Dynamic control of intermittent renewable energy fluctuations in two-layer power grids

TL;DR

This paper models two-layer power grids with a communication network to control and stabilize frequency fluctuations caused by intermittent renewable energy sources, demonstrating effective control strategies in simulations.

Contribution

It introduces a two-layer network model combining power grid dynamics with a communication layer for control, tested on the Italian high voltage grid, with novel control schemes for renewable fluctuation mitigation.

Findings

All-to-all communication topology yields high control efficiency.

Local control schemes are more effective with limited communication links.

Control strategies significantly improve frequency synchronization under stochastic renewable output.

Abstract

In this work we model the dynamics of power grids in terms of a two-layer network, and use the Italian high voltage power grid as a proof-of-principle example. The first layer in our model represents the power grid consisting of generators and consumers, while the second layer represents a dynamic communication network that serves as a controller of the first layer. The dynamics of the power grid is modelled by the Kuramoto model with inertia, while the communication layer provides a control signal P_i^c for each generator to improve frequency synchronization within the power grid. We propose different realizations of the communication layer topology and of the control signal, and test the control performances in presence of generators with stochastic power output. When using a control topology that allows all generators to exchange information, we find that a control scheme aimed to minimize the frequency difference between adjacent nodes operates very efficiently even against the worst scenarios with the strongest perturbations. On the other hand, for a control topology where the generators possess the same communication links as in the power grid layer, a control scheme aimed at restoring the synchronization frequency in the neighborhood of the controlled node turns out to be more efficient.