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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.

Load profiles derived from the growing amount of smart meter electricity consumption data are increasingly being sought. They can be valuable to public entities and grid operators for purposes such as operational planning, analyzing the co-location of users with smoothing profiles, dimensioning distribution grids, procuring distributed energy resources (DER) such as batteries or PV arrays, and implementing demand response projects. The challenge lies in analyzing the vast amount of available data in ways that make it useful - preferably to a broader audience. This article presents a straightforward and adaptable framework for creating typical load profiles (TLPs). The framework applies two normalizations across two clustering steps: k-Means, to identify prominent patterns in specific data subsets, and k-Modes, to recombine the identified subset patterns into a yearly-coherent and re-scalable TLP in an innovative way. The framework is demonstrated using real data of roughly 60,000 daily time series from around 40 public elementary schools in Uppsala Municipality, Sweden. The results showed that the framework could efficiently differentiate the load behaviors of the studied users over the year. Their largest load differences occurred midday and during weekends, although most studied users exhibited a similar behavior. The load magnitude was also shown to have a usable linear relationship with the schools' heated indoor area, enabling the use of TLPs to estimate the load of a new arbitrary school. Due to its design and versatility, the presented framework can serve as a valuable tool for identifying prominent patterns and supporting relevant decision-making processes.

As an increasing number of public actors both in the EU and in Sweden-from the highest political level to local operational levels-request greater flexibility and understanding of electrical power systems, additional methods and tools are necessary to identify potentially valuable sources of flexibility. In this study, we developed the Flexibility Value Index (FVI) to quantify and rank the system perspective-value of different electricity users' potential flexibility. Previous studies have mainly concentrated on either broad system levels, assessing total flexibility across an entire sector, country, or technology, or on a very detailed level, analyzing individual buildings based on specific appliance data on a case-by-case basis. However, there is a lack of studies between these two sides that, with limited information, aim to identify users whose consumption behavior or patterns would be valuable to change. This paper presents a quantification method based on five indicators demonstrated by evaluating electricity users from seven categories in Uppsala Municipality, Sweden. The results showed that the largest average FVIs were typically found in the later afternoon and early evenings during weekdays, coinciding with higher grid loads, but also in the mornings and weekends for individual users. Grid owners, public entities, or private businesses can use the developed index in strategic decision-making to identify users who might provide the most valuable flexibility for the power system for further investigations.

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation.

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 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 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.

With more electric vehicles introduced in society, there is a need for further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including novel concepts such as smart charging, vehicle-to-grid and vehicle-to-everything. Access to charging infrastructure and novel ideas on charging or discharging may contribute to driving the transition towards e-mobility. This article aims to provide an overview of vehicle charging and discharging innovations with a cable connection, including an example of a MATLAB/Simulink model of vehicle-to-grid dynamics.