By 2040, global CO2 emissions and energy consumption are expected to increase by 40%. In the US, buildings account for 40% of national CO2 emissions and energy consumption, of which 75% is met by fossil fuels. Reducing this impact on the environment requires both improved building energy efficiency and increased renewable utilization. To this end, this dissertation presents a demand-supplystorage- based decision framework to enable strategic energy management in smart buildings. This framework includes important but largely unaddressed aspects pertaining to building demand and supply such as occupant plugloads and the integration of weather forecast-based solar prediction, respectively. We devote the first part of our work to study occupant plugloads, which account for up to 50% of demand in high performance buildings. We investigate the impact of plugload control mechanisms based on the analysis of real-world data from experiments we conducted at NASA Ames sustainability base and Carnegie Mellon University (SV campus). Our main contribution is in extending existing demand response approaches to an occupant-in-the-loop paradigm. In the second part of this work, we describe methods to develop weather forecastbased solar prediction models using both local sensor measurements and global weather forecast data from the National Ocean and Atmospheric Administration (NOAA).We contribute to the state-of-the-art solar prediction models by proposing the incorporation of both local and global weather characteristics into their predictions. This weather forecast-based solar model plus the plugload-integrated demand model, along with an energy storage model constitutes the weather-driven plugloadintegrated decision-making framework for energy management. To demonstrate the utility of this framework, we apply it to solve an optimal decision problem with the objective of minimizing the energy-related operating costs associated with a smart building. The findings indicate that the optimal decisions can result in savings of up to 74% in the expected operational costs. This framework enables inclusive energy management in smart buildings by accounting for occupants-in-the-loop. Results are presented and discussed in the context of commercial office buildings.

Demand participation is a basic ingredient of the next generation of power exchanges in electricity markets. A key challenge in implementing demand response stems from establishing reliable market frameworks so that purchasers can estimate the demand correctly, buy as economically as possible and have the means of hedging the risk of lack of supply. System operators also need ways of estimating responsive load behaviour to reliably operate the grid. In this context, two aspects of demand response are addressed in this study: scheduling and baseline estimation. The thesis presents a market clearing algorithm including demand side reserves in a two-stage stochastic optimization framework to account for wind power production uncertainty. The results confirm that enabling the load to provide reserve can potentially benefit consumers by reducing electricity price, while facilitating a higher share of renewable energy sources in the power system. Two novel methods, Bayesian Linear regression and Kernel adaptive filtering, are proposed for baseline load forecasting in the second part of the study. The former method provides an integrated solution for prediction with full accounting for uncertainty while the latter provides an online sequential learning algorithm that is useful for short term forecasting. / Graduate / 0544 / nimahtehrani@gmail.com

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The Large Hadron Collider (LHC) located at CERN, Geneva has finally been put in production, generating unprecedented amount of data. These data are distributed across many computing centers all over the world that form the Worldwide LHC Computing Grid (WLCG). One of the main issues since the beginning of the WLCG project is an effective file access on the site level in order to fully exploit huge computing farms. The aim of this thesis is to explore existing data distribution work flows, standards, methods and protocols. An integral part of the work is the analysis of jobs of physicists to understand input/output workloads and to discover possible inefficiencies. Then, new upcoming solutions are evaluated in terms of performance, sustainability and integration into existing frameworks. It is expected that these solutions will be based on distributed file systems such as NFS 4.1, Lustre and HDFS.

A resilient power grid system is important to ensure the delivery of power to consumers while minimizing the cost of new technologies. Due to the increase of electricity consumption and CO2 emission, renewable energies and energy storage system are a compelling alternative. We started to identify decisions that need to be made, and parameters associated to model a power grid system expansion plan. Then, we investigated a utility company demand for the next 15 years. Also, we identified their current resources, and used that as a starting point. Then, we formulated an optimization model for a power grid expansion with different types of renewable energies, such as solar and wind, to meet the demand and minimize the cost of installation; as well as, a battery storage system (Lithium-ion) that is considered to come up with an optimal solution of a resilient power grid. Moreover, uncertainties of renewables are considered in the model, and robust optimization formulation is used to model them. Existing coal facilities are considered as a part of the model as well, and this part is designed on the optimization model in a way that would help decrease the use of such facilities and still manage them to meet demand. Numerical experiments are performed on several scenarios, and compared to what the utility company has forecasted in terms of cost, and renewable energies integration.A resilient power grid system is important to ensure the delivery of power to consumers while minimizing the cost of new technologies. Due to the increase of electricity consumption and CO2 emission, renewable energies and energy storage system are a compelling alternative. We started to identify decisions that need to be made, and parameters associated to model a power grid system expansion plan. Then, we investigated a utility company demand for the next 15 years. Also, we identified their current resources, and used that as a starting point. Then, we formulated an optimization model for a power grid expansion with different types of renewable energies, such as solar and wind, to meet the demand and minimize the cost of installation; as well as, a battery storage system (Lithium-ion) that is considered to come up with an optimal solution of a resilient power grid. Moreover, uncertainties of renewables are considered in the model, and robust optimization formulation is used to model them. Existing coal facilities are considered as a part of the model as well, and this part is designed on the optimization model in a way that would help decrease the use of such facilities and still manage them to meet demand. Numerical experiments are performed on several scenarios, and compared to what the utility company has forecasted in terms of cost, and renewable energies integration.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Caterina Scoglio / Many physical and engineered systems (e.g., smart grid, transportation and biomedical systems) are increasingly being monitored and controlled over a communication network. These systems where sensing, communication, computation and real time control are closely integrated are referred to as cyber physical systems (CPS). Cyber physical systems present a plethora of challenges related to their design, analysis, optimization and control. In this dissertation, we present some fundamental methodologies to analyze the optimization of physical systems over a communication network. Specifically, we consider a medium voltage DC shipboard smart grid (SSG) reconfiguration problem as a test case to demonstrate our approach. The main goal of SSG reconfiguration is to change the topology of the physical power system by switching circuit breakers, switches, and other devices in the system in order to route power effectively to loads especially in the event of faults/failures. A majority of the prior work has focused on centralized approaches to optimize the switch configuration to maximize specific objectives. These methods are prohibitively complex and not suited for agile reconfiguration in mission critical situations. Decentralized solutions proposed do reduce complexity and implementation time at the cost of optimality. Unfortunately, none of the prior efforts in this arena address the cyber physical aspects of an SSG. This dissertation aims to bridge this gap by proposing a suite of methods to analyze both centralized and decentralized SSG reconfigurations that incorporate the effect of the underlying cyber infrastructure. The SSG reconfiguration problem is a mixed integer non convex optimization problem for which branch and bound based solutions have been proposed earlier. Here, optimal reconfiguration strategies prioritize the power delivered to vital loads over semi-vital and non vital loads. In this work, we propose a convex approximation to the original non convex problem that significantly reduces complexity of the SSG reconfiguration. Tradeoff between power delivered and number of switching operations after reconfiguration is discussed at steady state. Second, the distribution of end-to-end delay associated with fault diagnosis and reconfiguration in SSG is investigated from a cyber-physical system perspective. Specifically, a cross-layer total (end-to-end) delay analysis framework is introduced for SSG reconfiguration. The proposed framework stochastically models the heterogeneity of actions of various sub-systems viz., the reconfiguration of power systems, generation of fault information by sensor nodes associated to the power system, processing actions at control center to resolve fault locations and reconfiguration, and information flow through communication network to:(1) analyze the distribution of total delay in SSG reconfiguration after the occurrence of faults; and (2) propose design options for real-time reconfiguration solutions for shipboard CPS, that meet total delay requirements. Finally, the dissertation focuses on the quality of SSG reconfiguration solution with incomplete knowledge of the overall system state, and communication costs that may affect the quality (optimality) of the resulting reconfiguration. A dual decomposition based decentralized optimization in which the shipboard system is decomposed into multiple separable subsystems with agents is proposed. Specifically, agents monitoring each subsystem solve a local concave dual function of the original objective while neighboring agents share information over a communication network to obtain a global solution. The convergence of the proposed approach under varying network delays and quantization noise is analyzed and comparisons with centralized approaches are presented. Results demonstrate the effectiveness as well as tradeoffs involved in centralized and decentralized SSG reconfiguration approaches.

We used a divide-and-conquer algorithm to recursively solve the two-dimensional problem of protein folding of an HP sequence with the maximum number of H-H contacts. We derived both lower and upper bounds for the algorithmic complexity by using the newly introduced concept of multi-directional width-bounded geometric separator. We proved that for a grid graph G with n grid points P, there exists a balanced separator A subseteq P$ such that A has less than or equal to 1.02074 sqrt{n} points, and G-A has two disconnected subgraphs with less than or equal to {2over 3}n nodes on each subgraph. We also derive a 0.7555sqrt {n} lower bound for our balanced separator. Based on our multidirectional width-bounded geometric separator, we found that there is an O(n^{5.563sqrt{n}}) time algorithm for the 2D protein folding problem in the HP model. We also extended the upper bound results to rectangular and triangular lattices.

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The concept of a value chain has assumed a dominant position in the strategic analysis of industries. However, the concept of linear value chain has recently become unsuitable as a tool to analyze some industries and to uncover many sources of value. The value grid approach allows firms to identify opportunities and threats in a more explicit way than with the traditional value chain model. Until now, there are still not many researchers working on the concept of value grid. Therefore, the purpose of this research is to exemplify the value grid model in airline industry with a case study of Scandinavian Airlines (SAS) and to illustrate its application, the provision of airline industry and content is explored to identify potential strategic implications for Scandinavian Airlines (SAS).

In the last twenty years, Google had a tremendous growth, from a small project of two PhD students to one of the most valuable companies on the globe. This growth is characterised by the versatile of the company, next to its search engine, Google explored many different value chains along the way. In this study, the value grid model is used to examine their movements. It can be stated that Google used, and uses, the paths/dimension as implied by Pil and Holweg (2006) to explore new opportunity and demand. The main reason why Google is able to do so is because of its board and management, who are innovative, and open-minded. Next to the top management is the appearance of Google in many different sectors and value chains a reason of their growth. The variety in businesses allows them to create a “Google experience”, and thus a competitive advantage in comparison with their main competitors who do not have this ability.