Predicting variability of high-penetration photovoltaic systems in a community microgrid by analyzing high-temporal rate data

Mohammad B. Shadmand, Robert Balog, Melanie D. Johnson

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Interest in renewable energy sources continues to gain popularity. However, a major fundamental limitation exists that prevents widespread adoption: variability of electricity generated. Distributed generation (DG) grid-tied photovoltaic (PV) systems with centralized battery back-up can mitigate the variability of PV systems and be optimized to reduce cost by analyzing high-temporal rate data. Thus, it is an attractive system to meet 'go green' mandates, while also providing reliable electricity. The focus of this paper is to analyze the variability of a high-penetration PV scenario when incorporated into the microgrid concept. The proposed system design approach is based on high-temporal rate instead of the more commonly used hourly data rate. The methodology presented in this paper employs a technoeconomic approach to determine the optimal system design to guarantee reliable electricity supply with lowest investment. The proposed methodology is used to demonstrate that the variability of the PV resource can be quantified by determining the number of PV arrays and their corresponding distance in the microgrid and then mitigate with optimized storage.

Original languageEnglish
Article number6891361
Pages (from-to)1434-1442
Number of pages9
JournalIEEE Transactions on Sustainable Energy
Volume5
Issue number4
DOIs
Publication statusPublished - 1 Oct 2014
Externally publishedYes

Fingerprint

Electricity
Systems analysis
Optimal systems
Distributed power generation
Costs

Keywords

  • Distributed PV systems
  • microgrid
  • photovoltaic (PV)
  • PV-storage system
  • smart grid
  • variability analysis

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

Predicting variability of high-penetration photovoltaic systems in a community microgrid by analyzing high-temporal rate data. / Shadmand, Mohammad B.; Balog, Robert; Johnson, Melanie D.

In: IEEE Transactions on Sustainable Energy, Vol. 5, No. 4, 6891361, 01.10.2014, p. 1434-1442.

Research output: Contribution to journalArticle

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