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Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: A case study in Sweden
KTH Royal Institute of Technology, Stockholm, Sweden.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH Royal Institute of Technology, Stockholm, Sweden.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-1351-9245
KTH Royal Institute of Technology, Stockholm, Sweden.
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2017 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 133, 249-263 p.Article in journal (Refereed) Published
Abstract [en]

The optimal components design for grid-connected photovoltaic-battery systems should be determined with consideration of system operation. This study proposes a method to simultaneously optimize the battery capacity and rule-based operation strategy. The investigated photovoltaic-battery system is modeled using single diode photovoltaic model and Improved Shepherd battery model. Three rule-based operation strategies—including the conventional operation strategy, the dynamic price load shifting strategy, and the hybrid operation strategy—are designed and evaluated. The rule-based operation strategies introduce different operation parameters to run the system operation. multi-objective Genetic Algorithm is employed to optimize the decisional variables, including battery capacity and operation parameters, towards maximizing the system's Self Sufficiency Ratio and Net Present Value. The results indicate that employing battery with the conventional operation strategy is not profitable, although it increases Self Sufficiency Ratio. The dynamic price load shifting strategy has similar performance with the conventional operation strategy because the electricity price variation is not large enough. The proposed hybrid operation strategy outperforms other investigated strategies. When the battery capacity is lower than 72 kW h, Self Sufficiency Ratio and Net Present Value increase simultaneously with the battery capacity.

Place, publisher, year, edition, pages
2017. Vol. 133, 249-263 p.
Keyword [en]
Battery, Genetic algorithm, Operation strategy, Optimization, Photovoltaic
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-34589DOI: 10.1016/j.enconman.2016.11.060ISI: 000392678900022ScopusID: 2-s2.0-85006791741OAI: oai:DiVA.org:mdh-34589DiVA: diva2:1062307
Available from: 2017-01-05 Created: 2017-01-05 Last updated: 2017-02-23Bibliographically 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
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf