Batch Computing Method for Sensitivity Analysis of Large Power Grids Based on GPU Acceleration

Abstract

For a given power system operating condition, sensitivity analysis is needed when it is necessary to analyze how the changes of some variables will affect other variables. With the increasing scale of the power grid, it is computationally expensive to perform sensitivity analysis under various operating conditions, such as changes in generation power and line outages. In order to accelerate sensitivity analysis of large-scale power grids, a batch computing method based on graphics processing unit-central processing unit (GPU-CPU) heterogeneous computing framework is proposed. Considering the coarse-grained parallelism of the calculation under different operating conditions and further mining fine-gain parallelism in calculation is an effective approach to improve computational efficiency. This paper proposes a fine-grained parallel batch calculation method to simultaneously obtain line outage distribution factors and generation shift distribution factors under various operating conditions. Finally, by comparing with the standard example, the effectiveness of the proposed method is verified by case study.