M. La Scala

Parallel processing in power systems computation

The availability of parallel processing hardware and software presents an opportunity and a challenge to apply this new computation technology to solve power system problems. The allure of parallel processing is that this technology has the potential to be cost effectively used on computationally intense problems. The objective of this paper is to define the state of the art and identify what the authors see to be the most fertile grounds for future research in parallel processing as applied to power system computation. As always, such projections are risky in a fast changing field, but the authors hope that this paper will be useful to the researchers and practitioners in this growing area.

On-line dynamic preventive control: an algorithm for transient security dispatch

This paper describes the philosophy and the implementation of a preventive control algorithm for application in power system dynamic security assessment. The methodology consists of an optimization procedure where: the objective function takes into account economic costs; inequality constraints confine the trajectory of the system in a practical domain of the state space; and equality constraints derive from the discretization of the differential-algebraic equations of the power system sparse representation. The algorithm has been implemented to reschedule the power system generation in order to guarantee transient stability. The feasibility of the approach is shown through computer simulation tests on a realistic sized test network.

A static optimization approach to assess dynamic available transfer capability

This paper deals with the development of a nonlinear programming methodology for evaluating available transfer capability. The main feature of the approach is the capability to treat static and dynamic security constraints in a unique integrated piece of software. The algorithm has been implemented and tested on an actual power system.

A highly parallel method for transient stability analysis

A simple but powerful method for solving the transient stability problem with a high degree of parallelism is implemented. The transient stability is seen as a coupled set of nonlinear algebraic and differential equations. By applying a discretization method such as the trapezoidal rule, the overall algebraic-differential set of equations is transformed into a unique algebraic problem at each time step. A solution that considers every time step, not in a sequential way, but concurrently, is suggested. The solution of this set of equations with a relaxation-type indirect method gives rise to a highly parallel algorithm. The parallelism consists of a parallelism in space (that is in the equations at each time step) and a parallelism in time. Another characteristic of the algorithm is that the time step can be changed between iterations using a nested iteration multigrid technique from a coarse time grid to the desired fine time grid to enhance the convergence of the algorithm. The method has been tested on various size power systems, for various solution time periods, and various types of disturbances. It is shown that the method has good convergence properties and can significantly increase computational efficiency in a parallel-processing environment.<<ETX>>

A distributed computing approach for real-time transient stability analysis

Power system online dynamic security assessment (DSA) is a challenging computing problem. A key problem in DSA is the analysis of a large number of dynamic stability contingencies every 10-20 minutes using online data. In order to speed up the transient stability analysis, parallel processing has been applied and several results can be found in the literature. In this paper, the authors present a distributed approach for real-time transient stability analysis. Distributed computing is economically attractive providing the processing power of supercomputing at a lower cost. Several distributed software environments like the parallel virtual machine (PVM) allow an effective use of heterogeneous clusters of workstations. Both functional and domain decomposition of the transient stability problem were tested under PVM on a homogeneous cluster of eight DEC ALPHA and on an IBM SP2 machine. Functional decomposition has been obtained by the Shifted-Picard algorithm, whereas domain decomposition has been obtained concurrently running different contingencies on different nodes of the cluster, using the very dishonest Newton algorithm. In order to assess the performance of these approaches, time domain simulations, adopting detailed modeling for synchronous machines, have been carried out on a realistic-sized power network comprising 2583 buses and 511 generators.

A Gauss-Jacobi-Block-Newton method for parallel transient stability analysis (of power systems)

A parallel method for the transient stability simulation of power systems is presented. The trapezoidal rule is used to discretize the set of algebraic-differential equations which describes the transient stability problem. A parallel Block-Newton relaxation technique is used to solve the overall set of algebraic equations concurrently on all the time steps. The parallelism in space of the problem is also exploited. Furthermore, the parallel-in-time formulation is used to change the time steps between iterations by a nested iteration multigrid technique, in order to enhance the convergence of the algorithm. The method has the same reliability and model-handling characteristics of typical dishonest Newton-like procedures. Test results on realistic power systems are presented to show the capability and usefulness of the suggested technique. >

Dynamic Security Corrective Control by UPFCs

This paper deals with the development of a nonlinear programming methodology for evaluating corrective actions to improve the dynamic security of power systems when transient instability is detected. Remedial actions are implemented by exploiting the fast response of unified power flow controllers (UPFCs). The algorithm is implemented and tested on the Italian grid.

Real-time preventive actions for the enhancement of voltage-degraded trajectories

This paper deals with the development of a nonlinear programming methodology for evaluating preventive actions to improve the dynamic security of power systems when poor voltage transients are detected. For the proposed power system modeling, voltage-degraded trajectories arise from both angle and voltage instability on the transient time scale. The rescheduling of the generation improves power system security by ensuring an acceptable voltage transient behavior and constraining the system to be stable. The algorithm is implemented and tested on an actual power system.

A pipelined-in-time parallel algorithm for transient stability analysis (power systems)

A new parallel algorithm for transient stability analysis is presented. An implicit trapezoidal rule is used to discretize the set of algebraic-differential equations which describe the transient stability problem. A parallel-in-time formulation has been adopted. A Newton procedure is used to solve the equations which describe the system at each time step, whereas a Gauss-Seidel algorithm relaxes the solution across the time steps. A Gauss-Seidel-like procedure can be usefully exploited in the parallel processing mode by pipelining the computation through time steps. The parallelism in space of the problem is also exploited. Furthermore, the parallel-in-time formulation is used to change the time steps between iterations by a nested iteration multigrid technique in order to enhance the convergence of the algorithm. The method has the same reliability and model-handling characteristics of typical dishonest Newton-like procedures. Test results on realistic power systems are presented to show the capability and usefulness of the suggested technique. >

Transient security dispatch for the concurrent optimization of plural postulated contingencies

The implementation. of a transient security dispatch (TSD) algorithm for real-time applications is presented. The algorithm, based on the generalized reduced gradient (GRG) method, processes collectively a set of contingencies in order to ensure transient stability for all postulated cases. The massive computation has been decomposed on a cluster of workstations, exploiting the decoupled nature of the GRG method and the benefits of distributed computing. The feasibility of the implementation is shown through tests on a realistic-sized test network.