Theses and Dissertations from UMD
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Item Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies(2016) Champion, Billy Ray; Gabriel, Steven A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.Item Optimization Models for Speed Control in Air Traffic Management(2015) Jones, James Calvin; Lovell, David J; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In a typical air traffic control environment, the precise landing times of en route aircraft are not set until each aircraft approaches the airspace adjacent to the destination airport. In times of congestion, it is not unusual for air traffic controllers to subject arriving aircraft to various maneuvers to create an orderly flow of flights onto an arrival runway. Typical maneuvers include flying in zig-zag patterns, flying in race track shaped patterns and tromboning. These maneuvers serve to delay the arrival time of the flight while also burning additional fuel. On the other hand, if the arrival time was established much earlier, then such delay could be realized by simply having flights fly slower while still at a higher altitude, which would incur much less fuel burn than the described maneuvers. Yet despite its potential benefit, thus far little has been done to promote the management of flights using speed control in the presence of uncertainty. This dissertation presents a set of models and prescriptions designed to use the mechanism of speed control to enhance the level of coordination used by FAA managers at the tactical and pre-tactical level to better account for the underlying uncertainty at the time of planning. Its models deal with the challenge of assigning delay to aircraft approaching a single airport, well in advance of each aircraft’s entry into the terminal airspace. In the first approach, we assume control of all airborne flights at a distance of 500 nm while assuming no control over flights originating less than 500 nm from the airport. We propose a set of integer programming models designed to issue arrival times for controlled flights in the presence of the uncertainty associated with the unmanaged flights. In the second approach, we assume control over all flights by subjecting flights to a combination of air and ground delay. Both approaches show strong potential to transfer delay from the terminal to the en route phase of flight and achieve fuel savings. Building on these ideas we then formulate an approach to incorporate speed control into Ground Delay Programs. We propose enhancements for equitably rationing airport access to carriers and develop a revised framework to allow carriers to engage in Collaborative Decision Making. We present new GDP control procedures and also new flight operator GDP planning models. While the ability to achieve all the benefits we describe will require NextGen capabilities, substantial performance improvements could be obtained even with a near-term implementation.Item Resource Allocation in Air Traffic Flow-Constrained Areas with Stochastic Termination Times(2010) Ganji, Moein; Lovell, David J.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation we address a stochastic air traffic flow management problem. This problem arises when airspace congestion is predicted, usually because of a weather disturbance, so that the number of flights passing through a volume of airspace (flow constrained area - FCA) must be reduced. We formulate an optimization model for the assignment of dispositions to flights whose preferred flight plans passed through the FCA. For each flight, the disposition can be either to depart as scheduled but via a secondary route thereby avoiding the FCA, or to use the originally intended route but to depart with a controlled (adjusted) departure time and accompanying ground delay. We model the possibility that the capacity of the FCA may increase at some future time once the weather activity clears. The model is a two-stage stochastic program that represents the time of this capacity windfall as a random variable, and determines expected costs given a second-stage decision, conditioning on that time. We also allow the initial reroutes to vary from a conservative or pessimistic approach where all reroutes avoid the weather entirely to an optimistic or hedging strategy where some or all reroute trajectories can presume that the weather will clear by the time the FCA is reached, understanding that a drastic contingency may be necessary later if this turns out not to be true. We conduct experiments allowing a range of such trajectories and draw conclusions regarding appropriate strategies.Item A Stochastic Equilibrium Model for the North American Natural Gas Market(2005-07-26) Zhuang, Jifang; Gabriel, Steven A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation is an endeavor in the field of energy modeling for the North American natural gas market using a mixed complementarity formulation combined with the stochastic programming. The genesis of the stochastic equilibrium model presented in this dissertation is the deterministic market equilibrium model developed in [Gabriel, Kiet and Zhuang, 2005]. Based on some improvements that we made to this model including proving new existence and uniqueness results, we present a multistage stochastic equilibrium model with uncertain demand for the deregulated North American natural gas market using the recourse method of the stochastic programming. The market participants considered by the model are pipeline operators, producers, storage operators, peak gas operators, marketers and consumers. Pipeline operators are described with regulated tariffs but also involve "congestion pricing" as a mechanism to allocate scarce pipeline capacity. Marketers are modeled as Nash-Cournot players in sales to the residential and commercial sectors but price-takers in all other aspects. Consumers are represented by demand functions in the marketers' problem. Producers, storage operators and peak gas operators are price-takers consistent with perfect competition. Also, two types of the natural gas markets are included: the long-term and spot markets. Market participants make both high-level planning decisions (first-stage decisions) in the long-term market and daily operational decisions (recourse decisions) in the spot market subject to their engineering, resource and political constraints as well as market constraints on both the demand and the supply side, so as to simultaneously maximize their expected profits given others' decisions. The model is shown to be an instance of a mixed complementarity problem (MiCP) under minor conditions. The MiCP formulation is derived from applying the Karush-Kuhn-Tucker optimality conditions of the optimization problems faced by the market participants. Some theoretical results regarding the market prices in both markets are shown. We also illustrate the model on a representative, sample network of two production nodes, two consumption nodes with discretely distributed end-user demand and three seasons using four cases.