Civil & Environmental Engineering
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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 Methods for Employing Real Options Models to Mitigate Risk in R&D Funding Decisions(2011) Eckhause, Jeremy Michael; Gabriel, Steven A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Government acquisitions requiring research and development (R&D) efforts are fraught with uncertainty. The risks are often mitigated by employing a multi-stage competition, with multiple projects funded initially until a single successful project is selected. While decision-makers recognize they are using a real options approach, analytical tools are often unavailable to evaluate optimal decisions. The use of these techniques for R&D project selection to reduce the uncertainties has been shown to increase overall project value. This dissertation first presents an efficient stochastic dynamic programming (SDP) approach that managers can use to determine optimal project selection strategies and apply the proposed approach on illustrative numerical examples. While the SDP approach produces optimal solutions for many applications, this approach does not easily accommodate the inclusion of a budget-optimal allocation or side constraints, since its formulation is scenario specific. Thus, we then formulate an integer program (IP), whose solution set is equivalent to the SDP model, but facilitates the incorporation of these features and can be solved using available commercial IP solvers. The one-level IP formulation can solve what is otherwise a nested two-level problem when solved as an SDP. We then compare the performance of both models on differently sized problems. For larger problems, where the IP approach appears to be untenable, we provide heuristics for the two-level SDP formulation to solve problems efficiently. Finally, we apply these methods to carbon capture and storage (CCS) projects in the European Union currently under development that may be subject to public funding. Taking the perspective of a funding agency, we employ the real options models presented in this dissertation for determining optimal funding strategies for CCS project selection. The models demonstrate the improved risk reduction by employing a multi-stage competition and explicitly consider the benefits of knowledge spillover generated by competing projects. We then extend the model to consider two sensitivities: 1) the flexibility to spend the budget among the time periods and 2) optimizing the budget, but specifying each time period's allocation a priori. State size, scenario reduction heuristics and run-times of the models are provided.