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The increasing trend of small-scale residential photovoltaic (PV) system installation in low-voltage (LV) distribution networks poses challenges for power grids. To quantify these impacts, hosting capacity has become a popular framework for analysis. However, previous studies have mostly focused on small-scale or test feeders and overlooked uncertainties related to rooftop azimuth and tilt. This paper presents a comprehensive evaluation of city-level PV hosting capacity using data from over 300 real LV systems in Varberg, Sweden. A previously developed rooftop azimuth and tilt model is also applied and evaluated. The findings indicate that the distribution systems of the city, with a definition of PV penetration as the percentage of houses with 12 kW installed PV systems, can accommodate up to 90\% PV penetration with less than 1\% risk of overvoltage, and line loading is not a limiting factor. The roof facet orientation modeling proves to be suitable for city-level applications due to its simplicity and effectiveness. Sensitivity studies reveal that PV size assumptions significantly influence hosting capacity analysis. The study provides valuable insights for planning strategies to increase PV penetration in residential buildings and offers technical input for regulators and grid operators to facilitate and manage residential PV systems.

To evaluate the effects of different energy retrofit scenarios on the residential building sector, in this study, an urban building energy model (UBEM) was developed from open data, calibrated using energy performance certificates (EPCs), and validated against hourly electricity use measurement data. The calibrated and validated UBEM was used for implementing energy retrofit scenarios and improving the energy performance of the case study city of Varberg, Sweden. Additionally, possible consequences of the scenarios on the electricity grid were also evaluated in this study. The results showed that for a calibrated UBEM, the MAPE of the simulated versus delivered energy to the buildings was 26 %. Although the model was calibrated based on annual values from some of the buildings with EPCs, the validation ensured that it could produce reliable results for different spatial and temporal levels than calibrated for. Furthermore, the validation proved that the spatial aggregation over the city and temporal aggregation over the year could considerably improve the results. The implementation of the energy retrofit scenarios using the calibrated and validated UBEM resulted in a 43 % reduction of the energy use in residential buildings renovated based on the Passive House standard. If this was combined with the generation of on-site solar energy, except for the densely populated areas of the city, it was possible to reach near zero (and in some cases positive) energy districts. The results of grid simulation and power flow analysis for a chosen low-voltage distribution network indicated that energy retrofitting of buildings could lead to an increase in voltage by a maximum of 7 %. This particularly suggests that there is a possibility of occasional overvoltages when the generation and use of electricity are not in perfect balance.

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sortingbased methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

BACKGROUND: Determination of a probability density function (PDF) is an area of active research in engineering sciences as it can improve process systems. A previously developed polynomial method-of-moments-based PDF estimation model has been applied in the research to produce accurate approximations to both standard and more complex PDF. A model with a different polynomial basis than a monomial is still to be developed and evaluated. This is the work that is undertaken in this study.

RESULTS: A set of standard PDF (Normal, Weibull, Log Normal and Bimodal) and more complex distributions (solutions to the Smoluchowski coagulation equation and Population Balance equation) were approximated by the method-of-moments using Chebyshev, Hermite and Lagrange polynomial-based density functions. Results show that Lagrange polynomial-based models improve the fit compared to monomial based-modeling in terms of RMSE and Kolmogorov-Smirnov test statistic estimates. The Kolmogorov-Smirnov test-statistics decreased by 19% and the RMSE values were improved by around 85% compared to the standard monomial basis when using Lagrange polynomial basis.

CONCLUSION: This study indicates that the procedure using Lagrange polynomials with method-of-moments is a more reliable reconstruction procedure that calculates the approximate distribution using lesser number of moments, which is desirable.

Renewable energy sources (RES) and electric vehicles (EVs) are two promising technologies that are widely recognized as key components for achieving sustainable cities. However, intermittent RES generation and increased peak load due to EV charging can pose technical challenges for the power systems. Many studies have shown that improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). This paper takes a city on the west coast of Sweden as a case study. The smart charging and V2G schemes in this study aim to minimize the mismatch between generation and load and are formulated as quadratic programming problems. Results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance is increased from 68% in the opportunistic charging scenario to 73% in the smart charging scenario and to 84% in the V2G scenario. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings in this paper indicate a high potential from EV flexibility in improving urban energy system performance. 

This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.

As interest in deploying Battery Storage systems (BSSs) grows, a significant challenge is to determine the specific services that the BSS should provide to maximize profits. This study aims to determine the most profitable strategy and size of integrated grid-connected BSS with and without PV park for participating in Day-Ahead Market (DAM) and Frequency Regulation Market (FRM). The Frequency control services activate in response to changes in the electricity grid frequency, with BSS supporting during frequency fluctuations. The focus of this study is on the primary regulation within FRM. In this study, a BSS operation algorithm is evaluated in economic terms. The algorithm imports inputs like market prices, fees, tariffs, PV production, and chosen BSS service. Economic metrics include Net Present Value (NPV) and Internal Rate of Return (IRR). Real-world data from a Swedish PV park was used for case studies across three categories: BSS stand-alone, PV park alone, and PV-BSS combination. Results highlight that stand-alone BSS scenarios are superior to PV-BSS combination cases, showing a 73% Internal Rate of Return (IRR) for a 1000 kWh/400 kW BSS configuration. PV park alone participation in FRM and DAM shows marginal benefits compared to only acting on the spot market. The sensitivity analysis examining changes in prices for both DAM and FRM relative to 2022 reveals a significant negative change in revenue in 2020, which is explained by the higher and more fluctuating electricity prices. Lastly, the sensitivity analysis explores changes in the acceptance rate of bids in the future relative to 2022, as FCR products will be procured at a marginal price. These analyses indicate potential negative changes that may occur as the acceptance rate may decrease.

The electrical system is currently undergoing a transition, where newhigh-power flexible loads, e.g., electric vehicles (EVs), are penetrating residential areas, and distributed power production from e.g., photovoltaic (PV) panels are also rapidly increasing in the low voltage (LV) grid. This transition requires new modeling methods to accurately predict the vulnerabilities and the needs to upgrade the current grid. A methodology to utilize spatiotemporal Markov based models of PV and EV charging to evaluate the impacts of these technologies on the high voltage (HV)/medium voltage (MV) substations is presented in this report. Furthermore, a case study on a large Swedish city was made. In this case study, penetrations of 100% for the EVs and PV were simulated. The results indicated that EV charging increases the peak load in the city by up to 18%–28%, and the peak load in the substations increased by up to 55%. During July, the PV yield was at most 45% of the winter consumption peak in the city and the summer-time net in-feed was at most 77% in any of the primary substations. Only 3 out of 10 substations experienced overloading events, and in all but one substation these events were shorter than 17 h/year. These overloading have negligible impact on the life-time of the main transformers as they predominantly occur when the ambient temperature is low. To avoid expensive upgrades to the MV transformers, the reserve transformers in the substations can be used to alleviate these overloading incidences. This solution however will not solve hosting capacity limitations in the underlying grid.

Statistics on installed solar energy systems (SES) play a crucial role in the solar energy industry, providing valuable information for a wide range of stakeholders, such as policy makers, authorities, and financial evaluators. For example, grid operators rely on accurate data on photovoltaic penetration levels to ensure the quality and stability of the power supply. In this research, we present an automatic approach helping generate these statistics using deep learning and image processing techniques. Our proposed model is a machine learning approach that utilizes a specific architecture of convolutional neural networks (CNN) called the "U-net'' to detect SES from aerial images. We experimented different network settings to enhance the SES identification performance.In this study, the model was evaluated using two datasets from different locations, one from Sweden and one from Germany. Additionally, the model was trained and tested on a combination of both datasets. The impact of image resolution was also examined. The experimental results show that this architecture performs better than many recent CNN models that have been proposed in the literature for the task of SES identification from aerial images. To make it easy for others to replicate our findings, We have shared all the scripts, software, and dependencies required for running the model in this paper, along with instructions on how to use it in Appendix A.

One of the essential epistemic uncertainties that has not yet been studied enough for distributed photovoltaic systems is the azimuth and tilt of rooftop photovoltaic panels, as previous studies of grid impacts and hosting capacity have tended to assume uniform and optimal roof facet conditions. In this study, rooftop facet orientation distributions are presented and analyzed for all single-family buildings in the Swedish city of Uppsala, based on LiDAR-based data that consist of every roof facet from the around 13,500 single-family buildings in the city. From these distributions, novel methods to proportionally include less suitable roofs for every penetration level are proposed using a simple method based on normal and uniform probability density functions, and are tested for both time-series and stochastic hosting capacity analysis. The results show that under the assumption that the best roof facets are utilized first, a uniform distribution for rooftop facet azimuth and a normal distribution for rooftop facet tilt with parameters that depend linearly on the penetration level were shown to be accurate. The hosting capacity simulations demonstrate how the proposed methods perform significantly better in estimating the photovoltaic hosting capacity than the more common simplified methods for both time-series and stochastic hosting capacity analysis. The proposed model could help distribution system operators as well as researchers in this area to model the rooftop facet orientation uncertainty better and improve the quality of aggregated photovoltaic generation models and hosting capacity analyses.