Iterative learning control in prosumer-based microgrids with hierarchical control

authored by

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.

External Organisation(s)

Technische Universität Berlin
Potsdam Institute for Climate Impact Research

Type

Conference article

Journal

IFAC-PapersOnLine

Volume

53

Pages

12251-12258

No. of pages

8

ISSN

2405-8963

Publication date

2020

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Control and Systems Engineering

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.1016/j.ifacol.2020.12.1145 (Access: Open)