A grid computing based approach for the power system dynamic security assessment

This paper addresses the problem of parallel dynamic security assessment applications from static homogeneous cluster environment to dynamic heterogeneous grid environment. Functional parallelism and data parallelism are supported by each of the message passing interface model and TCP/IP model. To consider the differences in heterogeneous computing resources and complexity of large-scale power system communities, a kernel-based multilevel algorithm is proposed for network partitioning. Since the bottleneck in distributed computation is low speed network communication, a bi-level latency exploitation technique is introduced for numerically solving system differential equations. The proposed grid-based implementation includes the core simulation engine, grid computing middleware, a Python interface and Python front-end utilities. Tests for a 39-bus network, a 4000-bus network and a 10,000-bus network are reported, and the results of these experiments demonstrate that the proposed scheme is able to execute the distributed simulations on computational grid infrastructure and provide efficient parallelism.