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Optimal sizing of grid-connected rooftop photovoltaic and battery energy storage for houses with electric vehicle

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Titel: Optimal sizing of grid-connected rooftop photovoltaic and battery energy storage for houses with electric vehicle
Autoren: Merrington, Sarah, Khezri, Rahmatollah, 1989, Mahmoudi, Amin
Quelle: IET Smart Grid. 6(3):297-311
Schlagwörter: rooftop solar photovoltaic, building management systems, particle swarm optimisation, battery storage plants, power system planning, optimisation, electric vehicle charging
Beschreibung: A practical optimal sizing model is developed for grid-connected rooftop solar photovoltaic (PV) and battery energy storage (BES) of homes with electric vehicle (EV) to minimise the net present cost of electricity. Two system configurations, (1) PV-EV and (2) PV-BES-EV, are investigated for optimal sizing of PV and BES by creating new rule-based home energy management systems. The uncertainties of EV availability (arrival and departure times) and its initial state of charge, when arrives home, are incorporated using stochastic functions. The effect of popular EV models in the market is investigated on the optimal sizing and electricity cost of the customers. Several sensitivity analyses are adopted based on variations in the grid constrains, retail price and feed in tariff. Uncertainty analysis is provided based on the variations of insolation, temperature, and load to approve the optimal results of the developed model. A practical guideline is presented for residential customers in a typical grid-connected household to select the optimal capacity of PV or PV-BES system considering the model of EV. While the proposed optimization model is general and can be used for various case studies, real annual data of solar insolation, temperature, household's load, electricity prices, as well as PV and BES market data are used for an Australian case study. The developed optimal sizing model is also applied to residential households in different Australian States.
Dateibeschreibung: electronic
Zugangs-URL: https://research.chalmers.se/publication/534239
https://research.chalmers.se/publication/534239/file/534239_Fulltext.pdf
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optimal sizing of grid-connected rooftop photovoltaic and battery energy storage for houses with electric vehicle
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Merrington%2C+Sarah%22">Merrington, Sarah</searchLink><br /><searchLink fieldCode="AR" term="%22Khezri%2C+Rahmatollah%22">Khezri, Rahmatollah</searchLink>, 1989<br /><searchLink fieldCode="AR" term="%22Mahmoudi%2C+Amin%22">Mahmoudi, Amin</searchLink>
– Name: TitleSource
  Label: Source
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  Data: <i>IET Smart Grid</i>. 6(3):297-311
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22rooftop+solar+photovoltaic%22">rooftop solar photovoltaic</searchLink><br /><searchLink fieldCode="DE" term="%22building+management+systems%22">building management systems</searchLink><br /><searchLink fieldCode="DE" term="%22particle+swarm+optimisation%22">particle swarm optimisation</searchLink><br /><searchLink fieldCode="DE" term="%22battery+storage+plants%22">battery storage plants</searchLink><br /><searchLink fieldCode="DE" term="%22power+system+planning%22">power system planning</searchLink><br /><searchLink fieldCode="DE" term="%22optimisation%22">optimisation</searchLink><br /><searchLink fieldCode="DE" term="%22electric+vehicle+charging%22">electric vehicle charging</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: A practical optimal sizing model is developed for grid-connected rooftop solar photovoltaic (PV) and battery energy storage (BES) of homes with electric vehicle (EV) to minimise the net present cost of electricity. Two system configurations, (1) PV-EV and (2) PV-BES-EV, are investigated for optimal sizing of PV and BES by creating new rule-based home energy management systems. The uncertainties of EV availability (arrival and departure times) and its initial state of charge, when arrives home, are incorporated using stochastic functions. The effect of popular EV models in the market is investigated on the optimal sizing and electricity cost of the customers. Several sensitivity analyses are adopted based on variations in the grid constrains, retail price and feed in tariff. Uncertainty analysis is provided based on the variations of insolation, temperature, and load to approve the optimal results of the developed model. A practical guideline is presented for residential customers in a typical grid-connected household to select the optimal capacity of PV or PV-BES system considering the model of EV. While the proposed optimization model is general and can be used for various case studies, real annual data of solar insolation, temperature, household's load, electricity prices, as well as PV and BES market data are used for an Australian case study. The developed optimal sizing model is also applied to residential households in different Australian States.
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  Data: electronic
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1049/stg2.12099
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 297
    Subjects:
      – SubjectFull: rooftop solar photovoltaic
        Type: general
      – SubjectFull: building management systems
        Type: general
      – SubjectFull: particle swarm optimisation
        Type: general
      – SubjectFull: battery storage plants
        Type: general
      – SubjectFull: power system planning
        Type: general
      – SubjectFull: optimisation
        Type: general
      – SubjectFull: electric vehicle charging
        Type: general
    Titles:
      – TitleFull: Optimal sizing of grid-connected rooftop photovoltaic and battery energy storage for houses with electric vehicle
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          Name:
            NameFull: Merrington, Sarah
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          Name:
            NameFull: Khezri, Rahmatollah
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            NameFull: Mahmoudi, Amin
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
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