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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.

This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4 s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation.

Building archetype identification is crucial for Urban Building Energy Modeling (UBEM), but is still considered one of the biggest challenges in this field. New methods of data acquisition, along with data mining techniques such as clustering, have recently received attention for the possibility of significantly increasing identification reliability and archetype accuracy. This paper aims to establish a new and simple clustering methodology for developing building archetypes for hybrid UBEM, using open data sets and multiple diverse variables, that is still reliable and possible to validate without the use of metered energy use or real building data. The methodology uses k-means clustering for 10 building parameters simultaneously, including socio-economic parameters obtained using spatial interpolation of statistical values. Building archetypes are successfully developed for the residential building stocks of two case study areas in Sweden. The results also show that the error metric values for multiple iterations diverge after a certain number of clusters, even when using the same clustering methodology on the same data set. This discovered effect, along with the combined use of one well-known and one novel error metric, constitutes a framework well adapted to accurately determining the optimal number of building archetypes.

Renewable hybrid power parks (HPPs) that combine wind power, solar photovoltaic (PV) power and storage have emerged as promising electricity generation resources. However, HPPs face operational challenges due to the uncertainty in power production and electricity prices, which is why probabilistic forecasts that capture the uncertainty associated with forecast errors have gained attention. While the research community has proposed several methods to improve the accuracy of probabilistic forecasts, the question on how these forecasts can improve decision-making over deterministic forecasts is rarely quantified. This study assesses the value of probabilistic forecasts and analyze the improvement compared to deterministic forecasts in day-ahead markets. The value is quantified using almost two years of data from an operational HPP in Sweden. Results show that: (i) high grid connection capacities leverage the value of probabilistic models, (ii) a deterministic model is preferable for parks with a ratio of battery energy capacity to installed nominal power of the renewable power park equal to 0.6 MWh/MW, (iii) a probabilistic model allows utilizing the energy storage more effectively by reducing the energy throughput of the battery with 61%–87%, and (iv) a probabilistic model increases the unit profit when the forecast errors of the regulating price are higher than the spot price, (v) a simple probabilistic benchmark model, which is worse in terms of forecast accuracy, increases the unit profit compared to the analyzed deterministic models, and (vi) the more advanced probabilistic model analyzed in this study does not provide a significant improvement over a simple probabilistic benchmark model.

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. 

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.

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.

Urban building energy models (UBEMs) are considered as applicable tools for urban energy planning. Model developers use different strategies to simulate urban building energy use appropriately, and yet they are often doing so in the absence of high-quality data. While data collection is challenging in many cases, in Sweden, the availability of national databases is relatively good and is expected to facilitate the modeling procedure considerably. This study aims to develop, calibrate and validate an UBEM using available national data, including GIS-based property maps and energy performance certificates (EPCs). The developed UBEM offers an automated framework for constructing simple building-level models from open data and conducting energy simulations in EnergyPlus. The developed UBEM was calibrated and validated for two case study cities in Sweden, Borlänge and Uppsala, where the mean absolute percentage error (MAPE) between simulated results and EPC data was 26% and 22%, respectively. Furthermore, a downward trend was observed in the MAPE with increasing spatial aggregation from building to district and city levels (from 26% to 21% and 10%), which highlights the performance of the UBEM in this study to support accurate urban-scale energy analyses for buildings.

The latest attempts in determining the spatiotemporal patterns of energy use in the building sector have led to the development of a new set of tools referred to as “urban building energy models” (UBEMs). Due to the high level of complexity, the computation cost of UBEMs risks becoming impractically large. As a substitution for complex models, in this study, using a simplified steady-state method for calculating the energy performance of buildings, a more computationally efficient UBEM is proposed. The developed model uses the available information of buildings from open datasets, translates them into simplified physical models, and, finally, estimates the energy performance of buildings for desired spatial and temporal resolutions. A comparison of the simplified UBEM with an advanced UBEM, developed around the building energy simulation software EnergyPlus, proves that the suggested simplified model performs within an acceptable range of accuracy. Furthermore, using the simplified model, the computation cost of the model can improve considerably, from hours to only a few seconds. By validating the results of the simplified UBEM against the measured energy performance of buildings from the Swedish energy performance certificate (EPC) database, it can be also seen that the MAPE does not go higher than 31%.