Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Optimization of Energy Systems for a Sustainable District in Stockholm Using Genetic Algorithms: The case of Albano
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Service and Energy Systems.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Multi-objective optimization tools using genetic algorithms (GAs) are being increasingly used for improving building performances and sustainability. However, few research studies focus on district-scale solutions. In the present project, a multi-objective optimization method using genetic algorithms was applied in order to help decision makers find the optimal energy mix of a district energy system in the preliminary design phase.

 

A case study consisting of the new campus Albano in Stockholm (comprising lecture buildings and student residences) was used for the analysis. A wide range of energy systems was included as a design variable: wind turbines, solar thermal collectors and photovoltaic cells, ground-source heat pumps, biomass boilers, combined cooling, heating and power, district heating and district cooling. The energy provided by the chosen technologies and the district energy balances are simulated on an annual basis using a steady-state method with an hourly resolution.

 

Three objectives functions were to be minimized: (1) the life-cycle costs; (2) the greenhouse gas emissions; and (3) the annual non-renewable primary energy consumption of the district. The optimization process was implemented on MOBO, a multi-objective optimization tool based on genetic algorithms.

 

The findings include understanding the trade-offs among the three objectives and a selection of alternatives of energy supply systems to be further investigated in the detailed design phase.

Place, publisher, year, edition, pages
2014. , 209 p.
Series
TRITA-IES, 2014-07
Keyword [en]
Energy system optimization; Multi-objectives genetic algorithm; sustainable districts; Albano;
National Category
Engineering and Technology Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-142684OAI: oai:DiVA.org:kth-142684DiVA: diva2:704751
Educational program
Master of Science in Engineering - Urban Management
Supervisors
Examiners
Available from: 2014-03-13 Created: 2014-03-12 Last updated: 2014-03-18Bibliographically approved

Open Access in DiVA

fulltext(9906 kB)365 downloads
File information
File name FULLTEXT01.pdfFile size 9906 kBChecksum SHA-512
c90a691b0545aedff3116adc4bdd2a92696c5dde60fbb926ff446cd4c44b0d47430c0f645e8bd35c79f43a2b92e1f8d3020ad3ecce89ad85a63a85972f868a4a
Type fulltextMimetype application/pdf

By organisation
Building Service and Energy Systems
Engineering and TechnologyCivil Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 365 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 571 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf