The recent generation of Agile Earth Observation Satellite (AEOS) has emerged to be highly effective due to its increased attitude maneuvering capabilities. However, due to these increased degrees of freedom in maneuverability, the scheduling problem has become increasingly difficult than its non-agile predecessors. The AEOS scheduling problem consists of finding an optimal assignment of user-requested imaging tasks to the respective AEOSs in their orbits by satisfying the operational resource constraints in a specified time frame. Some of these tasks might require imaging the same area of interest (AOI) multiple times, while in some tasks, the AOIs are too large for the AEOS to image in a single attempt. Some tasks might even arise while the AEOSs are preoccupied with existing tasks. This thesis focuses on formulating the AEOS scheduling models where onboard energy and memory constraints while operating and the task specifications are diverse. A mixed-integer non-linear scheduling problem with a reward factor has been considered in order to handle multiple scan requirements for a task. Although initially, it is assumed that the AOIs are small, this work is extended to a three-stage optimization framework to handle the segmentation of large AOIs into smaller regions that can be imaged in a single scan. The uncertainty regarding scan failure is handled through a Markov Decision Process (MDP). These two proposed methods have significant benefits when tasks are available to schedule prior to the mission. However, they lack the flexibility to accommodate newly arrived tasks during the mission. When multiple new tasks arrive during the mission, predictive scheduling based on learning historical data of task arrivals is proposed, which can schedule tasks in an online manner faster than complete rescheduling and minimize disruption from the original schedule. Evolutionary optimization-based solution methodologies are proposed to solve these models and are validated with simulations. / Thesis / Doctor of Philosophy (PhD)

Electric power systems are undergoing a paradigm shift where an increasing number of variable renewable energy resources such as wind and solar power are being introduced to all levels of existing power grids. At the same time consumers are gaining a more active role where self energy production and home automation solutions are no longer uncommon. This challenges traditional power systems which were designed to serve as a centralized top-down solution for providing electricity to consumers. Demand response has risen as a promising solution to cope with some of the challenges that this shift is creating. In this thesis, control and scheduling studies using demand response, and consumer load models adapted to environments similar to Sweden are proposed and evaluated. The studies use model predictive control approaches for the purpose of providing ancillary and financial services to electricity market actors using thermal flexibility from detached houses. The approaches are evaluated on use-cases using data from Sweden for the purpose of reducing power imbalances of a balance responsible player and congestion management for a system operator. Simulations show promising results for reducing power imbalances by up to 30% and managing daily congestion of 5-19 MW using demand response. Moreover, a consumer load model of an office building is proposed using a gray-box modeling approach combining physical understanding of buildings with empirical data. Furthermore, the proposed consumer load model along with a similar model for detached houses are packaged and made freely available as MATLAB applications for other researchers and stakeholders working with demand response. The applications allow the user to generate synthetic electricity load profiles for heterogeneous populations of detached houses and office buildings down to 1-min resolution. The aim of this thesis has been to summarize and discuss the main highlights of the included articles. The interested reader is encouraged to investigate further details in the second part of the thesis as they provide a more comprehensive account of the studies and models proposed. / <p>QC 20171011</p>

The area of focus for this research is the Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) with Stochastic Task Insertion (STI). The STI problem is a specific form of the SRCPSP, which may be considered to be a cross between two types of problems in the general form: the Stochastic Project Scheduling Problem, and the Resource Constrained Project Scheduling Problem. The stochastic nature of this problem is in the occurrence/non-occurrence of tasks with deterministic duration. Researchers Selim (2002) and Grey (2007) laid the groundwork for the research on this problem. Selim (2002) developed a set of robustness metrics and used these to evaluate two initial baseline (predictive) scheduling techniques, optimistic (0% buffer) and pessimistic (100% buffer), where none or all of the stochastic tasks were scheduled, respectively. Grey (2007) expanded the research by developing a new partial buffering strategy for the initial baseline predictive schedule for this problem and found the partial buffering strategy to be superior to Selim s extreme buffering approach. The current research continues this work by focusing on resource aspects of the problem, new buffering approaches, and a new rescheduling method. If resource usage is important to project managers, then a set of metrics that describes changes to the resource flow would be important to measure between the initial baseline predictive schedule and the final as-run schedule. Two new sets of resource metrics were constructed regarding resource utilization and resource flow. Using these new metrics, as well as the Selim/Grey metrics, a new buffering approach was developed that used resource information to size the buffers. The resource-sized buffers did not show to have significant improvement over Grey s 50% buffer used as a benchmark. The new resource metrics were used to validate that the 50% buffering strategy is superior to the 0% or 100% buffering by Selim. Recognizing that partial buffers appear to be the most promising initial baseline development approach for STI problems, and understanding that experienced project managers may be able to predict stochastic probabilities based on prior projects, the next phase of the research developed a new set of buffering strategies where buffers are inserted that are proportional to the probability of occurrence. The results of this proportional buffering strategy were very positive, with the majority of the metrics (both robustness and resource), except for stability metrics, improved by using the proportional buffer. Finally, it was recognized that all research thus far for the SRCPSP with STI focused solely on the development of predictive schedules. Therefore, the final phase of this research developed a new reactive strategy that tested three different rescheduling points during schedule eventuation when a complete rescheduling of the latter portion of the schedule would occur. The results of this new reactive technique indicate that rescheduling improves the schedule performance in only a few metrics under very specific network characteristics (those networks with the least restrictive parameters). This research was conducted with extensive use of Base SAS v9.2 combined with SAS/OR procedures to solve project networks, solve resource flow problems, and implement reactive scheduling heuristics. Additionally, Base SAS code was paired with Visual Basic for Applications in Excel 2003 to implement an automated Gantt chart generator that provided visual inspection for validation of the repair heuristics. The results of this research when combined with the results of Selim and Grey provide strong guidance for project managers regarding how to develop baseline predictive schedules and how to reschedule the project as stochastic tasks (e.g. unplanned work) do or do not occur. Specifically, the results and recommendations are provided in a summary tabular format that describes the recommended initial baseline development approach if a project manager has a good idea of the level and location of the stochasticity for the network, highlights two cases where rescheduling during schedule eventuation may be beneficial, and shows when buffering proportional to the probability of occurrence is recommended, or not recommended, or the cases where the evidence is inconclusive.