https://doi.org/10.1140/epjst/e2015-50136-y
Regular Article
Taming instabilities in power grid networks by decentralized control
1 Network Dynamics, Max-Planck-Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen, Germany
2 Potsdam Institute for Climate Impact Research, 14412 Potsdam, Germany
3 Department of Physics, Humboldt University Berlin, 12489 Berlin, Germany
4 Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3FX, UK
5 Department of Control Theory, Nizhny Novgorod State University, 606950 Nizhny Novgorod, Russia
6 Forschungszentrum Jülich, Institute for Energy and Climate Research (IEK-Systems Analysis and Technology Evaluation), 52428 Jülich, Germany
7 Institute for Theoretical Physics, University of Cologne, 50937, Köln, Germany
8 Institute for Nonlinear Dynamics, Faculty of Physics, University of Göttingen, 37077 Göttingen, Germany
9 Department of Physics, Technical University of Darmstadt, 64289 Darmstadt, Germany
10 Department of Mechanical and Aerospace Engineering, New York University Tandon, School of Engineering, Brooklyn, New York 11201, USA
Received: 17 June 2015
Revised: 5 February 2016
Published online: 25 May 2016
Renewables will soon dominate energy production in our electric power system. And yet, how to integrate renewable energy into the grid and the market is still a subject of major debate. Decentral Smart Grid Control (DSGC) was recently proposed as a robust and decentralized approach to balance supply and demand and to guarantee a grid operation that is both economically and dynamically feasible. Here, we analyze the impact of network topology by assessing the stability of essential network motifs using both linear stability analysis and basin volume for delay systems. Our results indicate that if frequency measurements are averaged over sufficiently large time intervals, DSGC enhances the stability of extended power grid systems. We further investigate whether DSGC supports centralized and/or decentralized power production and find it to be applicable to both. However, our results on cycle-like systems suggest that DSGC favors systems with decentralized production. Here, lower line capacities and lower averaging times are required compared to those with centralized production.
© EDP Sciences, Springer-Verlag, 2016
