A multiplex, multi-timescale model approach for economic and frequency control in power grids.

Area of Science:

• Electrical Engineering
• Complex Systems
• Network Science

Background:

• Power systems face fundamental changes from decentralized renewable energy sources and storage.
• The integration of these new actors necessitates novel grid structuring and control strategies across multiple timescales.

Purpose of the Study:

• To introduce a multiplex dynamical network model that integrates decentralized low-level control with a smart grid layer for comprehensive power system management.
• To investigate the interaction between self-organized control and aggregated information for grid stability and economic dispatch.

Main Methods:

• Development of a multiplex dynamical network model encompassing control timescales from seconds to days.
• Implementation of a decentralized, self-organized low-level control for frequency regulation.
• Integration of a smart grid layer for demand matching dispatch and information aggregation.

Main Results:

• The study found that adding communication via aggregation did not enhance system performance in the considered scenarios.
• The self-organized state of the power grid inherently contains sufficient information to learn the demand structure across the entire grid.
• The model demonstrates flexibility for various future power grid scenarios, particularly low-energy microgrids.

Conclusions:

• Self-organized control in power grids can be as effective as aggregated communication for learning demand structures.
• The proposed multiplex network model provides a robust framework for analyzing future power grid operations with high renewable penetration.
• Future research should explore the scalability and robustness of self-organized control in diverse microgrid architectures.