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The distribution grid in Uppsala, Sweden, has during the last years experienced an increasing number of hours with congestion, and as the city and number of EVs are growing new solutions are needed to not worsen this problem. Uppsala Municipality is planning a series of so-called “mobility houses” which will act as a hub for transportation while, at the same time, supporting the transition to a fossil-free energy system. Dansmästaren – the first mobility house in Uppsala – was built in 2020 and this paper describes its parking garage's main energy system parts, giving a brief introduction and analysis of each and their potential effect on Dansmästaren's grid impact. Dansmästaren has the potential to control its load on the grid, but it is essential to collect more data and analyze when flexibility is of most importance. Future studies suggest analyzing Dansmästaren's systems in more detail and developing a more advanced energy management system.

The electrification of the transport sector is of crucial importance for a successful transition to a fossil-free society. However, the electricity grid constitutes a bottleneck. This article provides a case study based on a real-world parking garage with a smart grid infrastructure, called Dansmästaren. The analysis shows how renewable energy sources, energy storage technologies, and smart charging of electric vehicles can smooth out the load curve of the parking garage and relieve the electric grid during peak hours. Dansmästaren is located in Uppsala, Sweden, and equipped with 60 charging points for electric vehicles, a PV system, and a battery storage system. The study utilizes an energy flow model to show the potential of a realistically dimensioned smart energy system, that can benefit the parking facility in itself and the local distribution grid in a city, Uppsala, with grid capacity challenges. The results suggest that the parking garage demand on the local grid can be significantly lowered by smarter control of its relatively small battery energy storage. Moreover, further smart control strategies can decrease demand up to 60% during high load hours while still guaranteeing fully charged vehicles at departure in near future scenarios. The study also shows that peak shaving strategies can lower the maximum peaks by up to 79%. A better understanding of the potential of public infrastructures for electric vehicle charging helps to increase knowledge on how they can contribute to more sustainable cities and a fossil-free society.

The need for a more flexible usage of power is increasing due to the electrification of new sectors in society combined with larger amounts of integrated intermittent electricity production in the power system. Among other cities, Uppsala in Sweden is undergoing an accelerated transition of its vehicle fleet from fossil combustion engines to electrical vehicles. To meet the requirements of the transforming mobility infrastructure, Uppsala municipality has, in collaboration with Uppsala University, built a full-scale commercial electrical vehicle parking garage equipped with a battery storage and photovoltaic system. This paper presents the current hardware topology of the parking garage, a neural network for day-ahead predictions of the parking garage’s load profile, and a simulation model in MATLAB using rule-based peak shaving control. The created neural network was trained on data from 2021 and its performance was evaluated using data from 2022. The performance of the rule-based peak shaving control was evaluated using the predicted load demand and photovoltaic data collected for the parking garage. The aim of this paper is to test a prediction model and peak shaving strategy that could be implemented in practice on-site at the parking garage. The created neural network has a linear regression index of 0.61, which proved to yield a satisfying result when used in the rule-based peak shaving control with the parking garage’s 60 kW/137 kWh battery system. The peak shaving model was able to reduce the highest load demand peak of 117 kW by 38.6% using the forecast of a neural network.