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Energy-smart facilities contribute to a cleaner future by assisting the decarbonization of power generation and transportation. One example is the multi-functional building called Dansmästaren, which comprises a residential part, a commercial part and a parking garage and that is located in Uppsala, Sweden. This facility is a test bed for research on smart infrastructures for electric vehicle charging and has the flexibility to integrate different energy storage technologies, electricity generation units, database collection, and connectivity systems. Furthermore, Dansmästaren’s parking garage targets two distinct EV owner groups: public charger users and those with residential charging access. This work provides insights into charging behavior across these categories. This paper applies load forecast model using LSTM neural network to predict the parking garage’s load demand profile at Dansmästaren based on historical data. The performance of the LSTM network during testing and training is analyzed across various scenarios, using historical data from May 2021 to April 2023 collected at the parking garage. The hour-ahead load demand prediction achieves a mean absolute error (MAE) of 4.7. The results obtained in this study are valuable for implementing future smart charge strategies in real environment and for increasing knowledge about EV charging patterns.

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 the 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 (Case A), the highest peak for the entire building (Case B), and the highest peak for the parking garage (Case C), 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 scenarios. The study highlights strong demand-side flexibility in Dansmästaren, with Case A, B, and C showing Time Flexibility Index values of 0.77, 0.79, and 0.72, where the index ranges from 0 to 1, with a value nearer to 1 indicating higher flexibility. The resulting load profiles show a peak load reduction of 62 %, 77 %, and 74%. These results highlight the effectiveness of smart charging in reducing peak loads and enhancing grid stability, suggesting that advanced strategies could further boost building sustainability, especially with Sweden's growing adoption of electric vehicles. The findings also encourage innovative urban solutions and pave the way for future research.

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 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 damping effect, induced inside the linear generator, is a vital factor to improve the conversion efficiency of wave energy converters (WEC). As part of the mechanical design, the translator mass affects the damping force and eventually affects the performance of the WEC by converting wave energy into electricity. This paper proposes research on the damping effect coupled with translator mass regarding the generated power from WEC. Complicated influences from ocean wave climates along the west coast of Sweden are also included. This paper first compares three cases of translator mass with varied damping effects. A further investigation on coupling effects is performed using annual energy absorption under a series of sea states. Results suggest that a heavier translator may promote the damping effect and therefore improve the power production. However, the hinder effect is also observed and analyzed in specific cases. In this paper, the variations in the optimal damping coefficient are observed and discussed along with different cases.

The working principle of the wave energy converter (WEC) developed at Uppsala University (UU) is based on a heaving point absorber with a linear generator. The generator is placed on the seafloor and is connected via a steel wire to a buoy floating on the surface of the sea. The generator produces optimal power when the translator's oscillations are centered with respect to the stator. However, due to the tides or other changes in sea level, the translator's oscillations may shift towards the upper or lower limit of the generator's stroke length, resulting in a limited stroke and a consequent reduction in power production. A compensator has been designed and developed in order to keep the generator's translator centered, thus compensating for sea level variations. This paper presents experimental tests of the compensator in a lab environment. The wire adjustments are based on online sea level data obtained from the Swedish Meteorological and Hydrological Institute (SMHI). The objective of the study was to evaluate and optimize the control and communication system of the device. As the device will be self-powered with solar and wave energy, the paper also includes estimations of the power consumption and a control strategy to minimize the energy requirements of the whole system. The application of the device in a location with high tides, such as Wave Hub, was analyzed based on offline tidal data. The results show that the compensator can minimize the negative effects of sea level variations on the power production at the WEC. Although the wave energy concept of UU is used in this study, the developed system is also applicable to other WECs for which the line length between seabed and surface needs to be adjusted.

This paper presents two wave energy concepts for small-scale electricity generation. In the presented case, these concepts are installed on the buoy of a heaving, point-absorbing wave energy converter (WEC) for large scale electricity production. In the studied WEC, developed by Uppsala University, small-scale electricity generation in the buoy is needed to power a tidal compensating system designed to increase the performance of the WEC in areas with high tides. The two considered and modeled concepts are an oscillating water column (OWC) and a heaving point absorber. The results indicate that the OWC is too small for the task and does not produce enough energy. On the other hand, the results show that a hybrid system composed of a small heaving point absorber combined with a solar energy system would be able to provide a requested minimum power of around 37.7W on average year around. The WEC and solar panel complement each other, as the WEC produces enough energy by itself during wintertime (but not in the summer), while the solar panel produces enough energy in the summer (but not in the winter).