Abstract
This thesis addresses the utilization of Graphics Processing Units (GPUs) to improve the Power Flow (PF) analysis of modern power systems. GPUs are powerful vector co-processors that have been very useful in the acceleration of several computational intensive applications. PF analysis is the steady-state analysis of AC power networks and is widely used for several tasks involved in system operation and planning. Currently, GPUs are challenged by applications exhibiting an irregular computational pattern, as is the case of most known methods to solve the PF analysis. At the same time, the PF analysis needs to be improved in order to cope with new requirements of efficiency and accuracy coming from the Smart Grid concept. The relevance of GPU-enhanced PF analysis is twofold. On one hand, it expands the application domain of GPUs to a new class of problems. On the other hand, it consistently increases the computational capacity available for power system operation and design. What specific features of GPUs can be used to enhance crucial aspects of PF analysis? What algorithms or models for PF analysis are best suited to the massively parallel architecture of GPUs? The present work attempts to answer such questions in two complementary ways: (i) by developing novel GPU programming strategies for available PF algorithms, and (ii) by proposing novel PF analysis methods that can exploit the numerous features present in GPU architectures. The proposed methods are implemented using state-of-the-art programming frameworks and paradigms, and tested over state-of-the-art GPU and CPU architectures. Results are discussed and compared with existing solutions from the literature of the field, leading to relevant conclusions and guidelines for future research. Specific contributions to GPU computing include: (i) a comparison of two programming paradigms, namely regularity and load-balancing, for implementing the so-called treefix operations; (ii) a study of the impact of the representation format over performance and accuracy, for fuzzy interval algebraic operations; and (iii) the utilization of architecture-specific design, as a novel strategy to improve performance scalability of scientific applications. Contributions to PF analysis include: (i) design and evaluation of a novel method for the uncertainty assessment, based on the fuzzy interval approach; and (ii) the development of an intrinsically parallel method to solve the PF analysis, which is not affected by the Amdahl's law.