Open AccessModelling and allocation of open‐UPQC‐integrated PV generation system to improve the energy efficiency and power quality of radial distribution networks
Author(s) -
Lakshmi Shubh,
Ganguly Sanjib
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2017.0525
Subject(s) - photovoltaic system , particle swarm optimization , voltage , electronic engineering , power quality , maximum power point tracking , engineering , power (physics) , computer science , control theory (sociology) , electrical engineering , inverter , algorithm , physics , control (management) , quantum mechanics , artificial intelligence
The study presents the modelling and allocation strategy for open unified power quality conditioner (UPQC‐O) integrated photovoltaic (PV) generation system in radial distribution networks to improve the energy efficiency and PQ. An UPQC is a custom power device, which consists of series and shunt inverters. In UPQC‐O, these inverters are placed in different locations in a network. There is a communication channel to share the information among these inverters to select the respective set point. Two models proposed are: (i) UPQC‐O with battery and PV array (UPQC‐O‐WB) and (ii) UPQC‐O with only PV array (UPQC‐O‐WOB). In UPQC‐O‐WB, the energy generated by PV array is stored during its operation hour to utilise it during peak hour. However, in UPQC‐O‐WOB, the energy generated by PV array is directly injected to the network. The proposed models are incorporated in the forward–backward sweep load flow to determine the operational parameters such as bus voltage. An optimisation problem is formulated to determine the optimal placement of UPQC‐O with PV array in distribution networks. The objective function includes the investment and operational costs of inverters, battery and PV array, and the cost of energy loss. The particle swarm optimisation is used as the solution strategy.