A High-Performance Hybrid Computing Approach to Massive Contingency Analysis in the Power Grid
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
Proceedings of the third ACM SIGPLAN X10 Workshop
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Modern power grids are continuously monitored by trained system operators equipped with sophisticated monitoring and control systems. Despite such precautionary measures, large blackouts, that affect more than a million consumers, occur quite frequently. To prevent such blackouts, it is important to perform high-order contingency analysis in real time. However, contingency analysis is computationally very expensive as many different combinations of power system component failures must be analyzed. Analyzing several million such possible combinations can take inordinately long time and it is not be possible for conventional systems to predict blackouts in time to take necessary corrective actions. To address this issue, we present a scalable parallel implementation of a probabilistic contingency analysis scheme that processes only most severe and most probable contingencies. We evaluate our implementation by analyzing benchmark IEEE 300 bus and 118 bus test grids. We perform contingency analysis up to level eight (contingency chains of length eight) and can correctly predict blackouts in real time to a high degree of accuracy. To the best of our knowledge, this is the first implementation of real time contingency analysis beyond level two.