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Equation-based modelling for dynamic optimization of district scale energy systems- A scalability study

  • Gerald Schweiger
  • , Filip Jorissen
  • , Hakan Runvik
  • , Lieve Helsen

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

The optimal operation of energy systems is essential for future sustainable energy systems. While previous studies have shown the potential for equation-based modelling languages for the dynamic optimization of various systems, there is a lack of scalability studies. We investigated the scalability of two different Modelica-based tools for dynamic optimization using two use cases. The first case represents a dynamic optimization of the heating system of a multi-zone building. The second case represents a dynamic optimization of a small-scale district heating system. We show that linear scaling is possible for the tested range.

Original languageEnglish
Title of host publicationSEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies
PublisherIEEE
ISBN (Electronic)9781728111568
DOIs
Publication statusPublished - 1 Sept 2019
Event2nd International Conference on Smart Energy Systems and Technologies: SEST 2019 - Porto, Portugal
Duration: 9 Sept 201911 Sept 2019

Publication series

NameSEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies

Conference

Conference2nd International Conference on Smart Energy Systems and Technologies
Country/TerritoryPortugal
CityPorto
Period9/09/1911/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • district energy system
  • dynamic optimization
  • equation-based modelling
  • scalability

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

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