Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and System

Secondary control in islanded smart distribution systems considering renewable resources and energy storage: A centralized approach based on convex optimization

This thesis proposes a receding horizon strategy for the secondary control of islanded microgrids. The proposed control takes into account the action of the primary control as well as the references given by the tertiary control. A convex optimization model is solved in each time step, based on a linear approximation of the frequency-dependent power flow equations. The main objective of the control is to carry out frequency and voltages to suitable values, taking into account capacity limits of renewable sources and energy-storage devices..

Power Management for Energy Systems

The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences

Self-Adaptive Methods for PDE

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