Abstract

The urban and rural energy systems are undergoing modernization. This modernization is motivated by the need to increase sustainability in both systems. Some characteristics of this modernization include electrification of industries, transports, and heating and cooling loads. Additionally, there has been an increase in building-applied photovoltaic (PV) systems, and in the flexibility of customer loads. This thesis aims to progress the knowledge regarding the electric power production and consumption in the future urban and rural energy systems. In total, three models were developed and applied to case studies: a spatial electric vehicle (EV) charging model, a residential load forecasting model, and a clear-sky index (CSI) generative model. The results of the EV spatial model showed that there is an aggregation effect for the charging of the EVs. If all EVs charge opportunistically upon arrival using 3.7 kW, at most 19% of the EVs in a large area will charge simultaneously. Delaying the charging to after 22:00 will result in a significant increase in the simultaneity factor — to 59%. Two forecasting models were compared for the residential load. Both models achieved a root mean square error (RMSE) smaller than 4%. One model had a slightly sharper forecast than the other model — by 2.6% — and a variable prediction interval (PI) which decreased at night. As regards the spatiotemporal matching between PV power production and EV charging in rural and urban areas, the results showed that there were no correlations between the building type in each part of the city and the temporal matching. Both residential and workplace areas had similar temporal matching. This is because of the orientations of the roofs in the cities and the sizes of the parking lots. Considering the impacts of EV charging on the distribution grid of a Swedish municipality (Herrljunga), it is shown that 3.7 kW chargers will result in at most a 1% decrease in the voltage of the grid. No under-voltages were witnessed. In conclusion, the urban and rural energy systems can withstand the penetration of PV and EVs in the nearby coming years. Extreme scenarios might, however, require increasing the flexibility or performing upgrades to the systems.  

Abstract

Decarbonization is one of the biggest challenges of our time. The European Union, as other organizations have already designed the strategies to achieve a more sustainable future. The integration of renewable energy sources is not a technical challenge anymore. However, due to the variability of these type of technologies some actions should be implemented so the conventional ones are not needed any more. One crucial solution is the implementation of demand side management actions which focus on controlling the demand rather than the generation. This thesis identifes the diferent actions on diferent demand side management actions and their performance in Spain. Also, an analysis regarding implemented policies in other European countries is performed to identify successful actions that could be later implemented in Spain. Ingeniería de la Energí

Abstract

The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared. Document type: Article