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Spatial optimization of residential urban district - Energy and water perspectives
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-1351-9245
Georgia Institute of Technology, USA.
ABB AB, Västerås, Sweden.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH Royal Institute of Technology, Sweden.
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2016 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 88, 38-43 p.Article in journal (Refereed) Published
Abstract [en]

Many cities around the world have reached a critical situation when it comes to energy and water supply, threatening the urban sustainable development. The aim of this paper is to develop a spatial optimization model for the planning of residential urban districts with special consideration of renewables and water harvesting integration. In particular, the paper analyses the optimal configuration of built environment area, PV area, wind turbines number and relative occupation area, battery and water harvester storage capacities, as a function of electricity and water prices. The optimization model is multi-objective which uses a genetic algorithm to minimize the system life cycle costs, and maximize renewables and water harvesting reliability. The developed model can be used for spatial optimization design of new urban districts. It can also be employed for analyzing the performances of existing urban districts under an energy-water-economic viewpoint. Assuming a built environment area equal to 75% of the total available area, the results show that the reliability of the renewables and water harvesting system cannot exceed the 6475 and 2500 hours/year, respectively. The life cycle costs of integrating renewables and water harvesting into residential districts are mainly sensitive to the battery system specific costs since most of the highest renewables reliabilities are guaranteed through the energy storage system.

Place, publisher, year, edition, pages
2016. Vol. 88, 38-43 p.
Keyword [en]
Genetic algorithm, Hybrid power systems, Optimization, Renewable energy, Residential urban districts, Water harvesting, Costs, Electric batteries, Electric energy storage, Genetic algorithms, Harvesting, Housing, Life cycle, Reliability, Renewable energy resources, Runoff, Site selection, Sustainable development, Water conservation, Water supply, Wind turbines, Zoning, Energy storage systems, Renewable energies, Residential districts, Spatial optimization model, Urban sustainable development
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-34624DOI: 10.1016/j.egypro.2016.06.011ScopusID: 2-s2.0-85007574574OAI: oai:DiVA.org:mdh-34624DiVA: diva2:1065089
Conference
Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015, 15 November 2015 through 17 November 2015
Available from: 2017-01-13 Created: 2017-01-13 Last updated: 2017-01-13Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
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  • Other locale
More languages
Output format
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  • asciidoc
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