Licentiate thesis seminar: Uncertainty and correlation modeling for load flow analysis of future electricity distribution systems: Probabilistic modeling of low voltage networks with residential photovoltaic generation and electric vehicle charging
- Date: –17:00
- Location: Ångströmlaboratoriet, Lägerhyddsvägen 1 Room 4001, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala
- Doctoral student: Umar Hanif Ramadhani
- Contact person: Umar Hanif Ramadhani
Umar Hanif Ramadhani defends his doctoral thesis "Uncertainty and correlation modeling for load flow analysis of future electricity distribution systems: Probabilistic modeling of low voltage networks with residential photovoltaic generation and electric vehicle charging". Opponent: Sarah Rönnberg, Luleå University of Technology.
Welcome to follow the seminar via Zoom
(Meeting ID: 656 1213 9716)
The penetration of photovoltaic (PV) and electric vehicles (EVs) continues to grow and is predicted to claim a vital share of the future energy mix. It poses new challenges in the built environment, as both PV systems and EVs are widely dispersed in the electricity distribution system. One of the vital tools for analyzing these challenges is load flow analysis, which provides insights on power system performance. Traditionally, for simplicity, load flow analysis utilizes deterministic approaches and neglecting correlation between units in the system. However, the growth of distributed PV systems and EVs increases the uncertainties and correlations in the power system and, hence, probabilistic methods are more appropriate.
This thesis contributes to the knowledge of how uncertainty and correlation models can improve the quality of load flow analysis for electricity distribution systems with large numbers of residential PV systems and EVs. The thesis starts with an introduction to probabilistic load flow analysis of future electricity distribution systems. Uncertainties and correlation models are explained, as well as two energy management system strategies: EV smart charging and PV curtailment. The probabilistic impact of these energy management systems in the electricity distribution system has been assessed through a comparison of allocation methods and correlation analysis of the two technologies.
The results indicate that these energy management system schemes improve the electricity distribution system performance. Furthermore, an increase in correlations between nodes is also observed due to these schemes. The results also indicate that the concentrated allocation has more severe impacts, in particular at lower penetration levels. Combined PV-EV hosting capacity assessment shows that a combination of EV smart charging with PV curtailment in all buildings can further improve the voltage profile and increase the hosting capacity. The smart charging scheme also increased 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 uncertainty and correlation modeling is vital in probabilistic load flow analysis of future electricity distribution systems.