High Performance Computing for Power System Dynamic Simulation

Abstract

High-speed extended term (HSET) time domain simulation (TDS) is intended to provide very fast computational capability to predict extendedterm dynamic system response to disturbances and identify corrective actions. The extended-term dynamic simulation of a power system is valuable because it provides ability for the rigorous evaluation and analysis of outages which may include cascading. It is important for secure power grid expansion, enhances power system security and reliability, both under normal and abnormal conditions. In this chapter the design of the envisioned future dynamic security assessment processing system (DSAPS) is presented where HSETTDS forms the core module. The power system is mathematically represented by a system of differential and algebraic equations (DAEs). These DAEs arise out of the modeling of the dynamic components such as generators, exciters, governors, automatic generation control, load tap changers, induction motors, network modeling and so on. To provide very fast computational capability within the HSET-TDS, this chapter motivates the need for high performance computing (HPC) for power system dynamic simulations through detailed modeling of power system components and efficient numerical algorithms to solve the resulting DAEs. The developed HSET-TDS is first validated for accuracy against commercial power simulators (PSSE, DSA Tools, Power- World) and then it is compared for computational efficiency. The chapter investigates some of the promising direct sparse linear solver for fast extended term time domain simulation and makes recommendation for the modern power grid computations. The results provide very important insights with regards to the impact of the different numerical linear solver algorithms for enhancing the power system TDS.KeywordsPower SystemInduction MotorHigh Performance ComputingLinear SolverSpeed DeviationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.