Thesis (Ph. D.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains ix, 105 p. : ill. Includes abstract. Includes bibliographical references (p. 103-105).

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. Page 274 blank. / Includes bibliographical references (pages 265-273). / This dissertation demonstrates a novel integrated electric power system planning framework that incorporates distributed energy resources (DERs), flexible and price-responsive demand, and distribution network losses and reinforcement costs. New methods are developed and demonstrated to derive the aggregate impact of demand and DERs on distribution network losses and upgrade costs from detailed distribution network simulations. The results from these simulations are used to parameterize a novel and tractable representation of distribution networks at an aggregate scale in a new formulation of the electricity resource capacity planning problem. A set of case studies demonstrate the utility of this modeling framework for modeling competition amongst distributed and centralized resources, exploring tradeoffs between economies of unit scale and locational value of various resources, assessing the value of price-responsive electricity demand, and considering the impact of policy or regulation that drives the adoption of DERs. Methodologically, this dissertation makes a set of contributions, including: 1. A new approach to using AC power flow simulations to accurately derive the effect of aggregate changes in power withdrawals and injections on resistive network losses in large-scale distribution networks. 2. A method for adapting AC optimal power flow simulations to identify the minimum quantity of net reductions in coincident peak demand (achieved either by demand flexibility or distributed generation or storage) necessary to accommodate demand growth in large-scale distribution networks without investment in new network assets (e.g., 'non-wires' alternatives). 3. A method for using a distribution network planning model to determine the cost of traditional network upgrades required to accommodate an equivalent increase in peak demand. 4. An integrated electricity resource capacity planning model that employs results from the above methods to incorporate DERs and flexible demand and consider key sources of locational value or cost, including impacts on transmission and distribution network costs and losses. Electricity system planning models provide decision support for electric utilities, insight for policy-makers, and a detailed techno-economic framework to evaluate emerging technologies. Collectively, the new methods demonstrated herein expand the capabilities of these important planning tools to keep pace with a rapidly evolving electric power sector. / by Jesse D. Jenkins. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Science, Technology, and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Environmental assessments, such as those for biotechnology applications, are typically conducted by small groups of expert assessors, but scholars and practitioners are increasingly interested in involving diverse stakeholders. In addition to other reasons for broader involvement, researchers have proposed that stakeholders could substantively aid assessment by (1) contributing system knowledge; (2) applying diverse conceptual models; (3) helping available knowledge keep pace with assessment needs; and (4) contributing based on their values, as do narrow expert assessors. Hypothesizing that these types of contribution, suggested theoretically or observed in single workshops, represent key sources of stakeholder contribution across processes, this study examines contribution in several diverse participant processes: an Environmental Protection Agency (EPA) workshop on testing schemes for some engineered microbes compared with another EPA office's testing requirements for other engineered microbes; an MIT-Wilson Center workshop series on synthetic biology environmental assessment research needs; and the Food and Drug Administration's engineered salmon environmental assessment along with diverse stakeholder comments and critiques. The study also identifies practical considerations for enabling multi-stakeholder contribution and applies lessons to broader societal processes. The study analyzes process documents, conversations with conveners and participants, and participant observation. It also reviews knowledge about biological processes representing important areas for assessment and research, discussing complexities of knowledge production and use for assessment. Stakeholders contributed in each of the four hypothesized ways across the cases, suggesting that diverse involvement could regularly contribute positively to assessment. Stakeholders also (5) challenged standard assessment approaches, challenges that could aid assessment as well. Practical considerations for enabling diverse participant contribution emerge from the cases: Process continuity over time; credible expectations of authority or influence in decision-making; and balance between predefined structure and flexibility and between technical tasks and enabling non-technical input may be key. Work developing approaches in these areas is needed, including on incorporating nontechnical inputs, on processes encompassing later assessment stages, on integrating diverse participant processes with governance, and on diverse involvement in other aspects of technology development and execution. Better and increased stakeholder involvement could, through substantive content and incorporation of values, enable science, technology development, and decision-making best to serve society. / by Shlomiya Lightfoot. / Ph. D. in Science, Technology, and Policy

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 327-360). / China's rapid economic growth -- largely industrial, energy-intensive, and reliant on coal -- has generated environmental, public health, and governance challenges. While China now leads the world in renewable energy deployment, curtailment (waste) of wind and solar is high and increasing, generating much discussion on the relative contributions of technical inflexibilities and incomplete institutional reforms on integration outcomes. These integration challenges directly affect China's ability to meet long-term environmental and economic objectives. A second, related challenge emerges from how wind integration interacts with China's reinvigoration in 2015 of a three-decade-old process to establish competitive electricity markets. A "standard liberalization prescription" for electricity markets exists internationally, though Chinese policy-makers ignore or under-emphasize many of its elements in current reforms, and some scholars question its general viability in emerging economies. This dissertation examines these interrelated phenomena by analyzing the contributions of diverse causes of wind curtailment, assessing whether current experiments will lead to efficient and politically viable electricity markets, and offering prescriptions on when and how to use markets to address renewable energy integration challenges. To examine fundamentals of the technical system and the impacts of institutional incentives on system outcomes, this dissertation develops a multi-method approach that iterates between engineering models and qualitative case studies of the system operation decision-making process (Chapter 2). These are necessary to capture, respectively, production functions inclusive of physical constraints and costs, and incentive structures of formally specified as well as de facto institutions. Interviews conducted over 2013-2016 with key stakeholders in four case provinces/regions with significant wind development inform tracing of the processes of grid and market operations (Chapter 3). A mixed-integer unit commitment and economic dispatch optimization is formulated and, based on the case studies, further tailored by adding several institutions of China's partially-liberalized system (Chapter 4). The model generates a reference picture of three of the systems as well as quantitative contributions of relevant institutions (Chapter 5). Insights from qualitative and quantitative approaches are combined iteratively for more parsimonious findings (Chapter 6). This dissertation disentangles the causes of curtailment, focusing on the directional and relative contributions of institutions, technical issues, and potential interactive effects. Wind curtailment is found to be closely tied to engineering constraints, such as must-run combined heat and power (CHP) in northern winters. However, institutional causes -- inflexibilities in both scheduling and inter-provincial trading -- have a larger impact on curtailment rates. Technical parameters that are currently set administratively at the provincial level (e.g., coal generator minimum outputs) are a third and important leading cause under certain conditions. To assess the impact of China's broader reform of the electricity system on wind curtailment, this dissertation examines in detail "marketizing" experiments. In principle, spot markets for electricity naturally prioritize wind, with near zero marginal cost, thereby contributing to low curtailment. However, China has not yet created a spot market and this dissertation finds that its implementation of other electricity markets in practice operates far from ideal. Market designs follow a similar pattern of relying on dual-track prices and out-of-market parameters, which, in the case of electricity, leave several key institutional causes of inefficiency and curtailment untouched. Compared to other sectors with successful marketization occurring when markets "grow out of the plan," all of the major electricity experiments examined show deficiencies in their ability to transition to an efficient market and to cost-effectively integrate wind energy. Although China's setting is institutionally very different, results support implementation of many elements of standard electricity market prescriptions: prioritize regional (inter-provincial) markets, eliminate conflicts of interest in dispatch, and create a consistent central policy on "transition costs" of reducing central planning. Important for China, though overlooked in standard prescriptions: markets are enhanced by clarifying the connection between dispatch and exchange settlement. As is well established, power system efficiency is expected to achieve greatest gains with a short-term merit order dispatch and primarily financial market instruments, though some workable near-term deviations for the Chinese context are proposed. Ambiguous property rights related to generation plans have helped accelerate reforms, but also delay more effective markets from evolving. China shares similarities with the large class of emerging economies undergoing electricity market restructuring, for which this suggests research efforts should disaggregate planning from scheduling institutions, analyze the range of legacy sub-national trade barriers, and prioritize finding "second-best" liberalization options fit to country context in the form and order of institutional reforms. / by Michael R. Davidson. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references. / The transmissibility of a given infectious disease is often described by its basic reproduction number (Ro) - namely, the average number of secondary infections caused by an index case in a fully susceptible population. Typical approaches to modeling transmission dynamics associated with infectious disease outbreaks frequently use Ro to produce deterministic case count projections, in effect treating the affected population as homogeneous (i.e. as if every individual in the population interest has an equal likelihood of passing on the infection of interest). As a result, such approaches often fail to effectively capture transmission dynamics during real-world outbreaks in heterogeneous populations. Here, we use analytical and simulation methods to show that the treatment of Ro as the mean of a random variable (thus permitting the estimation of non-deterministic case count projections) allows us to better assess outbreak trajectory and likelihood of disease propagation in non-homogeneous populations (Chapter 2). We then empirically investigate predictors of in-population transmission heterogeneity (i.e. the fact that some individuals in a given population are more likely than others to pass on the infection of interest) within the context of Middle East Respiratory Syndrome in South Korea using a combination of statistical- and review-driven approaches (Chapter 3). Then, in Chapter 4, we explore how in-population transmission heterogeneity can be used to our advantage through the deployment of risk-informed interventions (i.e. in which individuals who are more likely to pass on the infection of interest are exclusively targeted to receive the intervention) during infectious disease outbreaks. More specifically, we use the analytical and simulation methods first introduced in Chapter 2 - paired with inpopulation transmission heterogeneity data from Chapter 3 - to compare the utility of a variance-informed deployment scheme against a traditional, uniform deployment scheme (i.e. in which every individual has an equal likelihood of receiving the intervention). Finally, building off of our findings in Chapters 2, 3, and 4, we recommend four interrelated policies in Chapter 5 that aim to (1) normalize the treatment and reporting of Ro as the mean of a random variable and (2) improve access to the data required to sufficiently capture population heterogeneity when modeling disease propagation. / by Maimuna Shahnaz Majumder. / Ph. D. in Engineering Systems

Thesis: Ph. D., Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015. / Vita. Cataloged from PDF version of thesis. / Includes bibliographical references (pages 227-237). / Inclusive wealth (IW) measures the productive base of an economy, which is a linear index of its capital asset stocks. Changes to IW per capita over time track changes to intergenerational human well-being, thus non-declining IW per capita indicates sustainable development. National IW has only been measured retrospectively; this dissertation models and projects IW prospectively, measuring the impact of alternative infrastructure plans on IW. The focus of the work is on electricity planning in oil-exporting countries. Domestic oil consumption in these countries, driven by increasing electricity use, threatens long-term development by reducing the export revenue on which the government and economy depends. First, I develop a system dynamics model that connects electric power capacity expansion with macroeconomic development, tabulating both infrastructure costs and impacts to the capital stocks of IW over time. The Kingdom of Saudi Arabia (KSA) is the primary case study. Second, I analyze the capital stock projections generated by the model across a range of scenarios and countries. Under the baseline IW formulation, KSA experiences a negative annual growth rate to inclusive wealth per capita to 2050. However, adjusted formulations allow the possibility of periods of positive growth, and a non-oil sector that is less dependent on the oil sector will shift the IW trajectory upwards. Compared to KSA, Kuwait is likely to experience larger per capita declines in IW. The United Arab Emirates (UAE), on the other hand, will potentially experience positive growth rates in per capita IW starting in 2028. Third, I analyze the IW impacts of non-fossil investments in electricity infrastructure, specifically nuclear and solar, between now and 2050. In KSA, the produced capital benefits of non-fossil investment outweigh the oil capital costs (to finance the infrastructure) across a range of uncertainties. Including human capital benefits raises net benefits by an order of magnitude. The optimal allocation of nuclear and solar power ultimately depends on the evaluation metric used. The UAE gains least from non-fossil investment, since it uses comparatively less oil in its electricity system, while Kuwait experiences gains similar to KSA. Importantly, using IW as the basis for electricity policy evaluation yields qualitatively different prescriptions than a least-cost capacity expansion model. / by Ross D. Collins. / Ph. D.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 114-120). / This thesis proposes a research framework that leverages high-resolution traffic and urban infrastructure data to improve analytical approximation methods used to inform strategic decisions in designing last-mile distribution systems. In particular, this thesis explores the effects of the road-network on the circuity of local trips, and introduces data-driven extensions to improve predictive performance of route distance approximation methods by increasing the resolution of the underlying urban road-network. Overall, these circuity-based extensions significantly increase the real-world validity of routing approximations compared to classical methods, and entail relevant implications in the configuration of logistics networks within urban markets. The framework presented in this thesis entails three inter-dependent levels of analysis: individual trip, consolidated route and last-mile network levels. In Chapter 2, we introduce a method to quantify and analyze the network circuity of local trips leveraging contemporary traffic datasets. Using the city of Sao Paulo as the primary illustrative example and a combination of supervised and unsupervised machine learning methods, significant heterogeneities in local network circuity are observed, explained by dimensional and topological properties of the road-network. Results from Sao Paulo are compared to seven additional large and medium-sized urban areas in Latin America and the United States. At a coarse-grained level of analysis, we observe similar correlations between road-network properties and local circuity across these cities. In Chapter 3, this thesis proposes a data-driven extension to continuum approximation-based methods used to predict urban route distances. This extension efficiently incorporates the circuity of the underlying road-network into the approximation method to improve distance predictions in more realistic settings. The proposed extension significantly outperforms classic methods, which build on the assumption of travel according to the rectilinear distance metric within urban areas. By only marginally increasing the data collection effort, results of the proposed extension yield error reductions between 20-30% in mean absolute percentage error compared to classical approximation methods and are within 10 - 20% compared to near-optimal solutions obtained with a local search heuristic. Further, by providing a real-world validation of classic continuum approximation-based methods, we explore how contemporary mapping technologies and novel sources of geo-spatial and traffic data can be efficiently leveraged to improve the predictive performance of these methods. Finally, building on the augmented route distance approximation, in Chapter 4 we explore the effect of road-network circuity on the design and planning of urban last-mile distribution systems. These improved routing approximations are used within an integer linear programming model to solve large-scale, real-world instances of the two-echelon capacitated location routing problem. Using the parcel delivery operation of Brazil's largest e-commerce platform in the city of Sao Paulo as the primary example to illustrate the impact and relevance of this work, we demonstrate how explicitly accounting for local variations in road-network circuity can yield relevant implications for fleet capacity planning, the location of urban distribution facilities, and the definition of facility-specific service areas. Results indicate that failing to account for local circuity would underestimate the necessary fleet size by 20% and would increase the total last-mile network cost by approximately 8%. / by Daniel Esteban Merchán Dueñas. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, June 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 102-115). / This work aims to provide a widely applicable modeling framework that can be used to credibly investigate materials scarcity risks for various types of commodities. Different from existing literature, this work contributes to a better understanding of commodity scarcity risk, specifically copper future consumption on several fronts. Firstly, it introduces an elaborate price mechanism absent in comparable materials flow assessment. It teases out short term and long term substitution, allowing consumers to switch from one type of commodity to another based on price signals and their respective price elasticities of demand. Secondly, the model allows for individual deposit tracking, which allows the modeler to extract ore grade information as a function of consumption and reserve size. Thirdly, it models the supply side on an agent-based basis, allowing for aggregation of granular information, capturing potential emergent phenomena. We believe these three aspects, which are least addressed (none of existing work has addressed the first aspect, and few have addressed the second or the third), are important in assessing scarcity risks. Without them, scarcity assessment is likely to be biased. We hope our work may serve as some sort of foundation upon which more reliable future work on mineral scarcity evaluation can be carried out. / by Jingshu Zhang. / Ph. D. in Engineering Systems

Thesis: Ph. D., Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 123-130). / Sustainability negotiations and decisions require the integration of scientific information with stakeholder interests. Mathematical models help elucidate the physical world and therefore may orient the negotiators in a shared understanding of the physical world. Many researchers suggest collaborative modeling to facilitate integrating scientific information and stakeholder interests. In this thesis, I use methods that enable repeated instances of the same decision; the exploration of alternatives to model use (e.g. learning of a model's logic, relevant information, or irrelevant information); and the exploration of alternatives to collaborative modeling (e.g. using an expert model or not using a model). This thesis comprises two studies that use serious game role-play simulations. The first study is a computer-driven role-play simulation of governmental policy creation and the second is a five-party role-play simulation to negotiate a more sustainable end-of-life for used paper coffee cups. In the first study, model users reached the Pareto Frontier-the set of non-dominated points-more readily (13%) than non-model-users (2.5%) and model users discovered the win-win nature of electricity access with higher frequency (63%) than non-model users (9%). Participants who learned of the model's logic through presentation performed nearly as well as model users. In the second study, model use shortened the (mean) duration of the negotiation from 55 minutes to 45 minutes. Negotiating tables that co-created a model had a higher likelihood of reaching favorable agreements (44% compared to 25%). Model use did not significantly alter the value distribution among parties. Tables of negotiators used the model in two predominant manners: to test alternatives as they generated potential agreements and to verify a tentative agreement. The former resulted in higher mean table values than the latter. Together, these studies demonstrate: that mathematical models can be used in sustainability negotiations and decisions with good effect; that learning about the insights of a model is beneficial in decision making-but using a model is more beneficial; and that collaborative model building can provide better negotiation outcomes than using an expert model and can be faster than not using a model. / by Ellen Czaika. / Ph. D.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 151-163). / Water supply infrastructure planning faces many uncertainties. Uncertainty in short-term in rainfall and runoff, groundwater storage, and long-term climate change impacts water supply forecasts. Population and economic growth drive urban water demand growth at rapid but uncertain rates. Overbuilding infrastructure can lead to expensive stranded assets and unnecessary environmental impacts, while under building can cause reliability outages with impacts on the economy, ecosystems, and human health. This dissertation assesses the potential for Bayesian learning about uncertainty to enable flexible, adaptive approaches in which infrastructure can be changed over time to reduce cost risk while achieving reliability targets. It develops a novel planning framework that: 1) classifies uncertainties and applies appropriate, differentiated uncertainty analysis tools, 2) applies Bayesian inference to physical models of hydrology and climate to develop dynamic uncertainty estimates, and 3) uses stochastic dynamic programming and engineering options analysis to assess the value of flexibility in mitigating cost and reliability risk. This framework is applied to three applications. Chapter 3 evaluates the potential for modular desalination design to manage multiple, diverse uncertainties -- streamflow, demand growth, and the cost of water shortages -- in Melbourne, Australia. Chapter 4 addresses uncertainty in groundwater resources in desalination planning in Riyadh, Saudi Arabia, and Chapter 5 addresses model uncertainty in climate change projections in a dam design problem in Mombasa, Kenya. Across all three applications, we find value in flexible infrastructure planning with a 9-28% reduction in expected cost. However, the performance of flexible approaches compared to traditional robust approaches varies considerably and is influenced by technology choice, economies of scale, discounting, the presence of irreducible stochastic variability, and the value society places on water reliability. / by Sarah Marie Fletcher. / Ph. D. in Engineering Systems