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.

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 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.

Energy storage systems are widely used for power system applications. By implementing service stacking, enhanced performance of storage systems can potentially be obtained. A scheduling tool based on linear programming was implemented to schedule a grid connected energy storage for two portfolios in separate periods. The results show that it is possible to provide additional services which generate value to the power system. By implementing a capacity loss life model the increased cycle aging is estimated. 

Flexibility has increasingly gained attention within the field of electrification and energy transition where a common objective is to reduce the electricity consumption peaks. However, flexibility can increase the risk of grid congestion depending on where and when and it is used, thus an overall system perspective needs to be considered to ensure an effective energy transition. This paper presents a framework to assess electricity users' contributions to grid load peaks by splitting electricity consumption data into subsets based on time and temperature. The data in each subset is separately correlated with the grid load using three correlation measures to assess how the user's consumption changes at the same time as typical grid peak loads occur. The framework is implemented on four different types of business activities at Uppsala municipality in Sweden, which is a large public entity, to explore their behaviors and assess their grid peak load contributions. The results of this study conclude that all four activities generally contribute to the grid peak loads, but that differences exist. These differences are not visible without splitting the data, and not doing so can lead to unrepresentative conclusions. The presented framework can identify activities that contribute the most to unfavorable grid peaks, providing a tool for decision-makers to enable an accelerated energy transition.

As the global transition away from fossil fuels accelerates, energy systems across the globe face a significant challenge. Given the high energy consumption of electric vehicle chargers, effective control is imperative to prevent local grid overload and congestion. In Uppsala, Sweden, a newly built parking garage includes 30 electric vehicle chargers, 62 kW solar energy production, and a 60 kW/137 kWh battery energy storage system. This paper presents a control algorithm that uses a negative correlation scheme, adjusted to the local grid load, to effectively manage the battery energy storage. To improve the performance of the algorithm, a genetic optimization method is applied to find the best feasible daily load profile for the parking garage. The results indicate that peak load and energy consumption during grid high-load hours can be significantly reduced. This also results in an 9.5−12.8% reduction in electricity distribution fees at current prices as well as a peak load reduction of up to 50 %. Increasing the battery capacity and charging/discharging power in the scenarios analysed within the study will improve the algorithm’s ability to achieve a satisfactory negative correlation between the load demand of the facility and the local grid. The proposed control algorithm lowers the facility’s impact on the local grid during high-load peak hours by utilizing the battery energy storage system at the parking garage. Moreover, it decreases the distribution fees of the facility by lowering the load peaks and shifting the electricity consumption to the morning and night.

The power grid faces a rising challenge of increasing variability due to the integration of intermittent renewable energy sources (RES) and the connection of more and new types of loads. This development heightens the risk of both capacity shortage and grid congestion, addressing the need to complement traditional grid extension, which is expensive and can take a long time. A promising approach is load flexibility, which is the ability of an electricity user to adjust its consumption during a set time interval. This study proposes a Flexibility Value Index (FVI) to rank electricity users based on the value of their potential flexibility. The FVI utilizes three indicators derived from a user's consumption and the local grid's load. The FVI is demonstrated on seven test profiles, followed by ranking five different types of users from Uppsala Municipality, Sweden, during winter working days. The study reveals a spread in the FVI, and the ranked list enables a public entity or a grid owner to focus resources on the users that can potentially realize the most flexibility. Furthermore, the FVI can be utilized on the production from RES, indicating which might be a suitable match to enhance the grid's hosting capacity.