http://hdl.handle.net/1721.1/7885 Wed, 19 Jun 2013 11:51:56 GMT 2013-06-19T11:51:56Z http://hdl.handle.net/1721.1/79147 Incorporating operational flexibility into electric generation planning : impacts and methods for system design and policy analysis Palmintier, Bryan S. (Bryan Stephen) This dissertation demonstrates how flexibility in hourly electricity operations can impact long-term planning and analysis for future power systems, particularly those with substantial variable renewables (e.g., wind) or strict carbon policies. Operational flexibility describes a power system's ability to respond to predictable and unexpected changes in generation or demand. Planning and policy models have traditionally not directly captured the technical operating constraints that determine operational flexibility. However, as demonstrated in this dissertation, this capability becomes increasingly important with the greater flexibility required by significant renewables (>=20%) and the decreased flexibility inherent in some low-carbon generation technologies. Incorporating flexibility can significantly change optimal generation and energy mixes, lower system costs, improve policy impact estimates, and enable system designs capable of meeting strict regulatory targets. Methodologically, this work presents a new clustered formulation that tractably combines a range of normally distinct power system models, from hourly unit-commitment operations to long-term generation planning. This formulation groups similar generators into clusters to reduce problem size, while still retaining the individual unit constraints required to accurately capture operating reserves and other flexibility drivers. In comparisons against traditional unit commitment formulations, errors were generally less than 1% while run times decreased by several orders of magnitude (e.g., 5000x). Extensive numeric simulations, using a realistic Texas-based power system show that ignoring flexibility can underestimate carbon emissions by 50% or result in significant load and wind shedding to meet environmental regulations. Contributions of this dissertation include: 1. Demonstrating that operational flexibility can have an important impact on power system planning, and describing when and how these impacts occur; 2. Demonstrating that a failure to account for operational flexibility can result in undesirable outcomes for both utility planners and policy analysts; and 3. Extending the state of the art for electric power system models by introducing a tractable method for incorporating unit commitment based operational flexibility at full 8760 hourly resolution directly into planning optimization. Together these results encourage and offer a new flexibility-aware approach for capacity planning and accompanying policy design that can enable cleaner, less expensive electric power systems for the future. Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.; 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 (p. 253-272). Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/1721.1/79147 2013-01-01T00:00:00Z http://hdl.handle.net/1721.1/78483 Influencing managerial cognition and decisions using scenarios for long-range planning Phadnis, Shardul Sharad, 1978- This dissertation presents empirical findings related to two aspects of long-range planning: scenario planning as a planning method and cognition of planners. Long-range planning situations are encountered when designing public infrastructures (e.g. transportation systems) as well as developing strategies for corporate enterprises (e.g. firms' supply chains), due to the long implementation times and/or long lives of the invested assets. Such investments tend to have high stakes, face extreme uncertainty about the future environment they encounter, and have an open-systems nature as the implementation and operation of assets affects and is affected by the actions of many and diverse stakeholders. Three research questions pertaining to these aspects are answered in this work as three stand-alone studies. The first study (Chapter 2) examines the effects of scenario planning on long-range investment decisions made by field experts. The results of three field experiments show that experts systematically change their investment decisions and/or their confidence in them after evaluating the investments in a scenario. Field experts are also more likely to invest in flexible strategies after being exposed to multiple scenarios. The second study (Chapter 3) presents an extensive and an abridged version of the scenario creation process. Instead of seeing scenario-creation as an art, this research provides two versions of a more engineered scenario-creation process, and demonstrates their application in two separate field studies. Both versions of the process are presented with detailed instructions and rationale for performing each step. This study also provides clear definitions of the terms used in the process description and grounds them in the organizations literature. The third study (Chapter 4) explores the relationship between a manager's perceptions and beliefs about future environment, and the strategies s/he recommends. Using a prospective research design, this study first tests three hypotheses about general characteristics of managerial cognition. A closer look at different cognitive types in this data reveals unsuspected patterns in strategic thinking of managers of different types. A typology of managerial cognition is built using this inherent variation. Inductive analysis shows that managers of different cognitive types envision strikingly different types of strategies. Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 209-222). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/78483 2012-01-01T00:00:00Z http://hdl.handle.net/1721.1/78482 Human and modeling approaches for humanitarian transportation planning Gralla, Erica Lynn Recent disasters have highlighted the need for more effective supply chain management during emergency response. Planning and prioritizing the use of trucks and helicopters to transport humanitarian aid to affected communities is a key logistics challenge. This dissertation explores ways to improve humanitarian transportation planning by building on the strengths of both humans and models. The changing, urgent, multi-objective context of humanitarian aid makes it challenging to formulate and deploy useful planning models. Humans are better able to understand the context, but struggle with the complexity of the problem. This research investigates the strengths and weaknesses of human transportation planners in comparison with models, with the goal of supporting both- better human decision-making and better models for humanitarian transportation planning. Chapter 2 investigates how experienced humanitarian logisticians build transportation plans in a simulated emergency response. Based on an ethnographic study of ten logistics response teams, I show how humans come to understand the problem and its objectives through sensemaking, and solve it through a search-like series of decisions guided by goal-oriented decision rules. I find that the definition of objectives is an important strength of the sensemaking process, and that the human reliance on greedy search may be a weakness of human problem-solving. Chapter 3 defines a performance measure for humanitarian transportation plans, by measuring the importance of the objectives identified in the ethnographic study. I use a conjoint analysis survey of expert humanitarian logisticians to quantify the importance of each objective and develop a utility function to value the performance of aid delivery plans. The results show that the amount of cargo delivered is the most important objective and cost the least; experts prefer to prioritize vulnerable communities and critical commodities, but not to the exclusion of others. Chapter 4 investigates the performance of human decision-making approaches in comparison to optimization models. The human decision-making processes found in Chapter 2 are modeled as heuristic algorithms and compared to a mixed-integer linear program. Results show that optimization models create better transportation plans, but that human decision processes could be nearly as effective if implemented consistently with the right decision rules. Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/78482 2012-01-01T00:00:00Z http://hdl.handle.net/1721.1/78481 Measuring supply chain carbon efficiency : a carbon label framework Craig, Anthony (Anthony J.) In the near term, efficiency improvements represent a key option for reducing the impacts of climate change. The growing awareness of climate change has increased the attention regarding the carbon emissions "embedded" in the products we consume. This increased attention creates a need to measure and improve the carbon efficiency of the supply chains that produce those goods. In this thesis we present a method for measuring the carbon efficiency of a supply chain that recognizes the decentralized nature of supply chains. First, drawing from concepts in supply chain performance measurement and eco-efficiency we propose a definition of supply chain carbon efficiency that is consistent with the idea of a product's carbon footprint. We present Life Cycle Assessment (LCA), a method for quantifying the environmental impact of a product or service, as the appropriate method of measuring a product's carbon footprint and demonstrate the use of LCA through a case study involving the supply chain of bananas. Next, we characterize the difficulty and uncertainty in performing an LCA of a supply chain through an analysis of our case study of bananas. We present a framework to reduce the uncertainty though the concept of a carbon label. The carbon label provides a system where firms can measure the carbon footprint of their activities and share this information with their supply chain partners. We identify the role of third parties in facilitating information sharing and define the characteristics that describe the carbon label. Finally, we demonstrate how the carbon label works in the context of the supply chain. Through an analysis of the mode and carrier assignment steps in an integrated supply chain we develop new metrics that show how sharing information can increase the accuracy of the measured carbon footprint and improve decision-making. We provide incentive for firms to share information through the development of a vertical differentiation model of product carbon labels. Our model shows how consumer demand for lower carbon products drives reductions in the carbon footprint throughout the supply chain and induces firms to voluntarily disclose their carbon footprint. Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 273-293). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/78481 2012-01-01T00:00:00Z