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(Meeting ID: 656 1213 9716)

An electronic version of the doctoral thesis is available in DIVA.

**Abstract:**

The penetration of ph
otovoltaic (PV) and electric vehicles (EVs) continues to grow and is pre
dicted to claim a vital share of the future energy mix. It poses new cha
llenges in the built environment\, as both PV systems and EVs are widely
dispersed in the electricity distribution system. One of the vital tool
s for analyzing these challenges is load flow analysis\, which provides
insights on power system performance. Traditionally\, for simplicity\, l
oad flow analysis utilizes deterministic approaches and neglecting \
; correlation between units in the system. However\, the growth of distr
ibuted PV systems and EVs increases the uncertainties and correlations i
n the power system and\, hence\, probabilistic methods are more appropri
ate.

This thesis contributes to the knowledge of how uncertainty a nd correlation models can improve the quality of load flow analysis for electricity distribution systems with large numbers of residential PV sy stems and EVs. The thesis starts with an introduction to probabilistic l oad flow analysis of future electricity distribution systems. Uncertaint ies and correlation models are explained\, as well as two energy managem ent system strategies: EV smart charging and PV curtailment. The probabi listic impact of these energy management systems in the electricity dist ribution system has been assessed through a comparison of allocation met hods and correlation analysis of the two technologies.

The results indicate that these energy management system schemes improve the electr icity distribution system performance. Furthermore\, an increase in corr elations between nodes is also observed due to these schemes. The result s also indicate that the concentrated allocation has more severe impacts \, in particular at lower penetration levels. Combined PV-EV hosting cap acity assessment shows that a combination of EV smart charging with PV c urtailment in all buildings can further improve the voltage profile and increase the hosting capacity. \;The smart charging scheme also inc reased the PV hosting capacity slightly. The slight correlation between PV and EV hosting capacity shows that combined hosting capacity analysis of PV systems and EVs is beneficial and is suggested to be done in one framework. Overall\, this thesis concludes that an improvement of uncert ainty and correlation modeling is vital in probabilistic load flow analy sis of future electricity distribution systems.

DESCRIPTION:Umar Hanif Ramadhani defends his doctoral thesis "Uncertainty and correlation modeling for load flow analysis of future electricity d istribution systems: Probabilistic modeling of low voltage networks with residential photovoltaic generation and electric vehicle charging". Opp onent: Sarah Rönnberg\, Luleå University of Technology.\n SUMMARY:Licentiate thesis seminar: Uncertainty and correlation modeling f or load flow analysis of future electricity distribution systems: Probab ilistic modeling of low voltage networks with residential photovoltaic g eneration and electric vehicle charging LOCATION:Ångströmlaboratoriet\, Lägerhyddsvägen 1\, Room 4001\, Ångström Laboratory\, Lägerhyddsvägen 1\, Uppsala TZID:Europe/Stockholm DTSTART:20210409T131500 DTEND:20210409T170000 UID:20210409T131500-60444@uu.se END:VEVENT END:VCALENDAR