Open AccessOptimal sizing and placement of rooftop solar photovoltaic at Kabul city real distribution network
Author(s) -
Ahmadi Mikaeel,
Lotfy Mohammed Elsayed,
Shigenobu Ryuto,
Yona Atsushi,
Senjyu Tomonobu
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0687
Subject(s) - photovoltaic system , renewable energy , sizing , computer science , electric power system , grid parity , automotive engineering , grid connected photovoltaic power system , stand alone power system , electricity , distributed generation , reliability engineering , engineering , voltage , electrical engineering , maximum power point tracking , power (physics) , photovoltaics , inverter , art , physics , quantum mechanics , visual arts
Renewable energy resources (RERs) such as wind and solar are said to be considerable promising of the power system worldwide, and Afghanistan is evaluated for abundant and feasible electricity generation capacity from these resources. It fortifies merging of RER to the electric power system of Afghanistan where power quality issue sums up with scheduled and unscheduled load shedding due to the shortage of electricity. This research study presents an optimal solution comprising of rooftop solar photovoltaic (PV) as distributed generation to a real and substantial 162‐bus electric distribution network (EDN) in Kabul, the capital of Afghanistan. Genetic algorithm (GA) based on Newton–Raphson power flow with the objective of power loss minimisation is put forward for sizing and placement of the solar PV at practically available locations or candidate buses of the network. This approach tends to reduce the dependency on the import power and at the same time improves the performance of the current system through minimisation of the total power loss and voltage deviation. The proposed method is simulated by MATLAB ® software to compare and demonstrate the performance of the system under different scenarios of the PV allocations.