Under the patronage of:

Journal Metrics

Impact factor (2022): 2.1

75th percentile
Powered by  Scopus

More about CiteScore

Source Normalized Impact per Paper (SNIP): 0.474

SCImago Journal Rank (SJR): 0.377


Simulation-based Strategies for Smart Demand Response

Original scientific paper

Journal of Sustainable Development of Energy, Water and Environment Systems
Volume 6, Issue 1, March 2018, pp 33-46
DOI: https://doi.org/10.13044/j.sdewes.d5.0168
Ines Leobner1 , Peter Smolek1, Bernhard Heinzl2, Philipp Raich3, Alexander Schirrer4, Martin Kozek4, Matthias Rössler5, Benjamin Mörzinger6
1 Institute for Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9, Vienna, Austria
2 Institute for Computer Aided Automation, TU Wien, Favoritenstr. 9-11, Vienna, Austria
3 Institute for Computer Aided Automation, TU Wien, Treitlstraße 3, Vienna, Austria
4 Institute for Mechanics and Mechatronics, TU Wien, Getreidemarkt 9, Vienna, Austria
5 dwh GmbH, Simulation Services, Neustiftgasse 57-59, Vienna, Austria
6 Institute for Production Engineering and Laser Technology, TU Wien, Getreidemarkt 9, Vienna, Austria


Demand Response can be seen as one effective way to harmonize demand and supply in order to achieve high self-coverage of energy consumption by means of renewable energy sources. This paper presents two different simulation-based concepts to integrate demand-response strategies into energy management systems in the customer domain of the Smart Grid. The first approach is a Model Predictive Control of the heating and cooling system of a low-energy office building. The second concept aims at industrial Demand Side Management by integrating energy use optimization into industrial automation systems. Both approaches are targeted at day-ahead planning. Furthermore, insights gained into the implications of the concepts onto the design of the model, simulation and optimization will be discussed. While both approaches share a similar architecture, different modelling and simulation approaches were required by the use cases.

Keywords: Smart grids, Demand response, Modelling and simulation, Energy efficiency in industry, Smart buildings, Industrial energy management, Heating and cooling control, Optimization.

Creative Commons License
Views (in 2024): 306 | Downloads (in 2024): 133
Total views: 5187 | Total downloads: 2555