Matching Items (12)
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- All Subjects: engineering
- Genre: Doctoral Dissertation
- Creators: Parrish, Kristen
- Member of: Theses and Dissertations
- Status: Published
Description
Transmission expansion planning (TEP) is a complex decision making process that requires comprehensive analysis to determine the time, location, and number of electric power transmission facilities that are needed in the future power grid. This dissertation investigates the topic of solving TEP problems for large power systems. The dissertation can be divided into two parts. The first part of this dissertation focuses on developing a more accurate network model for TEP study. First, a mixed-integer linear programming (MILP) based TEP model is proposed for solving multi-stage TEP problems. Compared with previous work, the proposed approach reduces the number of variables and constraints needed and improves the computational efficiency significantly. Second, the AC power flow model is applied to TEP models. Relaxations and reformulations are proposed to make the AC model based TEP problem solvable. Third, a convexified AC network model is proposed for TEP studies with reactive power and off-nominal bus voltage magnitudes included in the model. A MILP-based loss model and its relaxations are also investigated. The second part of this dissertation investigates the uncertainty modeling issues in the TEP problem. A two-stage stochastic TEP model is proposed and decomposition algorithms based on the L-shaped method and progressive hedging (PH) are developed to solve the stochastic model. Results indicate that the stochastic TEP model can give a more accurate estimation of the annual operating cost as compared to the deterministic TEP model which focuses only on the peak load.
ContributorsZhang, Hui (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald T (Thesis advisor) / Mittelmann, Hans D (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2013
Description
Effective collection and dissemination of project information, including best practices, help increase the likelihood of project performance and are vital to organizations in the architecture-engineering-construction (AEC) industry. Best practices can help improve project performance, yet these practices are not universally implemented and used in the industry, due to the following: 1) not all practices are applicable to every project or organization, 2) knowledge lost in organizational turnover which leads to inconsistent collection and implementation of best practices and 3) the lack of standardized processes for best practice management in an organization.
This research, sponsored by National Academy of Construction, the Construction Industry Institute and Arizona State University, used structured interviews, a Delphi study and focus groups to explore: 1) potential benefit and industry interest in an open repository of best practices and 2) important elements of a framework/model that guides the creation, management and sustainment of an open repository of best practices.
This dissertation presents findings specifically exploring the term "Practices for Excellence", its definition, elements that hinder implementation, the potential value of an open online repository for such practices and a model to develop an open repository.
This research, sponsored by National Academy of Construction, the Construction Industry Institute and Arizona State University, used structured interviews, a Delphi study and focus groups to explore: 1) potential benefit and industry interest in an open repository of best practices and 2) important elements of a framework/model that guides the creation, management and sustainment of an open repository of best practices.
This dissertation presents findings specifically exploring the term "Practices for Excellence", its definition, elements that hinder implementation, the potential value of an open online repository for such practices and a model to develop an open repository.
ContributorsBosfield, Roberta Patrice (Author) / Gibson, Edd (Thesis advisor) / Chester, Mikhail (Committee member) / Parrish, Kristen (Committee member) / Sullivan, Kenneth (Committee member) / Arizona State University (Publisher)
Created2014
Description
The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small customer sited distributed generation (DG), and better utilize assets. The Smart Grid environment is envisioned to include distributed generation, flexible and controllable loads, bidirectional communications using smart meters and other technologies. Sensory technology may be utilized as a tool that enhances operation including operation of the distribution system. Addressing this point, a distribution system state estimation algorithm is developed in this thesis. The state estimation algorithm developed here utilizes distribution system modeling techniques to calculate a vector of state variables for a given set of measurements. Measurements include active and reactive power flows, voltage and current magnitudes, phasor voltages with magnitude and angle information. The state estimator is envisioned as a tool embedded in distribution substation computers as part of distribution management systems (DMS); the estimator acts as a supervisory layer for a number of applications including automation (DA), energy management, control and switching. The distribution system state estimator is developed in full three-phase detail, and the effect of mutual coupling and single-phase laterals and loads on the solution is calculated. The network model comprises a full three-phase admittance matrix and a subset of equations that relates measurements to system states. Network equations and variables are represented in rectangular form. Thus a linear calculation procedure may be employed. When initialized to the vector of measured quantities and approximated non-metered load values, the calculation procedure is non-iterative. This dissertation presents background information used to develop the state estimation algorithm, considerations for distribution system modeling, and the formulation of the state estimator. Estimator performance for various power system test beds is investigated. Sample applications of the estimator to Smart Grid systems are presented. Applications include monitoring, enabling demand response (DR), voltage unbalance mitigation, and enhancing voltage control. Illustrations of these applications are shown. Also, examples of enhanced reliability and restoration using a sensory based automation infrastructure are shown.
ContributorsHaughton, Daniel Andrew (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2012
Description
Engineering education can provide students with the tools to address complex, multidisciplinary grand challenge problems in sustainable and global contexts. However, engineering education faces several challenges, including low diversity percentages, high attrition rates, and the need to better engage and prepare students for the role of a modern engineer. These challenges can be addressed by integrating sustainability grand challenges into engineering curriculum.
Two main strategies have emerged for integrating sustainability grand challenges. In the stand-alone course method, engineering programs establish one or two distinct courses that address sustainability grand challenges in depth. In the module method, engineering programs integrate sustainability grand challenges throughout existing courses. Neither method has been assessed in the literature.
This thesis aimed to develop sustainability modules, to create methods for evaluating the modules’ effectiveness on student cognitive and affective outcomes, to create methods for evaluating students’ cumulative sustainability knowledge, and to evaluate the stand-alone course method to integrate sustainability grand challenges into engineering curricula via active and experiential learning.
The Sustainable Metrics Module for teaching sustainability concepts and engaging and motivating diverse sets of students revealed that the activity portion of the module had the greatest impact on learning outcome retention.
The Game Design Module addressed methods for assessing student mastery of course content with student-developed games indicated that using board game design improved student performance and increased student satisfaction.
Evaluation of senior design capstone projects via novel comprehensive rubric to assess sustainability learned over students’ curriculum revealed that students’ performance is primarily driven by their instructor’s expectations. The rubric provided a universal tool for assessing students’ sustainability knowledge and could also be applied to sustainability-focused projects.
With this in mind, engineering educators should pursue modules that connect sustainability grand challenges to engineering concepts, because student performance improves and students report higher satisfaction. Instructors should utilize pedagogies that engage diverse students and impact concept retention, such as active and experiential learning. When evaluating the impact of sustainability in the curriculum, innovative assessment methods should be employed to understand student mastery and application of course concepts and the impacts that topics and experiences have on student satisfaction.
Two main strategies have emerged for integrating sustainability grand challenges. In the stand-alone course method, engineering programs establish one or two distinct courses that address sustainability grand challenges in depth. In the module method, engineering programs integrate sustainability grand challenges throughout existing courses. Neither method has been assessed in the literature.
This thesis aimed to develop sustainability modules, to create methods for evaluating the modules’ effectiveness on student cognitive and affective outcomes, to create methods for evaluating students’ cumulative sustainability knowledge, and to evaluate the stand-alone course method to integrate sustainability grand challenges into engineering curricula via active and experiential learning.
The Sustainable Metrics Module for teaching sustainability concepts and engaging and motivating diverse sets of students revealed that the activity portion of the module had the greatest impact on learning outcome retention.
The Game Design Module addressed methods for assessing student mastery of course content with student-developed games indicated that using board game design improved student performance and increased student satisfaction.
Evaluation of senior design capstone projects via novel comprehensive rubric to assess sustainability learned over students’ curriculum revealed that students’ performance is primarily driven by their instructor’s expectations. The rubric provided a universal tool for assessing students’ sustainability knowledge and could also be applied to sustainability-focused projects.
With this in mind, engineering educators should pursue modules that connect sustainability grand challenges to engineering concepts, because student performance improves and students report higher satisfaction. Instructors should utilize pedagogies that engage diverse students and impact concept retention, such as active and experiential learning. When evaluating the impact of sustainability in the curriculum, innovative assessment methods should be employed to understand student mastery and application of course concepts and the impacts that topics and experiences have on student satisfaction.
ContributorsAntaya, Claire Louise (Author) / Landis, Amy E. (Thesis advisor) / Parrish, Kristen (Thesis advisor) / Bilec, Melissa M (Committee member) / Besterfield-Sacre, Mary E (Committee member) / Allenby, Braden R. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The 21st-century professional or knowledge worker spends much of the working day engaging others through electronic communication. The modes of communication available to knowledge workers have rapidly increased due to computerized technology advances: conference and video calls, instant messaging, e-mail, social media, podcasts, audio books, webinars, and much more. Professionals who think for a living express feelings of stress about their ability to respond and fear missing critical tasks or information as they attempt to wade through all the electronic communication that floods their inboxes. Although many electronic communication tools compete for the attention of the contemporary knowledge worker, most professionals use an electronic personal information management (PIM) system, more commonly known as an e-mail application and often the ubiquitous Microsoft Outlook program. The aim of this research was to provide knowledge workers with solutions to manage the influx of electronic communication that arrives daily by studying the workers in their working environment. This dissertation represents a quest to understand the current strategies knowledge workers use to manage their e-mail, and if modification of e-mail management strategies can have an impact on productivity and stress levels for these professionals. Today’s knowledge workers rarely work entirely alone, justifying the importance of also exploring methods to improve electronic communications within teams.
ContributorsCounts, Virginia (Author) / Parrish, Kristen (Thesis advisor) / Allenby, Braden (Thesis advisor) / Landis, Amy (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Today, we use resources faster than they can be replaced. Construction consumes more resources than any other industry and has one of the largest waste streams. Resource consumption and waste generation are expected to grow as the global population increases. The circular economy (CE) is based on the concept of a closed-loop cycle (CLC) and proposes a solution that, in theory, can eliminate the environmental impacts caused by construction and demolition (C&D) waste and increase the efficiency of resources’ use. In a CLC, building materials are reused, remanufactured, recycled, and reintegrated into other buildings (or into other sectors) without creating any waste.
Designing out waste is the core principle of the CE. Design for disassembly or design for deconstruction (DfD) is the practice of planning the future deconstruction of a building and the reuse of its materials. Concepts like DfD, CE, and product-service systems (PSS) can work together to promote CLC in the built environment. PSS are business models based on stewardship instead of ownership. CE combines DfD, PSS, materials’ durability, and materials’ reuse in multiple life cycles to promote a low-carbon, regenerative economy. CE prioritizes reuse over recycling. Dealing with resource scarcity demands us to think beyond the incremental changes from recycling waste; it demands an urgent, systemic, and radical change in the way we design, build, and procure construction materials.
This dissertation aims to answer three research questions: 1) How can researchers estimate the environmental benefits of reusing building components, 2) What variables are susceptible to affect the environmental impact assessment of reuse, and 3) What are the barriers and opportunities for DfD and materials’ reuse in the current design practice in the United States.
The first part of this study investigated how different life cycle assessment (LCA) methods (i.e., hybrid LCA and process-based LCA), assumptions (e.g., reuse rates, transportation distances, number of reuses), and LCA timelines can affect the results of a closed-loop LCA. The second part of this study built on interviews with architects in the United States to understand why DfD is not part of the current design practice in the country.
Designing out waste is the core principle of the CE. Design for disassembly or design for deconstruction (DfD) is the practice of planning the future deconstruction of a building and the reuse of its materials. Concepts like DfD, CE, and product-service systems (PSS) can work together to promote CLC in the built environment. PSS are business models based on stewardship instead of ownership. CE combines DfD, PSS, materials’ durability, and materials’ reuse in multiple life cycles to promote a low-carbon, regenerative economy. CE prioritizes reuse over recycling. Dealing with resource scarcity demands us to think beyond the incremental changes from recycling waste; it demands an urgent, systemic, and radical change in the way we design, build, and procure construction materials.
This dissertation aims to answer three research questions: 1) How can researchers estimate the environmental benefits of reusing building components, 2) What variables are susceptible to affect the environmental impact assessment of reuse, and 3) What are the barriers and opportunities for DfD and materials’ reuse in the current design practice in the United States.
The first part of this study investigated how different life cycle assessment (LCA) methods (i.e., hybrid LCA and process-based LCA), assumptions (e.g., reuse rates, transportation distances, number of reuses), and LCA timelines can affect the results of a closed-loop LCA. The second part of this study built on interviews with architects in the United States to understand why DfD is not part of the current design practice in the country.
ContributorsCruz Rios, Fernanda (Author) / Grau, David (Committee member) / Chong, Oswald (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2018
Description
With growing concern regarding environmental issues and the need for a more sustainable grid, power systems have seen a fast expansion of renewable resources in the last decade. The uncertainty and variability of renewable resources has posed new challenges on system operators. Due to its energy-shifting and fast-ramping capabilities, energy storage (ES) has been considered as an attractive solution to alleviate the increased renewable uncertainty and variability.
In this dissertation, stochastic optimization is utilized to evaluate the benefit of bulk energy storage to facilitate the integration of high levels of renewable resources in transmission systems. A cost-benefit analysis is performed to study the cost-effectiveness of energy storage. A two-step approach is developed to analyze the effectiveness of using energy storage to provide ancillary services. Results show that as renewable penetrations increase, energy storage can effectively compensate for the variability and uncertainty in renewable energy and has increasing benefits to the system.
With increased renewable penetrations, enhanced dispatch models are needed to efficiently operate energy storage. As existing approaches do not fully utilize the flexibility of energy storage, two approaches are developed in this dissertation to improve the operational strategy of energy storage. The first approach is developed using stochastic programming techniques. A stochastic unit commitment (UC) is solved to obtain schedules for energy storage with different renewable scenarios. Operating policies are then constructed using the solutions from the stochastic UC to efficiently operate energy storage across multiple time periods. The second approach is a policy function approach. By incorporating an offline analysis stage prior to the actual operating stage, the patterns between the system operating conditions and the optimal actions for energy storage are identified using a data mining model. The obtained data mining model is then used in real-time to provide enhancement to a deterministic economic dispatch model and improve the utilization of energy storage. Results show that the policy function approach outperforms a traditional approach where a schedule determined and fixed at a prior look-ahead stage is used. The policy function approach is also shown to have minimal added computational difficulty to the real-time market.
In this dissertation, stochastic optimization is utilized to evaluate the benefit of bulk energy storage to facilitate the integration of high levels of renewable resources in transmission systems. A cost-benefit analysis is performed to study the cost-effectiveness of energy storage. A two-step approach is developed to analyze the effectiveness of using energy storage to provide ancillary services. Results show that as renewable penetrations increase, energy storage can effectively compensate for the variability and uncertainty in renewable energy and has increasing benefits to the system.
With increased renewable penetrations, enhanced dispatch models are needed to efficiently operate energy storage. As existing approaches do not fully utilize the flexibility of energy storage, two approaches are developed in this dissertation to improve the operational strategy of energy storage. The first approach is developed using stochastic programming techniques. A stochastic unit commitment (UC) is solved to obtain schedules for energy storage with different renewable scenarios. Operating policies are then constructed using the solutions from the stochastic UC to efficiently operate energy storage across multiple time periods. The second approach is a policy function approach. By incorporating an offline analysis stage prior to the actual operating stage, the patterns between the system operating conditions and the optimal actions for energy storage are identified using a data mining model. The obtained data mining model is then used in real-time to provide enhancement to a deterministic economic dispatch model and improve the utilization of energy storage. Results show that the policy function approach outperforms a traditional approach where a schedule determined and fixed at a prior look-ahead stage is used. The policy function approach is also shown to have minimal added computational difficulty to the real-time market.
ContributorsLi, Nan (Author) / Hedman, Kory W (Thesis advisor) / Tylavksy, Daniel J (Committee member) / Heydt, Gerald T (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2016
Description
Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
ContributorsNaganathan, Hariharan (Author) / Chong, Oswald W (Thesis advisor) / Ariaratnam, Samuel T (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2017
Description
Project teams expend substantial effort to develop scope definition during the front end planning phase of large, complex projects, but oftentimes neglect to sufficiently plan for small projects. An industry survey administered by the author showed that small projects make up approximately half of all projects in the infrastructure construction sector (by count), the planning of these projects varies greatly, and that a consistent definition of “small infrastructure project” did not exist. This dissertation summarizes the motivations and efforts of Construction Industry Institute (CII) Research Team 314a to develop a non-proprietary front end planning tool specifically for small infrastructure projects, namely the Project Definition Rating Index (PDRI) for Small Infrastructure Projects. The author was a member of CII Research Team 314a, who was tasked with developing the tool in September 2015. The author, together with the research team, scrutinized and adapted an existing infrastructure-focused FEP tool, the PDRI for Infrastructure Projects, and other resources to develop a set of 40 specific elements relevant to the planning of small infrastructure projects. The author along with the research team supported the facilitation of seven separate industry workshops where 71 industry professionals evaluated the element descriptions and provided element prioritization data that was statistically analyzed and used to develop a corresponding weighted score sheet. The tool was tested on 76 completed and in-progress projects, the analysis of which showed that small infrastructure projects with greater scope definition (based on the tool’s scoring scheme) outperformed projects with lesser scope definition regarding cost performance, schedule performance, change performance, financial performance, and customer satisfaction. Moreover, the author found that users of the tool on in-progress projects agreed that the tool added value to their projects in a timeframe and manner consistent with their needs, and that they would continue using the tool in the future. The author also conducted qualitative and quantitative similarities and differences between PDRI – Infrastructure and PDRI – Small Infrastructure Projects in support of improved planning efforts for both types of projects. Finally, the author piloted a case study that introduced the PDRI into an introductory construction management course to enhance students’ learning experience.
ContributorsElZomor, Mohamed A (Author) / Parrish, Kristen (Thesis advisor) / Gibson, Jr., G. Edward (Committee member) / El Asmar, Mounir (Committee member) / Arizona State University (Publisher)
Created2017
Description
An Earned Value Management System (EVMS) is an organization’s system for project/program management that integrates a defined set of associated work scopes, schedules and budgets, allowing for effective planning, performance, and management control. A mature EVMS that is compliant with standards and guidelines, and that is applied in a positive social environment is critical to the overall success of large and complex projects and programs. However, a comprehensive and up-to-date literature review revealed a lack of a data-driven and consistent rating system that can gauge the maturity and the environment surrounding EVMS implementation. Therefore, the primary objective of this dissertation focuses on the EVMS maturity and environment, and investigates their impact on project performance. The author was one of the 41 research team members whose goal was to develop the novel rating system called Integrated Project/Program Management (IP2M) Maturity and Environment Total Risk Rating (METRR). Using a multi-method research approach, the rating system was developed based on a literature review of more than 600 references, a survey with 294 responses, focus group meetings, and research charrettes with more than 100 subject matter experts from the industry. Performance data from 35 completed projects and programs representing over $21.8 billion in total cost was collected and analyzed. The data analysis showed that the projects with high EVMS maturity and good EVMS environment outperformed those with low maturity and poor environment in key project performance measures. The contributions of this work includes: (1) developing definitions for EVM, EVMS and other research related terms, (2) determining the gaps in the EVMS literature, (3) determining the EVMS state of the practice in the industry, (4) developing a scalable rating system to measure the EVMS maturity and environment, (5) providing quantified evidence on the impact of EVMS maturity and environment on project performance, and (6) providing guidance to practitioners to gauge their EVMS maturity and environment for an enhanced project and program management integration and performance.
ContributorsAramali, Vartenie Mardiros (Author) / Gibson Jr., George Edward (Thesis advisor) / El Asmar, Mounir (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2022