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The world is now in a transition towards a more sustainable future. Actions to reduce the green-house gases (GHG) emissions have been promoted and implemented globally, including switching to electric vehicles (EVs) and renewable energy technologies, such as solar photovoltaics (PV). This has led to a massive increase of EVs and PV adoption worldwide in the recent decade.

However, large integration of EVs and PV in buildings and electricity distribution systems pose new challenges such as increased peak loads, power mismatch, component overloading, and voltage violations, etc. Improved synergy between EVs, PV and other building electricity load can overcome these challenges. Coordinated charging of EVs, or so-called EV smart charging, is believed to a promising solution to improve the synergy.

This licentiate thesis investigates the synergy between residential EV charging and PV generation with the application of EV smart charging schemes. The investigation in this thesis was carried out on the individual building, community and distribution grid levels. Smart charging models with an objective to reduce the net-load (load - generation) variability in residential buildings were developed and simulated. Reducing the net-load variability implies both reducing the peak loads and increasing the self-consumption of local generation, which will also lead to improved power grid performance. Combined PV-EV grid hosting capacity was also assessed.      

Results show that smart charging schemes could improve the PV self-consumption and reduce the peak loads in buildings with EVs and PV systems. The PV self-consumption could be increased up to 8.7% and the peak load could be reduced down to 50%. The limited improvement on self-consumption was due to low EV availability at homes during midday when the solar power peaks. Results also show that EV smart charging could improve the grid performance such as reduce the grid losses and voltage violation occurrences. The smart charging schemes improve the grid hosting capacity for EVs significantly and for PV slightly. It can also be concluded that there was a slight positive correlation between PV and EV hosting capacity in the case of residential electricity distribution grids.

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.

Cities are responsible for around 75% of global primary energy use and 70% of global greenhouse gas (GHG) emissions, with buildings and urban mobility being two key contributors. Actions to reduce GHG emissions have been promoted and implemented in many countries in the world. These include switching to electric vehicles (EVs) and renewable energy sources (RES), such as solar photovoltaics (PV). The transition has led to rapid increase in EV and PV adoption worldwide in the recent decades. However, large-scale integration of EVs and PV in urban energy systems poses new challenges such as increased peak loads, power mismatch, component overloading, and voltage violations. Improved synergy between EVs, PV and other loads can overcome these challenges. Coordinated charging of EVs, or so-called EV smart charging, is potentially a promising solution to improve the synergy. The synergy can be further enhanced with vehicle-to-grid (V2G) schemes, where an EV can not only charge, but also discharge power from its battery. 

This doctoral thesis investigates the synergy between EV charging and PV power generation with the application of EV smart charging and V2G schemes. The investigation was carried out through simulation studies on the system levels of residential buildings, workplaces, distribution grid, and city-scale. Smart charging and V2G optimization models with an objective to reduce the net-load (load minus generation) variability were developed and simulated. 

The results show that the PV-EV synergy can be improved with the proposed smart charging schemes. However, the levels of improvement depend highly on the user mobility behavior from and to the destined charging locations. PV-EV synergy is limited in residential buildings due to low EV occupancy during high solar power production, but has high potential at workplace charging stations due to high EV occupancy during the same time. In the case studies presented in this thesis, it was found that the implementation of smart charging can improve the synergy by up to around 9 percentage points in residential buildings and up to around 40 percentage points in workplaces. On a city-scale level, both optimal sizing and V2G play essential roles in improving city-scale generation-load synergy, as they can increase the load matching from 33% to 84%. The results also show that improved synergy leads to enhanced power grid performance and combined PV-EV grid hosting capacity.

In conclusion, the thesis demonstrates that EV smart charging schemes can improve PV-EV synergy, leading to enhanced performance of urban energy systems.

Urban demographics are changing, with more than half of the global population currently residing in urban areas. Traditionally, cities are often seen as passive energy consumers relying on external centralized systems. Motivated by the need to mitigate climate change, a shift is underway as cities actively shape energy systems. This shift involves decentralized power generation, electric vehicle (EV)-related electricity usage shifts, enhanced building energy efficiency, and increasing interaction between local power generation and load. This poses some challenges to distribution grid operation such as voltage violation, decreased power quality, equipment damage, power losses, and reliability issues. Addressing these issues requires load flow analysis, and to quantify the impacts based on load flow analysis, the hosting capacity concept has been introduced. Although traditional load flow analysis lacks uncertainty consideration, the growth of distributed photovoltaics (PV) generation and EVs demands enhanced accuracy through uncertainty modeling.

This thesis contributes to the knowledge of how uncertainty and correlation models can improve the quality of load flow and hosting capacity analysis for urban electricity distribution systems with high penetration of residential PV systems and EVs through the combination of methodological and case studies. Methodological studies propose uncertainty models for input variables and investigate their impact on load flow and hosting capacity assessment. Case studies demonstrate enhanced hosting capacity analysis quality through applied uncertainty models.

Results show that concentrated allocation of PV systems and EVs had more severe impacts, in particular at lower penetration levels, and smart charging in concentrated allocation had more significant benefits to reduce peak load and voltage drop. Results regarding residential building roofs show that the inclusion of more residential buildings when the PV penetration increases will require including a lot of less-optimal facets, and, hence, a novel method has been proposed to proportionally include less optimal roofs at every penetration level. The smart charging scheme, which has as its main objective to reduce the net-load variability, improves the electricity distribution system performance, and combined with PV curtailment, can further increase the hosting capacity. An increase in correlations between nodes is also observed due to this smart charging scheme. The city-level simulations show that the distribution system of the city can accommodate a 90% penetration level of PV with less than 1% risk of overvoltage and line loading does not limit the hosting capacity. The method used to model roof facet orientation proves effective for city level applications, given its simplicity and effectiveness.

In summary, this thesis concludes that the quality and knowledge of load flow and hosting capacity analysis for urban electricity distribution systems can be improved by several methods, including: the probabilistic model of PV power generation and EV charging profiles, the inclusion of EMS, the consideration of spatial allocation methods of PV and EV, the assessment of the correlation between PV and EV, and the consideration of rooftop tilt and azimuth uncertainties.