Open AccessComparison of point‐of‐load versus mid‐feeder compensation in LV distribution networks with high penetration of solar photovoltaic generation and electric vehicle charging stations
Author(s) -
Akhtar Zohaib,
Opatovsky Martin,
Chaudhuri Balarko,
Hui Shu Yuen Ron
Publication year - 2019
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2018.0193
Subject(s) - photovoltaic system , voltage , compensation (psychology) , automotive engineering , unavailability , voltage regulation , electrical engineering , low voltage , overvoltage , electric vehicle , engineering , control theory (sociology) , power (physics) , computer science , control (management) , reliability engineering , physics , psychology , quantum mechanics , psychoanalysis , artificial intelligence
Increasing use of distributed generation (DG), mainly roof‐top photovoltaic (PV) panels and electric vehicle (EV) charging would cause over‐ and under‐voltage problems generally at the remote sections of the low‐voltage (LV) distribution feeders. As these voltage problems are sustained for a few hours, power electronic compensators (PECs) with input voltage control, i.e. electric springs cannot be used due to the unavailability of non‐critical loads that can be subjected to non‐rated voltages for a long duration of time. However, PECs in output voltage control mode could be used to inject a controllable series voltage either somewhere on the feeder (mid‐feeder compensation, MFC) or between the feeder and each customer (point‐of‐load compensation, PoLC) both of which are effective in tackling the voltage problem without disrupting PV power output and EV charging. In this study, a comparison between the MFC and PoLC option is presented in terms of their voltage control capability, required compensator capacity, network losses, PV throughput, and demand response capability. The criteria for selection of the optimal location of these compensators are also discussed. Stochastic demand profile for different types of residential customers in the UK and a typical European LV network is used for the case study.