Speaker
Description
Power grids are large complex networks whose dynamics, stability and vulnerability are intensively studied; new challenges arise with the increase of distributed renewable energy resources.
The dynamics of electrical grids is highly affected by desynchronization between nodes, which can start an avalanche-like cascade of line failures causing massive outages.
Modelling power systems in detail leads to an increased computational cost, as a much larger number of nodes (in the order of millions) needs to be dealt with than in the traditional power grid models.
The Kuramoto model is a set of coupled nonlinear ordinary differential equations, that describes the power grid as an ensemble of coupled oscillators, and is widely used for investigating the synchronization properties of networks.
The modelling of the power grid by the Kuramoto model consists in the solution of a system of such equations where each equation corresponds to a node in the power grid leading to a solution of a number of equations by the millions.
To be able to efficiently handle the model, we numerically solved the second order Kuramoto equations on a GPU, and simulated cascades as threshold line failures.
In this talk, we present our solution, where a special memory layout for the network graph has been introduced for effective implementation. We studied different numerical solvers supplied by boost’s odeint library, which we compared in terms of precision and performance.
