Logo: to the web site of Uppsala University

Denna rapport sammanfattar en nio månader lång förstudie om möjligheterna att skapa ett mer sammankopplat och hållbart energisystem i Morgongåva, med särskilt fokus på Morgongåva Företagspark. Den långsiktiga målbilden är att skapa ett energisystem i Morgongåva där energi i större utsträckning delas mellan Företagsparken och samhället och där systemet samoptimeras utifrån parternas möjligheter och förutsättningar. Studien har genomförts i samverkan mellan Morgongåva Företagspark AB, Sala-Heby Energi AB, STUNS, Uppsala universitet och FVB, med stöd från Europeiska regionala utvecklingsfonden via Tillväxtverket. Bakgrunden till projektet är ett identifierat överskott av solel från Företagsparken samt ett behov av att förnya den befintliga pelletseldade fjärrvärmepannan i samhället. Förstudien har därför undersökt möjligheterna att gemensamt nyttja elöverskottet för lokal värmeproduktion genom en elpanna, i kombination med ett termiskt lager, för att på så sätt minska behovet av förbränningsbaserad fjärrvärme. Resultaten visar att upp till 25 % av nuvarande pelletsbehov skulle kunna ersättas med ett system bestående av ett solcellssystem på 2000 kWp, en elpanna på cirka 1,2 MW och ett termiskt lager på 2500 m³. Den största tekniska begränsningen är möjligheten till tillräckligt stor lagringskapacitet, som i nuläget hindras av gällande detaljplan. Ur ett ekonomiskt perspektiv är återbetalningstiden för investeringen upp till 20 år, men den kan halveras till omkring 10 år vid förändrade skattevillkor och fortsatt stigande bränslepriser. Studien pekar också på att flexibla, lokala energilösningar har värden som idag inte enkelt kan kvantifieras, men som är av stor vikt för ett långsiktigt hållbart och robust energisystem.

Accurate separation of global tilted irradiance (GTI) becomes important when the measured irradiance is used for quality control or PV simulation purposes, for which the latter often requires global horizontal irradiance (GHI), or diffuse and beam irradiance fractions. This study presents an evaluation of irradiance reverse transposition and separation models for the application with GTI in high latitudes. The evaluation is made based on measured and quality controlled six-second irradiance from latitude 59.53 degrees N, containing GTI at 30 degrees, 40 degrees, and 90 degrees tilt angles, as well as GHI and diffuse horizontal irradiance (DHI). Based on a literature review, two specialized GTI reverse transposition and separation models-GTI-DIRINT and PEREz-DRIESSE-and four GHI separation models were chosen for evaluation. The latter were tested in an optimization loop developed for this study that utilizes existing GHI separation models combined with transposition models for reverse transposition and separation of GTI. Specifically, the separation models ERaS, S << ARtVEIt1, ENGERER2, and YANG4 were tested with HAY & DAVIES and PEREz1990 transposition. The models were investigated using both statistical evaluation metrics and Diebold-Mariano test to compare measured and predicted GHI and DHI. An evaluation with measured data showed that for GTI reverse transposition and separation at high latitudes, the use of the proposed optimization model with ENGERER2 in combination with HAY & DAVIES transposition, or the PEREz-DRIESSE model is recommended. This is based on overall good ranking and low bias of GHI prediction with-2.0 W/m2 and-2.3 W/m2, respectively.

A significant challenge is to determine the specific services Battery Energy Storage System (BESS) should provide to maximize profits. This study investigates the most profitable markets and sizes of BESS with utility-scale solar Photovoltaics (PV) power plants using techno-economic analysis frameworks. The objective is to maximize profitability in energy and frequency markets, focusing on primary regulation and day-ahead markets for Sweden and Germany. The inputs are historical market prices and frequency data, as well as real measurement PV power data. The results show that adding a BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany during 2023, i.e., being 1.8 and 6.8 years, respectively. This is explained by the lower frequency market prices for Germany compared to Sweden. The technical results indicate that the BESS energy capacity after 10 years of operation is approximately 83% for Germany, whereas, for Sweden, it is around 87%. Also, combining the operating of BESS on primary regulation and day-ahead markets showed a 6-year payback period with a slight increase in loss of energy capacity (from 83 to 80%) for Germany. Moreover, combining various PV-BESS sizes showed a discrepancy in economic and technical metrics for the BESS in Germany, resulting in a best-case of a 6-year payback period. A sensitivity analysis, which examines a drop in the frequency control prices in the future relative to 2023 (by 20% and 50% for Germany and Sweden, respectively), reveals an increase in the payback period for both countries by approximately 1 year.

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.

In Sweden, Svenska Kyrkan (the Church of Sweden) has over 3300 churches. A majority of the churches are electrically heated. Usage patterns of electrically heated buildings such as church buildings, creating problems for the grid and the church organization through increased grid fees. Simultaneously, interest in deploying Battery Energy Storage Systems (BESSs) is growing. A significant challenge is determining the specific services the BESS should provide to maximize profits for the owner. For church load profiles, with the help of a battery, the church consumption peaks can be shaved. Additionally, when the Battery Energy Storage System (BESS) is not used for this purpose, it can instead be employed to support the grid through participation in the frequency regulation market. Frequency control services are activated in response to changes in the electricity grid frequency, with the BESS providing support during frequency fluctuations. The objective of this study is to investigate the economic value of installing BESS in a church powered by a PV system. Various frequency regulation services, with a focus on primary reserve, are explored. The model operates on other energy markets, which are local flexibility and day-ahead markets. The inputs include selected services, feed-in and feed-out profiles, historical frequency data, and frequency regulation and energy market prices over the year 2023. The case study involves measured data from Kila Church, which has a 60 kWp solar power system and is located in mid-western Sweden. The economic metrics are net present value and payback period, whereas technical and environment metrics are the battery degradation and CO2 emission equivalents, respectively. This study indicates that the investment in BESS is profitable if the BESS operates on frequency stability services together stacked with Peak Shaving (PS). The results show a 1.6-year payback period for a 120 kWh/60 kW BESS. A sensitivity analysis explores future changes in prices of the frequency regulation market and BESS shows that FCR-D Up has more sensitivity for a drop in the prices in the future. Nevertheless, FCR-D Down has more economic potential value. Conclusively, BESS would be a beneficial investment for churches and facilities with similar load and PV power generation profiles, both from an economic and societal perspective.

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.