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 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

Electric power distribution systems are gradually adopting new advancements in communication, control, measurement, and metering technologies to help realize the evolving concept of Smart Grids. Future distribution systems will facilitate increased and active participation of customers in Demand Side Management activities, with customer load profiles being primarily governed by real-time information such as energy price, emission, and incentive signals from utilities. In such an environment, new mathematical modeling approaches would allow Local Distribution Companies (LDCs) and customers the optimal operation of distribution systems and customer's loads, considering various relevant objectives and constraints. This thesis presents a mathematical model for optimal and real-time operation of distribution systems. Thus, a three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which incorporates comprehensive and realistic models of relevant distribution system components. A novel optimization objective, which minimizes the energy purchased from the external grid while limiting the number of switching operations of control equipment, is considered. A heuristic method is proposed to solve the DOPF model, which is based on a quadratic penalty approach to reduce the computational burden so as to make the solution process suitable for real-time applications. A Genetic Algorithm based solution method is also implemented to compare and benchmark the performance of the proposed heuristic solution method. The results of applying the DOPF model and the solution methods to two distribution systems, i.e., the IEEE 13-node test feeder and a Hydro One distribution feeder, are discussed. The results demonstrate that the proposed three-phase DOPF model and the heuristic solution method may yield some benefits to the LDCs in real-time optimal operation of distribution systems in the context of Smart Grids. This work also presents a mathematical model for optimal and real-time control of customer electricity usage, which can be readily integrated by industrial customers into their Energy Hub Management Systems (EHMSs). An Optimal Industrial Load Management (OILM) model is proposed, which minimizes energy costs and/or demand charges, considering comprehensive models of industrial processes, process interdependencies, storage units, process operating constraints, production requirements, and other relevant constraints. The OILM is integrated with the DOPF model to incorporate operating constraints required by the LDC system operator, thus combining voltage optimization with load control for additional benefits. The OILM model is applied to two industrial customers, i.e., a flour mill and a water pumping facility, and the results demonstrate the benefits to the industrial customers and LDCs that can be obtained by deploying the proposed OILM and three-phase DOPF models in EHMSs, in conjunction with Smart Grid technologies.

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.

The purpose of this work was to find a suitable calculation method for predicting the function of distribution systems in the design calculations of process and energy equipment. In particular, it aimed at describing the distribution of the working fluid flow in a dividing distribution system and a combined Z-type distribution system (with nozzles located parallel to opposite sides of the system). Analytical and CFD calculation tools validated by data from the performed physical experiments were used in this work. In the CFD method, the prediction of the dividing flow was performed for full 3D and simplified 2D geometry of Z-type distribution systems. The carried-out analyzes show that the prediction of the distribution system function obtained by both analytical and numerical approaches is accurate enough. The relative difference between the experimental and computational relative standard deviations did not exceed 9 %. The main disadvantage of 3D CFD analysis, especially concerning the purpose of the intended application, i.e., the inclusion of a distribution model in a complex modeling system for the initial design of heat transfer equipment, was the extremely long computational time. Analytical models appear to be a reasonable compromise between the accuracy of the flow distribution prediction and the computational times.

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The first part of the dissertation investigates cost experience in the US life and health insurance industry over the period 1998-2012. We generally test the difference in expenses among different distribution systems, which mainly consists of independent agency, broker, career agency, exclusive agency, and direct writing. We check to see if cost, revenue and profit efficiency differences are associated with different distribution methods. Cost, revenue, and profit efficiencies are estimated by Data Envelopment Analysis. Unlike the results in the property and liability insurance industry, the cost difference is insignificant among distribution systems. Results on cost efficiency and revenue efficiency support the market imperfection hypothesis, which says that the market imperfections such as entry barriers, price regulation, or search costs cause the coexistence of different distribution systems. The second part of the dissertation examines the relationship between mergers and acquisitions (M&As), and underwriting cycles in the P-L insurance industry. In a soft market, capital is relatively high. This leads to an increase in the number of M&A transactions and the probability that managers conduct non-value-increasing M&As. We test this proposition by analyzing the associations between volumes of M&A deals, and returns associated with M&As and underwriting cycle. The results show that the numbers of M&As are negatively related with the premium rate changes and positively related with changes in the combined ratio. We also find that the cumulative abnormal returns around the announcement date of M&As are smaller for the shareholders of insurer acquirers in a soft market. Even more, we find that the market reaction of M&As is less sensitive to agency problems in a hard market than in a soft market. / Business Administration/Risk Management and Insurance