This dissertation investigates the distribution and transmission systems reliability and economic impact of energy storage and renewable energy integration. The reliability and economy evaluation framework is presented. Novel operation strategies of energy storage and renewable energy are proposed. The method for optimizing the energy storage sizing and operation strategy in order to achieve optimal reliability and economy level is developed. The objectives of the movement towards the smart grid include making the power systems more reliable and economically efficient. The rapid development of the large scale energy storage technology makes it an excellent candidate in achieving these goals. A novel Model Predictive Control (MPC)-based operation strategy is proposed to optimally manage the charging and discharging operation of energy storage in order to minimize the energy purchasing cost for a distribution system load aggregator in power markets. Different operation strategies of energy storage have different reliability and economic impact on power systems. Simulation results illustrate the importance of the energy storage operation strategies. A hybrid operation strategy which combines the MPC-based operation strategy and the standby backup operation strategy is proposed to flexibly adjust the reliability and economic improvement brought by energy storage. A particle swarm optimization approach is developed to determine the optimal energy storage sizing and operation strategy while maximizing reliability and economic improvement. A reliability and economy assessment framework based on sequential Monte Carlo method integrated with the operation strategies is proposed. The impact on the transmission systems reliability brought by energy storage and renewable energy with the proposed operation strategies is investigated. Case studies are conducted to demonstrate the effectiveness of the proposed operation strategies, optimization approach, and the reliability and economy evaluation framework. Insights into how energy storage and renewable energy affect power system reliability and economy are obtained.

The primary objective of this thesis is to develop a model for quantification of weather-related railway embankments hazards. The model for quantification of embankment hazards constitutes an essential component of a decision support system that is required for the management of railway embankment hazards. A model for the deterministic and probabilistic assessment of weather-related geo-hazards (W-GHA model) is proposed based on concepts of unsaturated soil mechanics and hydrology. The model combines a system of two-dimensional partial differential equations governing the thermo-hydro-mechanical behaviour of saturated/unsaturated soils and soil-atmosphere coupling equations. A Dynamic Programming algorithm for slope stability analysis (Safe-DP) was developed and incorporated into the W-GHA model. Finally, an efficient probabilistic and sensitivity analysis framework based on an alternative point estimate method was proposed. According to the W-GHA model framework, railway embankment hazards are assessed based on factors of safety and probabilities of failures computed using soil property variability and case scenarios. <p> A comprehensive study of unsaturated property variability is presented. A methodology for the characterization and assessment of unsaturated soil property variability is proposed. Appropriate fitting equations and parameter were selected. Probability density functions adequate for representing the unsaturated soil parameters studied were determined. Typical central tendency measures, variability measures, and correlation coefficients were established for the unsaturated soil parameters. The inherent variability of the unsaturated soil properties can be addressed using the probabilistic analysis framework proposed herein. <p> A large number of hypothetical railway embankments were analysed using the proposed model. The embankment analyses were undertaken in order to demonstrate the application of the proposed model and in order to determine the sensitivity of the factor of safety to the uncertainty in several input variables. The conclusions drawn from the sensitivity analysis study resulted in important simplifications of the W-GHA model. It was shown how unsaturated soil mechanics can be applied for the assessment of near ground surface stability hazards. The approach proposed in this thesis forms a protocol for application of unsaturated soil mechanics into geotechnical engineering practice. This protocol is based on predicted unsaturated soil properties and based on the use of case scenarios for addressing soil property uncertainty. Other classes of unsaturated soil problems will benefit from the protocol presented in this thesis.

Modern day bridge foundations are often designed for the maximum loading condition on a single member and each following foundation member often follows the same design. A 4-pile and a 2-pile foundation system were analyzed with an upperbound plasticity model in an attempt to approximately represent the system capacity. A sensitivity analysis was performed to measure the change in the ultimate system capacity by altering the parameters used to define individual pile capacities. The load and the resistance are considered to be normally distributed and are each characterized by a mean and a standard deviation. A first order reliability method was used to measure and account for any system redundancy in the foundation system by predicting a probability of failure based on the load and resistance of the system. Based on the most heavily loaded pile and the analysis performed on the 2-pile and 4-pile foundation systems, the probabilities of failure are lower for the system than for the single pile. This would bring reason to state there is some measurable redundancy in the basic systems analyzed where these systems would once be considered to be non-redundant systems. / text

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / The U.S. Air Force's next generation reusable booster (NGRSB) offers the opportunity for the Space Command to use intelligent equipment for decision making replacing personnel, increasing safety and mission assurance by removing decisions from program management personnel who may not have had any flight-test experience. Adding intelligence to launch vehicle and spacecraft equipment may include requiring the builder to use a prognostic and health management (PHM) program. The PHM was added to NASA's aircraft programs in 2009 and we have requested NASA HQ and NASA Marshal Space Flight Center adopt the NASA PHM in the procurement contracts used on the new Space Launch Systems, NASA's congressionally mandated replacement for the Space Shuttle. Space Vehicle Program managers often make decisions for on-orbit spacecraft without ever having on-orbit space flight experience. Intelligent equipment would have eliminated the catastrophic failures on the NASA Space Shuttle Challenger and Columbia. These accidents occurred due to the lack of space vehicle subsystem engineering personnel analyzing real-time equipment telemetry presented on strip chart and video data prior to lift off during pre-launch checkout for the Space Shuttle Challenger and the lack of space vehicle real-time equipment telemetry for Columbia. The PHM requires all equipment to include analog telemetry for measuring the equipment performance and usable life determination in real-time and a prognostic analysis completed manually will identify the equipment that will fail prematurely for replacement before launch preventing catastrophic equipment failures that may cause loss of life.

Safeguards and security (S&S) systems for nuclear facilities include material control and accounting (MC&A) and a physical protection system (PPS) to protect nuclear materials from theft, sabotage and other malevolent human acts. The PPS for a facility is evaluated using probabilistic analysis of adversary paths on the basis of detection, delay, and response timelines to determine timely detection. The path analysis methodology focuses on systematic, quantitative evaluation of the physical protection component for potential external threats, and often calculates the probability that the PPS is effective (PE) in defeating an adversary who uses that attack path. By monitoring and tracking critical materials, MC&A activities provide additional protection against inside adversaries, but have been difficult to characterize in ways that are compatible with the existing path analysis methods that are used to systematically evaluate the effectiveness of a site’s protection system. This research describes and demonstrates a new method to incorporate MC&A protection elements explicitly within the existing probabilistic path analysis methodology. MC&A activities, from monitoring to inventory measurements, provide many, often recurring opportunities to determine the status of critical items, including detection of missing materials. Human reliability analysis methods are applied to determine human error probabilities to characterize the detection capabilities of MC&A activities. An object-based state machine paradigm was developed to characterize the path elements and timing of an insider theft scenario as a race against MC&A activities that can move a facility from a normal state to a heightened alert state having additional detection opportunities. This paradigm is coupled with nuclear power plant probabilistic risk assessment techniques to incorporate the evaluation of MC&A activities in the existing path analysis methodology. Event sequence diagrams describe insider paths through the PPS and also incorporate MC&A activities as path elements. This work establishes a probabilistic basis for incorporating MC&A activities explicitly within the existing path analysis methodology to extend it to address insider threats. The analysis results for this new method provide an integrated effectiveness measure for a safeguards and security system that addresses threats from both outside and inside adversaries. / text

Water supply systems aim, among other objectives, to protect public health by reducing the concentration of, and potentially eliminating, microorganisms pathogenic to human beings. Yet, because water supply systems are engineered systems facing variable conditions, such as raw water quality or treatment process performance, the quality of the drinking water produced also exhibits variability. The reliability of a treatment system is defined in this context as the probability of producing drinking water that complies with existing microbial quality standards. This thesis examines the concept of reliability for two physicochemical treatment technologies, conventional rapid granular filtration and ultrafiltration, used to remove the protozoan pathogen Cryptosporidium parvum from drinking water. First, fault tree analysis is used as a method of identifying technical hazards related to the operation of these two technologies and to propose ways of minimizing the probability of failure of the systems. This method is used to compile operators’ knowledge into a single logical diagram and allows the identification of important processes which require efficient monitoring and maintenance practices. Second, an existing quantitative microbial risk assessment model is extended to be used in a reliability analysis. The extended model is used to quantify the reliability of the ultrafiltration system, for which performance is based on full-scale operational data, and to compare it with the reliability of rapid granular filtration systems, for which performance is based on previously published data. This method allows for a sound comparison of the reliability of the two technologies. Several issues remain to be addressed regarding the approaches used to quantify the different input variables of the model. The approaches proposed herein can be applied to other water treatment technologies, to aid in prioritizing interventions to improve system reliability at the operational level, and to determine the data needs for further refinements of the estimates of important variables.

Uncertainty analysis of a system response is an important part of engineering probabilistic analysis. Uncertainty analysis includes: (a) to evaluate moments of the response; (b) to evaluate reliability analysis of the system; (c) to assess the complete probability distribution of the response; (d) to conduct the parametric sensitivity analysis of the output. The actual model of system response is usually a high-dimensional function of input variables. Although Monte Carlo simulation is a quite general approach for this purpose, it may require an inordinate amount of resources to achieve an acceptable level of accuracy. Development of a computationally efficient method, hence, is of great importance. First of all, the study proposed a moment method for uncertainty quantification of structural systems. However, a key departure is the use of fractional moment of response function, as opposed to integer moment used so far in literature. The advantage of using fractional moment over integer moment was illustrated from the relation of one fractional moment with a couple of integer moments. With a small number of samples to compute the fractional moments, a system output distribution was estimated with the principle of maximum entropy (MaxEnt) in conjunction with the constraints specified in terms of fractional moments. Compared to the classical MaxEnt, a novel feature of the proposed method is that fractional exponent of the MaxEnt distribution is determined through the entropy maximization process, instead of assigned by an analyst in prior. To further minimize the computational cost of the simulation-based entropy method, a multiplicative dimensional reduction method (M-DRM) was proposed to compute the fractional (integer) moments of a generic function with multiple input variables. The M-DRM can accurately approximate a high-dimensional function as the product of a series low-dimensional functions. Together with the principle of maximum entropy, a novel computational approach was proposed to assess the complete probability distribution of a system output. Accuracy and efficiency of the proposed method for structural reliability analysis were verified by crude Monte Carlo simulation of several examples. Application of M-DRM was further extended to the variance-based global sensitivity analysis of a system. Compared to the local sensitivity analysis, the variance-based sensitivity index can provide significance information about an input random variable. Since each component variance is defined as a conditional expectation with respect to the system model function, the separable nature of the M-DRM approximation can simplify the high-dimension integrations in sensitivity analysis. Several examples were presented to illustrate the numerical accuracy and efficiency of the proposed method in comparison to the Monte Carlo simulation method. The last contribution of the proposed study is the development of a computationally efficient method for polynomial chaos expansion (PCE) of a system's response. This PCE model can be later used uncertainty analysis. However, evaluation of coefficients of a PCE meta-model is computational demanding task due to the involved high-dimensional integrations. With the proposed M-DRM, the involved computational cost can be remarkably reduced compared to the classical methods in literature (simulation method or tensor Gauss quadrature method). Accuracy and efficiency of the proposed method for polynomial chaos expansion were verified by considering several practical examples.

The purpose of this thesis is to demonstrate that two-girder or two-web structural systems can be employed to design efficient bridges with an adequate level of redundancy. The issue of redundancy in two-girder bridges is a constraint for the bridge designers in North America who want to take advantage of efficiency in this type of structural system. Therefore, behavior of two-girder or two-web structural systems after failure of one main load-carrying component is evaluated to validate their safety. A procedure is developed to perform system-level reliability analysis of bridges. This procedure is applied to two bridge concepts, a twin steel girder with composite deck slab and a concrete double-T girder with unbonded external tendons. The results show that twin steel girder bridges can be designed to fulfill the requirements of a redundant structure and the double-T girder with external unbonded tendons can be employed to develop a robust structural system.

The purpose of this thesis is to demonstrate that two-girder or two-web structural systems can be employed to design efficient bridges with an adequate level of redundancy. The issue of redundancy in two-girder bridges is a constraint for the bridge designers in North America who want to take advantage of efficiency in this type of structural system. Therefore, behavior of two-girder or two-web structural systems after failure of one main load-carrying component is evaluated to validate their safety. A procedure is developed to perform system-level reliability analysis of bridges. This procedure is applied to two bridge concepts, a twin steel girder with composite deck slab and a concrete double-T girder with unbonded external tendons. The results show that twin steel girder bridges can be designed to fulfill the requirements of a redundant structure and the double-T girder with external unbonded tendons can be employed to develop a robust structural system.

Water supply systems aim, among other objectives, to protect public health by reducing the concentration of, and potentially eliminating, microorganisms pathogenic to human beings. Yet, because water supply systems are engineered systems facing variable conditions, such as raw water quality or treatment process performance, the quality of the drinking water produced also exhibits variability. The reliability of a treatment system is defined in this context as the probability of producing drinking water that complies with existing microbial quality standards. This thesis examines the concept of reliability for two physicochemical treatment technologies, conventional rapid granular filtration and ultrafiltration, used to remove the protozoan pathogen Cryptosporidium parvum from drinking water. First, fault tree analysis is used as a method of identifying technical hazards related to the operation of these two technologies and to propose ways of minimizing the probability of failure of the systems. This method is used to compile operators’ knowledge into a single logical diagram and allows the identification of important processes which require efficient monitoring and maintenance practices. Second, an existing quantitative microbial risk assessment model is extended to be used in a reliability analysis. The extended model is used to quantify the reliability of the ultrafiltration system, for which performance is based on full-scale operational data, and to compare it with the reliability of rapid granular filtration systems, for which performance is based on previously published data. This method allows for a sound comparison of the reliability of the two technologies. Several issues remain to be addressed regarding the approaches used to quantify the different input variables of the model. The approaches proposed herein can be applied to other water treatment technologies, to aid in prioritizing interventions to improve system reliability at the operational level, and to determine the data needs for further refinements of the estimates of important variables.