Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design

Md Shahriar Mahbub, Marco Cozzini, Poul Alberg Østergaard and Fabrizio Alberti

Applied Energy, 2016, vol. 164, issue C, 140-151

Abstract: Environmental and security concerns urge energy planners to design more sustainable energy systems, reducing fossil fuel consumptions in favour of renewable solutions. The proposed scenarios typically rely on a mixing of different energy sources, thereby mitigating the availability and intermittency problems typically related to renewable technologies. Optimizing this combination is of crucial importance to cope with economic, technical, and environmental issues, which typically give rise to multiple contradictory objectives. To this purpose, this article presents a generalized framework coupling EnergyPLAN – a descriptive analytical model for medium/large-scale energy systems – with a multi-objective evolutionary algorithm – a type of optimizer widely used in the context of complex problems. By using this framework, it is possible to automatically identify a set of Pareto-optimal configurations with respect to different competing objectives. As an example, the method is applied to the case of Aalborg municipality, Denmark, by choosing cost and carbon emission minimization as contrasting goals. Results are compared with a manually identified scenario, taken from previous literature. The automatic approach, while confirming that the available manual solution is very close to optimality, yields an entire set of additional optimal solutions, showing its effectiveness in the simultaneous analysis of a wide range of combinations.

Keywords: Energy systems optimization; Multi-objective evolutionary algorithm; EnergyPLAN modelling; Automatic energy scenario design (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261915014920
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:164:y:2016:i:c:p:140-151

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2015.11.042

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().