Theses - Electrical Engineering and Computer Sciences
http://hdl.handle.net/1721.1/7814
2017-03-28T23:28:36ZPrivatizing the Saudi electricity sector
http://hdl.handle.net/1721.1/107590
Privatizing the Saudi electricity sector
Alhumaid, Mohammed S. (Mohammed Saud)
Electricity demand in Saudi Arabia has been growing rapidly with an average peak demand growth rate of 6% over the past decade. Currently, the structure of the electricity industry is based on a monopoly framework dominated by a government owned utility company (Saudi Electric Company (SEC)). Furthermore, electricity prices in KSA are heavily subsidized and as a result, SEC relies heavily on government support through grants and soft loans to finance expansion projects to meet growing demand. In order to alleviate the financial dependency of the electricity sector on government funding, the system regulator (ECRA) announced major reform plans intended to encourage private sector participation in the electricity industry. This research aims to evaluate regulatory reform options available to the Saudi government for achieving privatization objectives. Chapter 1 lays the foundation of electricity regulation and addresses technical, economical, and regulatory aspects of electricity trading. Chapter 2, deep dives into the liberalization of the electricity industry in Great Britain as a pioneer case study with main take away being the importance of ownership unbundling in structural reforms. Chapter 3 provides a description of the current status of the sector in the KSA. It also discusses the regulatory options available to the government. Chapter 4 applies a mathematical model based on the concept of "Supply Function Equilibrium" to evaluate the government proposal of splitting the generation assets of SEC between four-generation companies. The model analyzes the level of market competition as a result of the proposed plan. The analysis shows that the establishment of four-generation companies will result in imperfect competition and that additional measures are needed to mitigate market power. Chapter 5 provides a summary of the proposed recommendations and suggests future work.
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.; Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 97-100).
2016-01-01T00:00:00ZGraphical model driven methods in adaptive system identification
http://hdl.handle.net/1721.1/107499
Graphical model driven methods in adaptive system identification
Yellepeddi, Atulya
Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible. This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods. Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods. The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems. The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.
Thesis: Ph. D., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2016.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 209-225).
2016-01-01T00:00:00ZInfluence maximization over a network : static and dynamic policies
http://hdl.handle.net/1721.1/107377
Influence maximization over a network : static and dynamic policies
Ben Chaouch, Zied
The problem of maximizing the spread of an opinion inside a social network has been investigated extensively during the past decade. The importance of this problem in applications such as marketing has been amplified by the major expansion of online social networks. In this thesis, we study opinion control policies, first under a broad class of deterministic dynamics governing the interactions inside a network, and then under the classical "Voter Model". In the former case, we design a policy that a controller can follow in order to spread an opinion inside a network with the smallest possible cost. In the latter case, we consider networks whose underlying graph is the d-dimensional integer torus Zd/n, and we design policies that minimize the expected time until the network reaches a consensus. We also show that, in dimension d >/= 2, dynamic policies do not perform significantly better than static policies, while, in dimension d = 1, optimal dynamic policies perform much better than optimal static policies..
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 131-132).
2016-01-01T00:00:00ZAggregation for modular robots in the pivoting cube model
http://hdl.handle.net/1721.1/107376
Aggregation for modular robots in the pivoting cube model
Claici, Sebastian
In this thesis, we present algorithms for self-aggregation and self-reconfiguration of modular robots in the pivoting cube model. First, we provide generic algorithms for aggregation of robots following integrator dynamics in arbitrary dimensional configuration spaces. We describe solutions to the problem under different assumptions on the capabilities of the robots, and the configuration space in which they travel. We also detail control strategies in cases where the robots are restricted to move on lower dimensional subspaces of the configuration space (such as being restricted to move on a 2D lattice). Second, we consider the problem of finding a distributed strategy for the aggregation of multiple modular robots into one connected structure. Our algorithm is designed for the pivoting cube model, a generalized model of motion for modular robots that has been effectively realized in hardware in the 3D M-Blocks. We use the intensity from a stimulus source as a input to a decentralized control algorithm that uses gradient information to drive the robots together. We give provable guarantees on convergence, and discuss experiments carried out in simulation and with a hardware platform of six 3D M-Blocks modules.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 61-66).
2016-01-01T00:00:00Z