independently increases computational run-time of EA-based optimization of an algorithm that is already computationally intensive compared to other (inferior) algorithms that have been used in WDS optimization. Water quality models tend to run slower than hydraulic models due to the shorter timestep that is required to ensure sufficient accuracy, and the need for extended period simulations thereby increasing the simulation duration. Real-world models run slower due to their size. Data uncertainty is typically accounted for through the use of Monte Carlo simulations, that add several orders of magnitude to the computational requirements of optimization. Considering each of these factors together compounds the computational requirements to a point where it is impossible to optimize WDS using EAs in a reasonable amount of time. In this research metamodels have been used in place of simulation models within an EA to reduce this computational burden. A metamodel is a model of a model that runs much faster than the said model, but is still a high-fidelity approximation of it. The particular type of metamodel used in this research is an Artificial Neural Network (ANN) due to its theoretical capabilities and demonstrated effectiveness in water resources applications. The use of metamodels to act as surrogates for complex simulation models is not a trivial task. Therefore, guidelines have been developed on how best to incorporate them into the WDS optimization process. The overall metamodel-empowered, EA-based optimization algorithm developed in this research was applied to several case studies. Two small case studies, both variations of the New York Tunnels problem were studied for proof-of-concept purposes. They demonstrated that near globally-optimal solutions could still be found using the metamodel-based approach, i.e. there was minimal compromise in the effectiveness of the EA-based approach. Two larger, real-world problems were also studied: Wallan (operations planning) and Pacific City (system augmentation). These last two case studies were key to demonstrating the power of using metamodels in that they enabled a computational speed-up of up to 1375 times (137,500%) compared to a non-metamodel approach. This speed-up includes factoring in the computational overheads of using metamodels, i.e. time to generate calibration data and calibrate the metamodels.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 2014.

Most of the real world problems in water resources involve nonlinear formulations in their solution construction. Obtaining optimal solutions for large scale nonlinear optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming (NLP) may often face problems in solving them. Recently, there has been an increasing interest in biologically motivated adaptive systems for solving real world optimization problems. The multi-member, stochastic approach followed in Evolutionary Algorithms (EA) makes them less susceptible to getting trapped at local optimal solutions, and they can search easier for global optimal solutions. In this thesis, efficient optimization techniques based on swarm intelligence and evolutionary computation principles have been proposed for single and multi-objective optimization in water resource systems. To overcome the inherent limitations of conventional optimization techniques, meta-heuristic techniques like ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India. First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved. To achieve faster optimization in water resource systems, a novel technique based on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions. Most of the practical problems in water resources are not only nonlinear in their formulations but are also multi-objective in nature. For multi-objective optimization, generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The classical optimization methods often fail to attain a good and true Pareto optimal front due to accretion of the above problems. To overcome such drawbacks of the classical methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles. By incorporating Pareto optimality principles into particle swarm optimization algorithm, a novel approach for multi-objective optimization has been developed. To obtain efficient Pareto-frontiers, along with proper selection scheme and diversity preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is tested for various numerical test problems and engineering design problems. It is found that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker. In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential evolution (DE) scheme, which has a proven ability of effective handling of this kind of interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non- dominated solutions and an effective constraint handling scheme. First, different variations of DE approaches for multi-objective optimization are evaluated through several benchmark test problems for numerical optimization. The developed MODE algorithm showed improved performance over a standard MOEA, namely non-dominated sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir system optimization. It is found that MODE is achieving robust performance in evaluation for the water resources problem, and that the interdependence relationships among the decision variables can be effectively modeled using differential evolution operators. For optimal utilization of scarce water resources, an integrated operational model is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame work is developed and solved using the MODE algorithm to derive simultaneous policies for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system. For efficient management of water resources projects, there is always a great necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle swarm optimization trained neural network (PSONN) and adaptive network fuzzy inference system (ANFIS) models have been developed. The developed models are applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The developed methodologies can certainly help in better planning and operation of the scarce water resources.

Municipal water distribution system (WDS) expansion is often focused on increasing system capacity with designs that best meet hydraulic requirements at the least cost. Increasing public awareness regarding global warming and environmental degradation is making environmental impact an important factor in decision-making for municipalities. There is thus a growing need to consider environmental impacts alongside cost and hydraulic requirements in the expansion and design of WDSs. As a result, the multiplicity of environmental impacts to consider in WDS expansion can complicate the decisions faced by water utilities. For example, a water utility may wish to consider environmental policy issues such as greenhouse gas emissions, non-renewable resource use, and releases to land, water, and air in WDS expansion planning. This thesis outlines a multi-objective optimization approach for WDS design and expansion that balances the objectives of capital cost, annual pumping energy use, and environmental impact minimization, while meeting hydraulic constraints. An environmental impact index that aggregates multiple environmental measures was incorporated as an environmental impact objective function in the multi-objective non-dominated sorting genetic algorithm-II (NSGA-II) optimization algorithm. The environmental impact index was developed to reflect stakeholder prioritization of specific environmental policy issues. The evaluation of the environmental impact index and its application to the WDS expansion problem was demonstrated with a water transmission system example. The environmental impact index and multi-objective non-dominated sorting genetic algorithm-II (NSGA-II) optimization algorithm were applied to the “Anytown” network expansion problem. Preliminary results suggest that solutions obtained with the triple-objective capital cost/energy/EI index optimization minimize a number of environmental impact measures while producing results that are comparable in pumping energy use and, in some instances, slightly higher in capital cost when compared to solutions obtained with a double cost/energy optimization in which environmental impact was not considered.
Thesis (Master, Civil Engineering) -- Queen's University, 2009-08-25 16:08:33.636

In this study, methodologies for modeling grey uncertainty in water resources systems are developed, specifically for the problems in two identified areas in water resources: waste load allocation in streams and floodplain planning. A water resources system is associated with some degree of uncertainty, due to randomness of hydrologic and hydraulic parameters, imprecision and subjectivity in management goals, inappropriateness in model selection, inexactness of different input parameters for inadequacy of data, etc. Uncertainty due to randomness of input parameters could be modeled by the probabilistic models, when probability distributions of the parameters may be estimated. Uncertainties due to imprecision in the management problem may be addressed by the fuzzy decision models. In addition, some parameters in any water resources problems need to be addressed as grey parameters, due to inadequate data for an accurate estimation but with known extreme bounds of the parameter values. Such inexactness or grey uncertainty in the model parameters can be addressed by the inexact or grey optimization models, representing the parameters as interval grey numbers. The research study presented in this thesis deals with the development of grey and fuzzy optimization models, and the combination of the two for water resources systems decision-making. Three grey fuzzy optimization models for waste load allocation, namely (i) Grey Fuzzy Waste Load Allocation Model (GFWLAM), (ii) two-phase GFWLAM and (iii) multiobjective GFWLAM, and a Grey Integer Programming (GIP) model for floodplain planning, are developed in this study. The Grey Fuzzy Waste Load Allocation Model (GFWLAM) for water quality management of river system addresses uncertainty in the membership functions for imprecisely stated management goals of the Pollution Control Agency (PCA) and dischargers. To address the imprecision in fixing the boundaries of membership functions (also known as membership parameters), the membership functions themselves are treated as imprecise in the model and the membership parameters are expressed as interval grey numbers. The conflict between the fuzzy goals of PCA and dischargers is modeled using the concept of fuzzy decision, but because of treating the membership parameters as interval grey numbers, in the present study, the notion of ‘fuzzy decision’ is extended to the notion of ‘grey fuzzy decision’. A terminology ‘grey fuzzy decision’ is used to represent the fuzzy decision resulting from the imprecise membership functions. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for membership functions are interval grey numbers in place of a deterministic real number. In the solution, optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. The methodology is demonstrated with the case studies of a hypothetical river system and the Tunga-Bhadra river system in Karnataka, India. Formulation of GFWLAM is based on the approach for solving fuzzy multiple objective optimization problem using max-min as the operator, which usually may not result in a unique solution. The two-phase GFWLAM captures all the alternative optimal solutions of the GFWLAM. The solution technique in the Phase 1 of two-phase GFWLAM is the same as that of GFWLAM. The Phase 2 maximizes upper bounds and minimizes lower bounds of decision variables, keeping the optimal value of goal fulfillment level same as obtained in the Phase 1. The two-phase GFWLAM gives the unique, widest, intervals of the optimal fractional removal levels of pollutant corresponding to the optimal value of goal fulfillment level. The solution increases the widths of interval-valued fractional removal levels of pollutants by capturing all the alternative optimal solutions and thus enhances the flexibility and applicability in decision-making. The model is applied to the case study of Tunga-Bhadra river system, which shows the existence of multiple solutions when the GFWLAM is applied to the same case study. The width of the interval of optimal fractional removal level plays an important role in the GFWLAM, as more width in the fractional removals implies a wider choice to the decision-makers and more applicability in decision-making. The multiobjective GFWLAM maximizes the width of the interval-valued fractional removal levels for providing a latitude in decision-making and minimizes the width of goal fulfillment level for reducing the system uncertainty. The multiobjective GFWLAM gives a new methodology to get a satisfactory deterministic equivalent of a grey fuzzy optimization problem, using the concept of acceptability index for a meaningful ranking between two partially or fully overlapping intervals. The resulting multiobjective optimization model is solved by fuzzy multiobjective optimization technique. The consistency of the solution is verified by solving the problem with fuzzy goal programming technique. The multiobjective GFWLAM avoids intermediate submodels unlike GFWLAM, so that the solution from a single deterministic equivalent of the GFWLAM adequately covers all possible situations. Although the solutions obtained from multiobjective GFWLAM provide more flexibility than those of the GFWLAM, its application is limited to grey fuzzy goals expressed by linear imprecise membership functions only, whereas GFWLAM has the capability to solve the model with any monotonic nonlinear imprecise membership functions also. The methodology is demonstrated with the case studies of a hypothetical river system and the Tunga-Bhadra river system in Karnataka, India. The Grey Integer Programming (GIP) model for floodplain planning is based on the floodplain planning model developed by Lund (2002), to identify an optimal mix of flood damage reduction options with probabilistic flood descriptions. The model demonstrates how the uncertainty of various input parameters in a floodplain planning problem can be modeled using interval grey numbers in the optimization model. The GIP model for floodplain planning does not replace a post-optimality analysis (e.g., sensitivity analysis, dual theory, parametric programming, etc.), but it provides additional information for interpretation of the optimal solutions. The results obtained from GIP model confirm that the GIP is a useful technique for interpretation of the solutions particularly when a number of potential feasible measures are available in a large scale floodplain planning problem. Though the present study does not directly compare the GIP technique with sensitivity analysis, the results indicate that the rigor and extent of post-optimality analyses may be reduced with the use of GIP for a large scale floodplain planning problem. Application of the GIP model is demonstrated with the hypothetical example as presented in Lund (2002).

abstract: Nowadays there is a pronounced interest in the need for sustainable and reliable infrastructure systems to address the challenges of the future infrastructure development. This dissertation presents the research associated with understanding various sustainable and reliable design alternatives for water distribution systems. Although design of water distribution networks (WDN) is a thoroughly studied area, most researchers seem to focus on developing algorithms to solve the non-linear hard kind of optimization problems associated with WDN design. Cost has been the objective in most of the previous studies with few models considering reliability as a constraint, and even fewer models accounting for the environmental impact of WDN. The research presented in this dissertation combines all these important objectives into a multi-objective optimization framework. The model used in this research is an integration of a genetic algorithm optimization tool with a water network solver, EPANET. The objectives considered for the optimization are Life Cycle Costs (LCC) and Life Cycle Carbon Dioxide (CO2) Emissions (LCE) whereby the system reliability is made a constraint. Three popularly used resilience metrics were investigated in this research for their efficiency in aiding the design of WDNs that are able to handle external natural and man-made shocks. The best performing resilience metric is incorporated into the optimization model as an additional objective. Various scenarios were developed for the design analysis in order to understand the trade-offs between different critical parameters considered in this research. An approach is proposed and illustrated to identify the most sustainable and resilient design alternatives from the solution set obtained by the model employed in this research. The model is demonstrated by using various benchmark networks that were studied previously. The size of the networks ranges from a simple 8-pipe system to a relatively large 2467-pipe one. The results from this research indicate that LCE can be reduced at a reasonable cost when a better design is chosen. Similarly, resilience could also be improved at an additional cost. The model used in this research is more suitable for water distribution networks. However, the methodology could be adapted to other infrastructure systems as well.
Dissertation/Thesis
Ph.D. Construction 2012

In a water distribution system from groundwater supply, the bulk of energy consumption is expended at pump stations. These pumps pressurize the water and transport it from the aquifer to the distribution system and to elevated storage tanks. Each pump in the system has a range of possible operating conditions with varying flow rates, hydraulic head imparted, and hydraulic efficiencies. In this research, the water distribution system of a mid-sized city in a subtropical climate is modeled and optimized in order to minimize the energy usage of its fourteen pumps. A simplified model of the pipes, pumps, and storage tanks is designed using freely-available EPANET hydraulic modeling software. Physical and operational parameters of this model are calibrated against five weeks of observed data using a genetic algorithm to predict storage tank volume given a forecasted system demand. Uncertainty analysis on the calibrated parameters is performed to assess model sensitivity. Finally, the pumping schedule for the system's fourteen pumps is optimized using a genetic algorithm in order to minimize total energy use across a 24-hour period.

Water distribution systems (WDSs) are becoming increasingly complex and larger in scale due to the rapid growth of population and fast urbanization. Hence, they require high levels of investment for their construction and maintenance. This motivates the need to optimally design these systems, with the aim being to minimize the investment budget while maintaining high service quality. Over the past 25 years, a number of evolutionary algorithms (EAs) have been developed to achieve optimal design solutions for WDSs, representing a focal point of much research in this area. One issue that hinders EAs’ wide application in industry is their significant demand on computational resources when handling real-world WDSs. In recognition of this, there has been a move from aiming to find the globally optimal solutions to identifying the best possible solutions within constrained computational resources. While many studies have been undertaken to attain this goal, there have been limited efforts that use engineering knowledge to reduce the computational effort. The research undertaken in this thesis is such an attempt, as it aims to efficiently identify near-optimal solutions with the aid of WDS design knowledge. This thesis presents a domain-knowledge based optimization framework that enables the near-optimal solutions (fronts) of WDS problems to be identified within constrained computing time. The knowledge considered includes (i) the relationship between pipe size and distance to the water source(s); (ii) the impact of flow velocities on optimal solutions; and (iii) the relationship between flow velocities and network resilience. This thesis consists of an Introduction, three chapters that are based around a series of three journal papers and a set of Conclusions and Recommendations for Further Work. The first paper introduces a new initialization method to assist genetic algorithms (GAs) to identify near-optimal solutions in a computationally efficient manner. This is attained by incorporating domain knowledge into the generation of the initial population of GAs. The results show that the proposed method performs better than the other three initialization methods considered, both in terms of computational efficiency and the ability to find near-optimal solutions. The second paper investigates the relative impact of different algorithm initializations and searching mechanisms on the speed with which near-optimal solutions can be identified for large WDS design problems. Results indicate that EA parameterizations, that emphasize exploitation relative to exploration, enable near-optimal solutions to be identified earlier in the search, which is due to the “big bowl” shape of the fitness function for all of the WDS problems considered. Using initial solutions that are informed using domain knowledge can further increase the speed with which near-optimal solutions can be identified. The third publication extends the single-objective method in the first paper to a two-objective problem. The objectives considered are the minimization of cost and maximization of network resilience. The performance of the two-objective initialization approach is compared with that of randomly initializing the population of multi-objective EAs applied to range of WDS design problems. The results indicate that there are considerable benefits in using the proposed initialization method in terms of being able to identify near-optimal fronts more rapidly. Although all of the results obtained in this research have shown that the proposed method is effective for improving the efficiency of EAs in finding near-optimal solutions, only gravity fed water distribution systems with a single loading case were considered as case studies. One important area for future research is the extension of the proposed method to more complex WDSs which may include tanks, pumps and valves.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 2016.

Drinking water distribution systems (WDSs) hold a particularly critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of this infrastructure, its importance for public health, and increased risk of terrorism, several aspects of emergency management for WDSs remain at an undeveloped stage. A set of methods is developed to analyze the risk and consequences of WDS contamination events and develop emergency response support tools. Monte Carlo and optimization schemes are developed to evaluate contamination risk of WDSs for generation of critical contamination scenarios. A multicriteria optimization approach is proposed that treats likelihood and consequences as independent risk measures to find an ensemble of uniformly-distributed critical scenarios. This approach provides insight into system risk and potential mitigation options not available under maximum risk or maximum consequences analyses. Static multiobjective simulation-optimization schemes are developed for generation of optimal response mechanisms for contamination incidents with twoconflicting objectives of minimization of health consequences and impacts on non-consumptive water uses. Performance of contaminant flushing and containment are investigated. Pressure-driven hydraulic analysis is performed to simulate the complicated system hydraulics under pressure-deficit conditions. Performance of a novel preventive response action ? injection of food-grade dye directly into drinking water ? for mitigation of health impacts as a contamination threat unfolds is explored. The emergency response is formulated as a multiobjective optimization problem for the minimization of risks to life with minimum false warning and cost. A multiobjective optimization scheme is used for the management of contamination events for diverse contaminant agents without interruption of firefighting. A dynamic modeling scheme is developed that accounts for the time-varying behavior of the system during an emergency. Effects of actions taken by the managers and consumers as well as the changing perceived contaminant source attributes are included in the simulation model to provide a realistic picture of the dynamic environment. A dynamic optimization scheme is coupled with the simulation model to identify and update the optimal response recommendations during the emergency. Machine learning approaches are employed for real-time characterization of contaminant sources and identification of effective response strategies for a timely and effective response to contamination incidents and threats. In contrast to traditional approaches that perform whole analysis after a contamination event occurs, proposed machine learning methods gain system knowledge in advance and use this extracted information to identify contamination attributes after an incident occurs.

In response to growing environmental concerns, policy makers in Canada have been developing climate change mitigation strategies that will enable Canada to meet medium and long-term greenhouse gas (GHG) emission reduction targets. The water industry is energy- and carbon-intensive, thus the magnitude and long-term uncertainty of proposed carbon mitigation policies could have implications for water distribution system capital planning decisions that are made today. The intent of this thesis was to examine the implications of discount rate and carbon price uncertainty on cost, energy use and GHG emissions in the design/optimization of the Amherstview water distribution system in Loyalist Township, Ontario, Canada. A non-dominated sorting genetic algorithm is coupled with the hydraulic solver EPANET2 in a single-objective optimization approach to identify network expansion designs that minimize total cost as the sum of: i) capital cost of installing new and parallel pipes and of cleaning and lining existing pipes; ii) operation cost of electricity for pumping water; and iii) carbon cost levied on electricity used for pumping water. The Amherstview system was optimized for a range of discount rates and carbon prices reflective of possible climate change mitigation strategies in Canada over the next 50 years. The problem formulation framework was developed according to a “real-world” municipal approach to water distribution system design and expansion. Decision variables such as pipe sizes are restricted to “real-world” commercially-available pipe diameters and parameter values are chosen according to engineering judgment and best-estimates. Parameter uncertainty is characterized by sensitivity analysis rather than the more computationally-demanding and data-intensive Monte Carlo simulation method. The impact of pipe material selection on energy use and GHG emissions was investigated for polyvinyl chloride and cement-mortar lined ductile iron pipes. Results from this first-ever study indicate that the discount rate and carbon prices investigated had no significant influence on energy use and GHG emissions in the Amherstview system. Pipe material selection was also found to minimally affect the amount of GHG emitted in the Amherstview system.
Thesis (Master, Civil Engineering) -- Queen's University, 2010-08-26 15:01:27.174

abstract: The phrase water-energy nexus is commonly used to describe the inherent and critical interdependencies between the electric power system and the water supply systems (WSS). The key interdependencies between the two systems are the power plant’s requirement of water for the cooling cycle and the water system’s need of electricity for pumping for water supply. While previous work has considered the dependency of WSS on the electrical power, this work incorporates into an optimization-simulation framework, consideration of the impact of short and long-term limited availability of water and/or electrical energy. This research focuses on the water supply system (WSS) facet of the multi-faceted optimization and control mechanism developed for an integrated water – energy nexus system under U.S. National Science Foundation (NSF) project 029013-0010 CRISP Type 2 – Resilient cyber-enabled electric energy and water infrastructures modeling and control under extreme mega drought scenarios. A water supply system (WSS) conveys water from sources (such as lakes, rivers, dams etc.) to the treatment plants and then to users via the water distribution systems (WDS) and/or water supply canal systems (WSCS). Optimization-simulation methodologies are developed for the real-time operation of water supply systems (WSS) under critical conditions of limited electrical energy and/or water availability due to emergencies such as extreme drought conditions, electric grid failure, and other severe conditions including natural and manmade disasters. The coupling between WSS and the power system was done through alternatively exchanging data between the power system and WSS simulations via a program control overlay developed in python. A new methodology for WDS infrastructural-operational resilience (IOR) computation was developed as a part of this research to assess the real-time performance of the WDS under emergency conditions. The methodology combines operational resilience and component level infrastructural robustness to provide a comprehensive performance assessment tool. The optimization-simulation and resilience computation methodologies developed were tested for both hypothetical and real example WDS and WSCS, with results depicting improved resilience for operations of the WSS under normal and emergency conditions.
Dissertation/Thesis
Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019