Multi-objective model predictive control for microgrids-Reference-Cited by-同舟云学术

Multi-objective model predictive control for microgrids

Published:2020-07-31 Issue:8 Volume:68 Page:687-702
ISSN:2196-677X
Container-title:at - Automatisierungstechnik
language:en
Short-container-title:

Author:

Schmitt Thomas¹,Rodemann Tobias²,Adamy Jürgen¹

Affiliation:

1. Control Methods and Robotics Laboratory , Technical University of Darmstadt , Landgraf-Georg Straße 4 , Darmstadt , Germany

2. Honda Research Institute Europe GmbH , Carl-Legien-Straße 30 , Offenbach am Main , Germany

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

https://www.degruyter.com/document/doi/10.1515/auto-2020-0031/pdf

Reference41 articles.

1. Fabrizio Ascione, Nicola Bianco, Claudio De Stasio, Gerardo Maria Mauro and Giuseppe Peter Vanoli. Casa, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy and Buildings, 146:200–219, 2017.

2. Timothy Ward Athan and Panos Y Papalambros. A note on weighted criteria methods for compromise solutions in multi-objective optimization. Engineering optimization, 27(2):155–176, 1996.

3. Julián Barreiro-Gomez, Carlos Ocampo-Martinez and Nicanor Quijano. Evolutionary game-based dynamical tuning for multi-objective model predictive control. In Developments in model-based optimization and control, pages 115–138. Springer, 2015.

4. Alberto Bemporad and David Muñoz de la Peña. Multiobjective model predictive control. Automatica, 45(12):2823–2830, 2009.

5. Johannes Bisschop. AIMMS optimization modeling. Lulu. com, 2006.

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