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Multi-objective model predictive control for microgrids

  • Thomas Schmitt

    M. Sc. Thomas Schmitt is a PhD-student at the Control Methods and Robotics Laboratory, Technical University of Darmstadt, with his focus on energy management of micgrogrids with model predictive control. He studied mechatronics at the Technical University of Darmstadt and received his B. Sc. in 2014 and his M. Sc. in 2017.

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    , Tobias Rodemann

    Dr. rer. nat. Tobias Rodemann is a principal scientist at the Honda Research Institute Europe working in the fields of energy management, many-objective optimization, and multi-criteria decision making. He studied physics and received a diploma in Neuroinformatics from the Ruhr Universität Bochum in 1998. After joining Honda he got a PhD from the Technical University Bielefeld in Computational Neuroscience in 2003.

    und Jürgen Adamy

    Prof. Dr.-Ing. Jürgen Adamy is head of the Control Methods and Robotics Laboratory of the Technical University of Darmstadt. Main fields of activity: Control theory, computational intelligence, autonomous mobile robots.

Veröffentlicht/Copyright: 31. Juli 2020

Abstract

Economic model predictive control is applied to a simplified linear microgrid model. Monetary costs and thermal comfort are simultaneously optimized by using Pareto optimal solutions in every time step. The effects of different metrics and normalization schemes for selecting knee points from the Pareto front are investigated. For German industry pricing with nonlinear peak costs, a linear programming trick is applied to reformulate the optimization problem. Thus, together with an efficient weight determination scheme, the Pareto front for a horizon of 48 steps is determined in less than 4 s.

Zusammenfassung

Ökonomische modellprädiktive Regelung wird auf ein vereinfachtes lineares Modell eines Mikrogrids angewandt. Dazu wird in jedem Zeitschritt die Pareto-Front zu den beiden Gütekriterien monetäre Kosten und thermischer Komfort erzeugt. Von den Pareto-Fronten werden für verschiedene Metriken und Normalisierungen Kniepunkte ausgewählt und deren Effekte auf die Regelung analysiert. Das Optimierungsproblem mit nichtlinearen Peak-Kosten bei der Bepreisung für deutschen Industriestrom wird durch eine Relaxation in ein quadratisches Programm umformuliert. Zusammen mit einem effizienten Algorithmus zur Adaptierung der Gewichte kann die Pareto-Front für einen Horizont von 48 Zeitschritten damit in weniger als 4 s erzeugt werden.

Funding statement: Thomas Schmitt acknowledges the financial support from Honda Research Institute Europe.

About the authors

M. Sc. Thomas Schmitt

M. Sc. Thomas Schmitt is a PhD-student at the Control Methods and Robotics Laboratory, Technical University of Darmstadt, with his focus on energy management of micgrogrids with model predictive control. He studied mechatronics at the Technical University of Darmstadt and received his B. Sc. in 2014 and his M. Sc. in 2017.

Dr. rer. nat. Tobias Rodemann

Dr. rer. nat. Tobias Rodemann is a principal scientist at the Honda Research Institute Europe working in the fields of energy management, many-objective optimization, and multi-criteria decision making. He studied physics and received a diploma in Neuroinformatics from the Ruhr Universität Bochum in 1998. After joining Honda he got a PhD from the Technical University Bielefeld in Computational Neuroscience in 2003.

Prof. Dr.-Ing. Jürgen Adamy

Prof. Dr.-Ing. Jürgen Adamy is head of the Control Methods and Robotics Laboratory of the Technical University of Darmstadt. Main fields of activity: Control theory, computational intelligence, autonomous mobile robots.

Appendix

Numerical results for all simulation settings. Jmon and Jcomf describe the (unweighted) monetary and comfort costs for the entire year, respectively.

Table 3

2018 results for the intraday scenario.

Jmon in €Jcomf
wmon=1, wcomf098341.662211587.11
wmon=wmon=0.5209709.194932.82
wmon=0.14, wcomf=0.86216148.36438.71
CUP (dynamic)215593.361281.63
CUP (fixed)214209.201110.64
ATN (dynamic)215480.221678.42
ATN (fixed)213606.311869.77
AEP (dynamic)217274.28881.78
AEP (fixed)213570.401741.81
wmon0, wcomf=1226940.7635.16

Table 4

2018 results for the industry scenario.

Jmon in €Jcomf
wmon=1, wcomf0297114.091031189.72
wmon=wmon=0.5347226.945028.78
wmon=0.26, wcomf=0.74351185.201153.58
CUP (dynamic)354854.43975.78
CUP (fixed)350899.901325.99
ATN (dynamic)353513.581667.77
ATN (fixed)349152.453463.39
AEP (dynamic)354785.96948.41
AEP (fixed)349662.534175.14
wmon0, wcomf=1377954.8735.24

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Received: 2020-02-28
Accepted: 2020-06-19
Published Online: 2020-07-31
Published in Print: 2020-08-27

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 16.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/auto-2020-0031/pdf
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