Iterative learning control in prosumer-based microgrids with hierarchical control

verfasst von

Lia Strenge, Xiaohan Jing, Ruth Boersma, Paul Schultz, Frank Hellmann, Jürgen Kurths, Jörg Raisch, Thomas Seel

Abstract

Power systems are subject to fundamental changes due to the increasing infeed of renewable energy sources. Taking the accompanying decentralization of power generation into account, the concept of prosumer-based microgrids gives the opportunity to rethink structuring and operation of power systems from scratch. In a prosumer-based microgrid, each power grid node can feed energy into the grid and draw energy from the grid. The concept allows for spatial aggregation such that also an interaction between microgrids can be represented as a prosumer-based microgrid. The contribution of this work is threefold: (i) we propose a decentralized hierarchical control approach in a network including different time scales, (ii) we use iterative learning control to compensate periodic demand patterns and save lower-layer control energy and (iii) we assure asymptotic stability and monotonic convergence in the iteration domain for the linearized dynamics and validate the performance by simulating the nonlinear dynamics.

Externe Organisation(en)

Technische Universität Berlin
Potsdam-Institut für Klimafolgenforschung (PIK)

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

IFAC-PapersOnLine

Band

53

Seiten

12251-12258

Anzahl der Seiten

8

ISSN

2405-8963

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2020.12.1145 (Zugang: Offen)