This dissertation examines resource allocation optimization in the areas of Smart Grid and high-performance computing (HPC). The primary focus of this work is resource allocation related to Smart Grid, particularly in the areas of aggregated demand response and demand side management. Towards that goal, a framework for heuristic optimization for demand response in the Smart Grid is designed. The optimization problem, denoted Smart Grid resource allocation (SGRA), controls a large set of individual customer assets (e.g., smart appliances) to enact a beneficial change on the electric power system (e.g., peak load reduction). In one part of this dissertation, the SGRA heuristic framework uses a proposed aggregator-based approach. The aggregator is a for-profit entity that uses information about customers' smart appliances to create a schedule that maximizes its profit. To motivate the customers to participate with the aggregator, the aggregator offers a reduced rate of electricity called customer incentive pricing (CIP). A genetic algorithm is used to find a smart appliance schedule and CIP to maximize aggregator profit. By optimizing for aggregator profit, the peak load of the system is also reduced, resulting in a beneficial change for the entire system. Visualization techniques are adapted, and enhanced, to gain insight into the results of the aggregator-based optimization.
The secondary focus of the research is resource allocation for scientific applications in HPC using a dual-stage methodology. In the first stage, a batch scheduler assigns a number of homogeneous processors from a set of heterogeneous parallel machines to each application in a batch of parallel, scientific applications. The scheduler assigns machine resources to maximize the probability that all applications complete by a given time, denoted the makespan goal. This objective function is denoted robustness. The second stage uses runtime optimization in the form of dynamic loop scheduling to minimize the execution time of each application using the resources allocated in the first stage. It is shown that by combining the two optimization stages, better performance is achieved than by using either approach separately or by using neither.
The specific contributions of this preliminary dissertation are: (a) heuristic frameworks and mathematical models for resource allocation in the Smart Grid and dual-stage HPC are designed, (b) CIP is introduced to allow an aggregator profit and encourage customer participation, and (c) heuristics are designed and analyzed within the two problem domains to evaluate their performance. To complete the dissertation, the aggregator-based SGRA will be expatiated on to include a variety of asset types (e.g., electric water heaters, plug-in hybrid electric vehicles). The SGRA problem is inherently stochastic, necessitating the design of stochastic asset models, heuristics, and objective function(s). In addition to optimizing from the aggregator point-of-view, it is necessary to take into consideration the customer perspective as well. In that regard, a non-myopic customer optimization problem, in the form of a partially observable Markov decision process, is being researched to determine when to make the customer assets available to minimize electricity costs over a long time horizon.
Adviser: Prof. HJ Siegel Co-Adviser: Prof. Maciejewski Non-ECE Member: Prof. Thomas Bradley, Mechanical Engineering Member 3: Prof. Suryanarayanan, ECE Addional Members: N/A
Publications:  Florina M. Ciorba, Timothy Hansen, Srishti Srivastava, Ioana Banicescu, Anthony A. Maciejewski, and Howard Jay Siegel, “A Combined Dual-Stage Framework for Robust Scheduling of Scientific Applications in Heterogeneous Environments with Uncertain Availability,” 21st Heterogeneity in Computing Workshop (HCW 2012), cosponsors: IEEE Computer Society and U.S. Office of Naval Research,in the proceedings of 2012 International Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), pp. 187-200, Shanghai, China, May 2012.
 Timothy Hansen, Robin Roche, Siddharth Suryanarayanan, Howard Jay Siegel, Daniel Zimmerle, Peter M. Young, and Anthony A. Maciejewski, “A Proposed Framework for Heuristic Approaches to Resource Allocation in the Emerging Smart Grid,” IEEE PES International Conference on Power Systems Technology (POWERCON 2012), sponsor: IEEE Power and Energy Society, 6 pp., Auckland, New Zealand, Oct. 2012.
 Timothy Hansen, Siddharth Suryanarayanan, Anthony A. Maciejewski, and Howard Jay Siegel, “A Visualization Aid for Demand Response Studies in the Smart Grid,” under review for conference, 5 pp., 2014.
 Timothy Hansen, Robin Roche, Siddharth Suryanarayanan, Anthony A. Maciejewski, and Howard Jay Siegel, “Heuristic Optimization for an Aggregator-based Resource Allocation in the Smart Grid,” under review for journal, 8 pp., 2014.
 Timothy Hansen, Florina M. Ciorba, Anthony A. Maciejewski, Howard Jay Siegel, Srishti Srivastava, and Ioana Banicescu, “Heuristics for Robust Allocation of Resources to Parallel Applications with Uncertain Execution Times in Heterogeneous Systems with Uncertain Availability,” under review for conference, 6 pp., 2014.
Program of Study: CS545 CS555 ECE514 ECE520 ECE554 ECE565 ECE666 GRAD511