Artikel

Optimal planning in a developing industrial microgrid with sensitive loads

Computer numerical control (CNC) machines are known as sensitive loads in industrial estates. These machines require reliable and qualified electricity in their often long work periods. Supplying these loads with distributed energy resources (DERs) in a microgrid (MG) can be done as an appropriate solution. The aim of this paper is to analyze the implementation potential of a real and developing MG in Shad-Abad industrial estate, Tehran, Iran. Three MG planning objectives are considered including assurance of sustainable and secure operation of CNC machines as sensitive loads, minimizing the costs of MG construction and operation, and using available capacities to penetrate the highest possible renewable energy sources (RESs) which subsequently results in decreasing the air pollutants specially carbon dioxide (CO2). The HOMER (hybrid optimization model for electric renewable) software is used to specify the technical feasibility of MG planning and to select the best plan economically and environmentally. Different scenarios are considered in this regard to determine suitable capacity of production participants, and to assess the MG indices such as the reliability.

Sprache
Englisch

Erschienen in
Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 3 ; Year: 2017 ; Pages: 124-134 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
CNC machine
HOMER
MG planning
Reliability
RES penetration

Ereignis
Geistige Schöpfung
(wer)
Naderi, M.
Bahramara, S.
Khayat, Y.
Bevrani, Hassan
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2017

DOI
doi:10.1016/j.egyr.2017.08.004
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Naderi, M.
  • Bahramara, S.
  • Khayat, Y.
  • Bevrani, Hassan
  • Elsevier

Entstanden

  • 2017

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