UBC Theses and Dissertations
Parallel computation of large power system networks using the multi-area Thévenin equivalents Tomim, Marcelo Aroca
The requirements of today's power systems are much different from the ones of the systems of the past. Among others, energy market deregulation, proliferation of independent power producers, unusual power transfers, increased complexity and sensitivity of the equipments demand from power systems operators and planners a thorough understanding of the dynamic behaviour of such systems in order to ensure a stable and reliable energy supply. In this context, on-line Dynamic Security Assessment (DSA) plays a fundamental role in helping operators to predict the security level of future operating conditions that the system may undergo. Amongst the tools that compound DSA is the Transient Stability Assessment (TSA) tools, which aim at determining the dynamic stability margins of present and future operating conditions. The systems employed in on-line TSA, however, are very much simplified versions of the actual systems, due to the time-consuming transient stability simulations that are still at the heart of TSA applications. Therefore, there is an increasing need for improved TSA software, which has the capability of simulating bigger and more complex systems in a shorter lapse of time. In order to achieve such a goal, a reformulation of the Multi-Area Thévenin Equivalents (MATE) algorithm is proposed. The intent of such an algorithm is parallelizing the solution of the large sparse linear systems associated with transient stability simulations and, therefore, speeding up the overall on-line TSA cycle. As part of the developed work, the matrix-based MATE algorithm was re-evaluated from an electric network standpoint, which yielded the network-based MATE presently introduced. In addition, a performance model of the proposed algorithm is developed, from which a theoretical speedup limit of p/2 was deduced, where p is the number of subsystems into which a system is torn apart. Applications of the network-based MATE algorithm onto solving actual power systems (about 2,000 and 15,000 buses) showed the attained speedup to closely follow the predictions made with the formulated performance model, even on a commodity cluster built out of inexpensive out-of-the-shelf computers.
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