Two applications of parallel processing in power system computation

This paper discusses performance improvements achieved in two power system software modules through the use of parallel processing techniques. The first software module, EVARISTE, outputs a voltage stability indicator for various power system situations. This module was designed for extended real-time use and is therefore required to give guaranteed response times. The second module, MEXICO, assesses power system reliability and operating costs by simulating a large number of contingencies for generation and transmission equipment. This module, used for power system planning purposes, uses a Monte-Carlo method to build the various system states, and makes heavy demands on CPU time for running simulations. Like many power system computation packages, both software modules are well-suited to coarse-grain parallel processing. The first module was parallelized on a distributed-memory machine and the second on a shared-memory machine. In this paper, we start by giving a description of the parallelization process used in these two cases, then go on to give details on the performance levels achieved, discussing aspects of programming, parameter selection (number of situations processed, number of processors), and machine characteristics (limitations due to interprocessor communications network, for instance)