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The decarbonization of the power generation and transport sector encourage the analysis of connection of distributed energy resources (DER), such as electric vehicles (EVs), to the electrical system, as well as the evaluation of their impact on smart cities. A better understanding of the negative impacts on the power systems will lead to propose mitigation measures and eventually revolutionize the way distributed generation works. This paper aims at modelling and evaluating the impact of EVs on a real distribution network. The energy system chosen operates at 60 Hz, 34.5 kV (medium voltage) and 0.208 kV (low voltage) and it is simulated using PSCAD/EMTDC. To reproduce realistic user consumption profiles, dynamic load profiles based on EV owners behaviour have been simulated. The vehicle-to-grid (V2G) technology is modelled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. The results show the importance of active management in modern power systems, especially considering the increase in DER penetration expected for the coming years. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case. This paper aims at modelling and evaluating the impact of EVs on a real distribution network. The V2G technology is modelled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case.image

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.

This study investigates the potential implementation of a microgrid at Dansmästaren, a multifunctional building in Uppsala, Sweden, comprising a supermarket, residential apartments, and a parking garage. This paper analyzes the load profiles of the various components within Dansmästaren and the Uppsala grid to identify overlapping peak demand periods that increase overall facility power consumption and contribute to grid congestion. Focus is given to the three days with the highest peak loads; the worst day for Uppsala’s distribution grid, the highest peak for the entire building, and the highest peak for the parking garage, representing the most challenging scenarios for the microgrid. The study explores how integrating smart charging capabilities for electric vehicles in the parking garage can provide flexibility to shift loads and mitigate peak demands during these worst-case days. A flexibility index quantifies the extent to which the parking garage’s smart charging capabilities can support the building’s energy needs and reduce grid strain during peak demands. The study demonstrates a high potential for demand-side flexibility in Dansmästaren, as reflected by the Time Flexibility Index values of 0.77, 0.79, and 0.72. These results underscore the effectiveness of smart charging strategies in reducing peak loads and enhancing grid stability. The findings suggest that more advanced approaches could further improve building sustainability, especially in light of Sweden’s growing adoption of electric vehicles. This study encourages innovative urban solutions and provides a foundation for future research.

E-mobility is pivotal in enabling sustainable and technologically advanced urban environments. In line with this, Sweden's electric vehicle fleet is rapidly expanding, thereby increasing the power necessary for charging electric vehicles. If not properly managed and controlled, this increase in power can potentially threaten grid stability and exacerbate grid congestion. 

The primary aim of this thesis was to assess and investigate the potential of a next-generation parking facility at a multifunctional building to be an active part of the city’s distribution grid. The research was guided by the question of to what capacity smart control of a parking facility with a technical system could assist and alter the load demand to generate benefits for both the building and the city’s distribution grid.

This was investigated at Dansmästaren, the first multifunctional building in Uppsala, Sweden. An experimental setup with an electric vehicle charging station and an energy management system was developed at the Ångström laboratory to test and verify control strategies before their implementation at the multifunctional building's parking facility. Thereafter, a second energy management system was developed and implemented at Dansmästaren with the purpose of monitoring and controlling the electric vehicle charging at the parking facility.

The findings of the included papers were divided into two categories. The charging of the electric vehicles can either be assisted by the parking facility's technical system or altered by including the electric vehicle charging in the control for the technical system. Both categories show that a parking facility with a technical system in a multifunctional building can help reduce local grid demand while also providing local benefits for the building.

While the contribution of a single multifunctional building may appear negligible from a grid perspective, the cumulative effect becomes substantial when applied across multiple buildings. Thus, the parking facility at Dansmästaren has the potential to play an active role in the city’s distribution grid through smart charging and the utilization of an energy management system.

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.

Energy storage systems (ESS) are suitable for many grid applications, where some have large seasonal variations. By combining additional services, i.e., service stacking, companies with energy storage assets may generate additional revenue as well as provide services to the power system. This paper aims to highlight and estimate the technical and economic potential of stacking services using energy storage systems in congested a distribution grid. A scheduling optimization tool was implemented for a large-scale battery ESS providing a selection of deemed relevant services over two years. The results show that there is a great potential to enhance the value of the ESS significantly depending on the service portfolio layout. A capacity life loss model was also implemented to analyse the degradation of the ESS, and the cycle aging was estimated for the chosen portfolios. The results showed that a fully stacked portfolio does not necessarily result in increased cycle aging, but that it depended on the services in the portfolio. The cycle aging was affected the most by stacking flexibility with energy arbitrage, while frequency regulation services only resulted in a few additional cycles during each year of operation. 

The clean energy transition is expected to continue at a fast pace in the upcoming years and will imply connection of large amounts of distributed energy resources to existing distribution grids. These have the opportunity to support the power system both locally but also regionally and possibly on a system level too, which could be achieved by bundling services i.e., service stacking. The aim of this article is to compare effects on distribution grids when implementing service stacked portfolios for centralized and distributed storage capacities in congested distribution grids. The complex nature of the scheduling optimization problem motivated using a non-linear solver and for this study a meta-heuristic approach was chosen. A large number of energy storage units were connected to the IEEE European Low Voltage test feeder and load flow calculations were executed using the open-source distribution grid simulator OpenDSS. The results indicate that service stacking could be implemented successfully for both centralized and distributed storage capacities, but the possibility to target local and regional power quality measures varies for the two cases. Finally, multi-service provision using energy storage systems should be considered in more extensive simulation and real-world studies to fully capture the effects on grid dynamics and scheduling possibilities. 

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. 

Congestion management in distribution grids is a growing challenge for network operators due to a combination of fast growing cities and electrification of transport and industry sectors. Energy storage systems connected downstream of bottlenecks have the potential to relieve parts of the peak demand, and typically does not motivate investments financially as a third-party actor alone. By stacking services it is possible for storage units to become financially viable while also enabling opportunities for providing additional local services and participation in markets for ancillary services. In this study, a large customer connected to the congested distribution grid in Uppsala, Sweden, was considered and the systematic potential of a battery connected to the facility was evaluated. A set of portfolios including the services tariff optimization, flexibility-as-a-service, and frequency containment reserve was evaluated by finding the optimal scheduling strategy using a metaheuristic optimization approach together with a capacity loss life model. The analysis was done for two separate years to illustrate the impact of yearly market fluctuations on the service allocation. Additionally, a sensitivity analysis was also performed for the rated energy capacity of the battery. The simulation results indicate that stacking services yields the most valuable portfolio with respect to both technical and economic value where the battery managed to capture several revenue streams at the cost of a small increase in cycle aging, compared to when using the BESS for tariff optimization only. From a system perspective, stacking services enables the BESS to support the power system and the local distribution grid during a large majority of the year. 

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.