Open AccessProbabilistic methodology for estimating the optimal photovoltaic capacity in distribution systems to avoid power flow reversals
Author(s) -
LujanoRojas Juan M.,
DufoLópez Rodolfo,
BernalAgustín José L.,
DomínguezNavarro José A.,
Catalão João P.S.
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2017.0777
Subject(s) - photovoltaic system , probabilistic logic , ac power , computer science , reliability engineering , power (physics) , voltage , control engineering , mathematical optimization , engineering , automotive engineering , electrical engineering , mathematics , artificial intelligence , physics , quantum mechanics
The large‐scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi‐static time‐series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic‐cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.