A Scheme for Creating an Small-Signal On-line Dynamic Security Assessment Tool - Using PSS/E and PacDyn

Abstract

The trend in today's electrical power system is that consumption and power peaks are increasing, which will push the grid to its limits. The large deployment of new renewable energy connected to the grid has also shifted the operational scene from only a few large power plants towards a multitude of medium and small generators, changing the natural flow of the power. The power flow is becoming less predictable, and more dynamic approaches for defining the limits are needed.

All synchronous machines that are connected to a steady-state power grid is working in synchronism, where all machines have found their power equilibrium point. The difference between the rotor angle voltage and the infinite bus are defining the power output of the generators. When machines that are connected through the grid experience changes such as changed consumption demand or other small disturbances, the equilibrium point is perturbed. As a result all the generators need to find back to a new equilibrium point, and while this occur, some power oscillations could arise between the machines. For security reasons the oscillations should damp out fast enough to maintain security in the grid.

By monitoring the eigenvalues of the system the damping ratio could be measured, and through this master thesis a on-line dynamic security assessment tool is created in Python. The tool is collecting (almost) real-time measurements from NordPoolSpot, updating a system model, load flow simulation is perform in PSS/E, small signal analysis is perform using Dominant Pole Spectrum Eigensolver in PacDyn and the security state of the system is presented to the operator. If the power system should move towards insecurity, alerts will arise, and the operator could remedy the threads. The results shows that the damping of power oscillation between Norway and Finland is worst during the winter, when the consumption is high. The scheme for assessing small-signal stability proved to be working, being able to track multiple eigenvalues and perform contingencies analysis and recommend remedy actions.