Assessing Combined High Photovoltaic and Electric Vehicle Charging Penetration in Low-Voltage Distribution Networks: A Case Study in Malta

Brian Azzopardi () and Yesbol Gabdullin
Additional contact information
Brian Azzopardi: MCAST Energy Research Group, Institute for Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), PLA 9032 Paola, Malta
Yesbol Gabdullin: MCAST Energy Research Group, Institute for Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), PLA 9032 Paola, Malta

Energies, 2024, vol. 17, issue 1, 1-22

Abstract: High Photovoltaic (PV) and Electric Vehicle (EV) Charging Penetration challenges the grid’s Low-Voltage (LV) Distribution Network’s stability due to voltage variations and the overloading of feeders. This research paper investigates the potential of combined PV and Electric Vehicle (EV) charging integration within LV DN, using a representative DN in Malta as a case study. The European Union (EU) has set forth objectives and guidelines that suggest a high likelihood of Distributed Networks (DNs) incorporating a significant number of Photovoltaic Systems (PVs), resulting in overvoltage occurrences, as well as a substantial number of Electric Vehicles (EVs), which may charge in an erratic manner, leading to undervoltage and overloading events. A distribution network (DN) may experience unfavorable situations concurrently due to the simultaneous occurrence of photovoltaic (PV) generation and electric vehicle (EV) charging, particularly in residential case studies. Effectively employing either dispersed or centralized storage is a viable approach to tackle these issues. However, this strategy may defer the requirement for expensive DN investments. The study showcases the extent of automated mitigation attained in the urban zones of Malta. The data presented primarily comprises empirical measurements obtained at the onset of the LV feeder.

Keywords: photovoltaic (PV); electric vehicle (EV); low-voltage (LV) networks; stochastic processes; Monte Carlo methods; optimal power flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/1/263/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/1/263/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:1:p:263-:d:1313070

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().