Battery Capacity of Deferrable Energy Demand

We investigate the ability of a homogeneous collection of deferrable energy loads to behave as a battery; that is, to absorb and release energy in a controllable fashion up to fixed and predetermined limits on volume, charge rate and discharge rate. We derive bounds on the battery capacity that can be realized and show that there are fundamental trade-offs between battery parameters. By characterizing the state trajectories under scheduling policies that emulate two illustrative batteries, we show that the trade-offs occur because the states that allow the loads to absorb and release energy at high aggregate rates are conflicting

Optimization of Lyapunov invariants in analysis and implementation of safety-critical software systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (leaves 168-176).This dissertation contributes to two major research areas in safety-critical software systems, namely, software analysis, and software implementation. In reference to the software analysis problem, the main contribution of the dissertation is the development of a novel framework, based on Lyapunov invariants and convex optimization, for verification of various safety and performance specifications for software systems. The enabling elements of the framework for software analysis are: (i) dynamical system interpretation and modeling of computer programs, (ii) Lyapunov invariants as behavior certificates for computer programs, and (iii) a computational procedure for finding the Lyapunov invariants. (i) The view in this dissertation is that software defines a rule for iterative modification of the operating memory at discrete instances of time. Hence, it can be modeled as a discrete-time dynamical system with the program variables as the state variables, and the operating memory as the state space. Three specific modeling languages are introduced which can represent a broad range of computer programs of interest to the control community. These are: Mixed Integer-Linear Models, Graph Models, and Linear Models with Conditional Switching. (ii) Inspired by the concept of Lyapunov functions in stability analysis of nonlinear dynamical systems, Lyapunov invariants are introduced and proposed for analysis of behavioral properties, and verification of various safety and performance specifications for computer programs. In the same spirit as standard Lyapunov functions, a Lyapunov invariant is an appropriately defined function of the state which satisfies a difference inequality along the trajectories. It is shown that variations of Lyapunov invariants satisfying certain technical conditions can be formulated for verification of several common specifications.(cont.) These include but are not limited to: absence of overflow, absence of division-by-zero, termination in finite time, and certain user-specified program assertions. (iii) A computational procedure based on convex relaxation techniques and numerical optimization is proposed for finding the Lyapunov invariants that prove the specifications. The framework is complemented by the introduction of a notion of optimality for the graph models. This notion can be used for constructing efficient graph models that improve the analysis in a systematic way. It is observed that the application of the framework to (graph models of) programs that are semantically identical but syntactically different does not produce identical results. This suggests that the success or failure of the method is contingent on the choice of the graph model. Based on this observation, the concepts of graph reduction, irreducible graphs, and minimal and maximal realizations of graph models are introduced. Several new theorems that compare the performance of the original graph model of a computer program and its reduced offsprings are presented. In reference to the software implementation problem for safety-critical systems, the main contribution of the dissertation is the introduction of an algorithm, based on optimization of quadratic Lyapunov functions and semidefinite programming, for computing optimal state space implementations for digital filters. The particular implementation that is considered is a finite word-length implementation on a fixed-point processor with quantization before or after multiplication. The objective is to minimize the effects of finite word-length constraints on performance deviation while respecting the overflow limits. The problem is first formulated as a special case of controller synthesis where the controller has a specific structure, which is known to be a hard non-convex problem in general.(cont.) It is then shown that this special case can be convexified exactly and the optimal implementation can be computed by solving a semidefinite optimization problem. It is observed that the optimal state space implementation of a digital filter on a machine with finite memory, does not necessarily define the same transfer function as that of an ideal implementation.by Mardavij Roozbehani.Ph.D

On the Economic Value and Price-Responsiveness of Ramp-Constrained Storage

The primary concerns of this paper are twofold: to understand the economic value of storage in the presence of ramp constraints and exogenous electricity prices, and to understand the implications of the associated optimal storage management policy on qualitative and quantitative characteristics of storage response to real-time prices. We present an analytic characterization of the optimal policy, along with the associated finite-horizon time-averaged value of storage. We also derive an analytical upperbound on the infinite-horizon time-averaged value of storage. This bound is valid for any achievable realization of prices when the support of the distribution is fixed, and highlights the dependence of the value of storage on ramp constraints and storage capacity. While the value of storage is a non-decreasing function of price volatility, due to the finite ramp rate, the value of storage saturates quickly as the capacity increases, regardless of volatility. To study the implications of the optimal policy, we first present computational experiments that suggest that optimal utilization of storage can, in expectation, induce a considerable amount of price elasticity near the average price, but little or no elasticity far from it. We then present a computational framework for understanding the behavior of storage as a function of price and the amount of stored energy, and for characterization of the buy/sell phase transition region in the price-state plane. Finally, we study the impact of market-based operation of storage on the required reserves, and show that the reserves may need to be expanded to accommodate market-based storage

A Characterization of Lyapunov Inequalities for Stability of Switched Systems

We study stability criteria for discrete-time switched systems and provide a meta-theorem that characterizes all Lyapunov theorems of a certain canonical type. For this purpose, we investigate the structure of sets of LMIs that provide a sufficient condition for stability. Various such conditions have been proposed in the literature in the past fifteen years. We prove in this note that a family of languagetheoretic conditions recently provided by the authors encapsulates all the possible LMI conditions, thus putting a conclusion to this research effort. As a corollary, we show that it is PSPACE-complete to recognize whether a particular set of LMIs implies stability of a switched system. Finally, we provide a geometric interpretation of these conditions, in terms of existence of an invariant set.Comment: arXiv admin note: text overlap with arXiv:1201.322

Resilience of Locally Routed Network Flows: More Capacity is Not Always Better

In this paper, we are concerned with the resilience of locally routed network flows with finite link capacities. In this setting, an external inflow is injected to the so-called origin nodes. The total inflow arriving at each node is routed locally such that none of the outgoing links are overloaded unless the node receives an inflow greater than its total outgoing capacity. A link irreversibly fails if it is overloaded or if there is no operational link in its immediate downstream to carry its flow. For such systems, resilience is defined as the minimum amount of reduction in the link capacities that would result in the failure of all the outgoing links of an origin node. We show that such networks do not necessarily become more resilient as additional capacity is built in the network. Moreover, when the external inflow does not exceed the network capacity, selective reductions of capacity at certain links can actually help averting the cascading failures, without requiring any change in the local routing policies. This is an attractive feature as it is often easier in practice to reduce the available capacity of some critical links than to add physical capacity or to alter routing policies, e.g., when such policies are determined by social behavior, as in the case of road traffic networks. The results can thus be used for real-time monitoring of distance-to-failure in such networks and devising a feasible course of actions to avert systemic failures.Comment: Accepted to the IEEE Conference on Decision and Control (CDC), 201

Lower bounds on the performance of Analog to Digital Converters

This paper deals with the task of finding certified lower bounds for the performance of Analog to Digital Converters (ADCs). A general ADC is modeled as a causal, discrete-time dynamical system with outputs taking values in a finite set. We define the performance of an ADC as the worst-case average intensity of the filtered input matching error. The input matching error is the difference between the input and output of the ADC. This error signal is filtered using a shaping filter, the passband of which determines the frequency region of interest for minimizing the error. The problem of finding a lower bound for the performance of an ADC is formulated as a dynamic game problem in which the input signal to the ADC plays against the output of the ADC. Furthermore, the performance measure must be optimized in the presence of quantized disturbances (output of the ADC) that can exceed the control variable (input of the ADC) in magnitude. We characterize the optimal solution in terms of a Bellman-type inequality. A numerical approach is presented to compute the value function in parallel with the feedback law for generating the worst case input signal. The specific structure of the problem is used to prove certain properties of the value function that allow for iterative computation of a certified solution to the Bellman inequality. The solution provides a certified lower bound on the performance of any ADC with respect to the selected performance criteria.United States. Army Research Office. Efficient Linearized All-Silicon Transmitter IC