Sarosh N. Talukdar

Distributed model predictive control

The article presents results for distributed model predictive control (MPC), focusing on i) the coordination of the optimization computations using iterative exchange of information and ii) the stability of the closed-loop system when information is exchanged only after each iteration. Current research is focusing on general methods for decomposing large-scale problems for distributed MPC and methods for guaranteeing stability when multiple agents are controlling systems subject to abrupt changes.

Computer-aided dispatch for electric power systems

This paper provides a review of the subject of electric power dispatching. In Section I we show where dispatching fits into the hierarchy of power system operating and planning problems. Section II provides an overview of the issues, concerns and practices of dispatching. Sections III-V are devoted to the algorithmic aspects of dispatching. Finally, in Section VI we highlight the conclusions reached in the proceeding sections and identify a number of research needs.

Characterization of Programmed-Waveform Pulsewidth Modulation

Programmed-waveform pulsewidth modulated (PWM) waveforms, applicable to ac-dc/dc-ac converters, are synthesized and analyzed in terms of several structural parameters. By invoking sensitivity studies and heuristics, optimal PWM structures are identified and contrasted. The results show total harmonic performance as a function of switching levels, waveform types, commutations per cycle, and filter bandpass.

Distributed Model Predictive Control: Synchronous and Asynchronous Computation

Model predictive control (MPC) has become one of the leading technologies to control complex processes, to a great extent, as a result of its flexibility and explicit handling of constraints. Given a dynamic problem (DP), MPC converts DP into a series of static optimization problems, thereby allowing the use of standard optimization techniques to compute the control signals. The reliance of MPC on centralized computations, however, stands as a barrier to its use in the real-time operation of large dynamic networks. To this end, this paper proposes an extension to MPC by decomposing DP into a network of small but coupled subproblems and solving them with a network of asynchronous agents. The net result, after each agent applies MPC to its dynamic subproblem, is a series of sets of static subproblems. Our focus is on the simultaneous solution of these sets of static subproblems. The paper delivers a framework to carry out the decomposition and develops conditions under which the iterative synchronous processes of the agents converge to solutions. Furthermore, it proposes heuristics for asynchronous convergence and reports experimental results from prototypical dynamic networks, demonstrating the effectiveness of the proposed extension.

Trends in the history of large blackouts in the United States

Despite efforts to mitigate blackout risk, the data available from the North American Electric Reliability Council (NERC) for 1984-2006 indicate that the frequency of large blackouts in the United States is not decreasing. This paper describes the data and methods used to come to this conclusion and several other patterns that appear in the data. These patterns have important implications for those who make investment and policy decisions in the electricity industry. Several example calculations show how these patterns can significantly affect the decision-making process.

Distributed Model Predictive Control for the Mitigation of Cascading Failures

Most large blackouts are caused by cascading failures—sequences of equipment outages, one set of outages precipitating another. We study the application of distributed, autonomous agents for shortening such sequences. Each agent controls a single variable—the consumption of a load or the output of a generator. Each agent uses model predictive control and cooperates with its neighbors in making its decisions. Experiments using the IEEE 118 bus test case illustrate the effectiveness of this method.

Advances in Finite Element Techniques for Calculating Cable Resistances and Inductances

In theory, finite element procedures can be applied to arbitrary conductor configurations in order to determine their frequency dependent resistances and inductances to an arbitrarily high degree of accuracy. In practice, limitations are imposed by the shapes of the finite elements used and by the accuracy of the formulae invoked to estimate their inductances. This paper reviews the concepts underlying extant finite element procedures, proposes a more efficient set of element shapes than are used in extant procedures and develops the inductance formulae necessary to implement these shapes. This results in a considerably more capable algorithm.

Autonomous agents and cooperation for the control of cascading failures in electric grids

A power system can be thought of as a stochastic hybrid system: a finite state machine whose states involve continuous variables with uncertain dynamics. Transitions in this machine correspond to outages of generation and transmission equipment. A cascading failure corresponds to a series of such transitions whose net effect is a blackout. We present evidence that the probability of cascading failures is subject to phase transitions - large and abrupt changes that result from only small changes in system stress. We suggest a network of distributed, autonomous agents to reduce the ill effects of cascading failures. These agents improve their decisions by cooperating (sharing goals and exchanging information with their neighbors). Results from experiments on the IEEE 118 bus test case are included.

Quasi-Newton method for optimal power flows

Abstract A prototype procedure for solving the optimal power flow problem with a quasi-Newton (variable metric) method is described. The method was developed by Powell and later extended by Berna, Locke and Westerberg. It is attractive for three reasons. First, it can accommodate optimal power flow constraints in a straightforward manner. Second, it is robust and will home in on a solution even from infeasible starting points. Third, it promises to be very fast. The adaptation of the method to the optimal power flow is discussed and illustrated with the results from tests on two small power systems.

Multiagent organizations for real-time operations

The real-time operations of electric power networks are subject to two sets of forces. The first, including deregulation movements and growing environmental concerns, is acting to increase the complexity of operations. The second, including new computer technologies and emerging knowledge-based agents, provides some means for handling additional complexity. The authors argue that organizational changes will have to be made before the second set of forces can be applied to effectively counter the first. To make this argument, a framework for discussing organizational structures is presented. Then the structures of the two generations of computer-based, multiagent systems that have been developed for operations are reviewed. It is pointed out that these structures are well-suited to the algorithmic tasks involved in operations but not to the knowledge-based tasks. The authors conclude with some suggestions for research into alternative structures. >