Planning of Smart Microgrids with High Renewable Penetration Considering Electricity Market Conditions
Hakimi, Seyed M.; Bagheritabar, Hamed; Hasankhani, Arezoo; Shafie-khah, Miadreza; Lotfi, Mohamed; Catalão, João P. S. (2019-08-01)
Hakimi, Seyed M.
Bagheritabar, Hamed
Hasankhani, Arezoo
Shafie-khah, Miadreza
Lotfi, Mohamed
Catalão, João P. S.
IEEE
01.08.2019
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202102195431
https://urn.fi/URN:NBN:fi-fe202102195431
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vertaisarvioitu
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Tiivistelmä
In this paper, a new method for optimal sizing of distributed generation (DG) is presented in order to minimize electricity costs in smart microgrids (MGs). This paper presents a study of the effect of wholesale electricity market on smart MGs. The study was performed for the Ekbatan residential complex which includes three smart MGs considering high penetration of renewable energy resources and a 63/20 kV substation in Tehran, Iran. The role of these smart MGs in the pool electricity market is a price maker, and a game-theoretical (GT) model is applied for their bidding strategies. The objective cost function considers different cost parameters in smart MGs, which are optimized using particle swarm optimization (PSO). The results show that applying this method is effective for economic sizing of DGs.
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