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This report is the result from a project funded by ELFORSK done by STRI. Theproject is studying the effects the introduction of so called “prosumers”(customers with own production) and electrical vehicles will have on differenttypes of networks. Four different cases are studied: covering urban and ruralareas with different types of customers.In the urban areas the power through the transformer will be the limitingfactor. The major impact in the cases studied is from the introduction ofproduction from photovoltaics at the customer-side of the meter. This willresult in an introduction of surplus due to production which in one case led toan increase of the absolute power through the transformer with more than30%, which resulted in transformer overloading.In the rural areas the voltage drop or rise will be the limiting factor. The casesstudied had already high voltage drops even in the base cases. In the casestudies it was seen that the voltage drop could be slightly reduced whenintroducing more local production, but the production also led to that voltagerise could appear. As a result the interval of the voltage variations wasincreased, which in turn leads to difficulties with designing the network suchthat neither overvoltage nor undervoltage occurs.Introducing control algorithms had a very positive effect on reducing the netproduction from the photovoltaics. Using both hard and soft curtailment madeit possible to remove all overcurrents or overvoltages. Using hard curtailment,where all production is turned off during overcurrent or overvoltage, leadshowever to a large reduction in energy from renewable energy sources.Therefore soft curtailment should as much as possible be used.The control algorithms studied for reducing the net consumption had a morelimited effect and even resulted in an increase of the maximum netconsumption. When trying to reduce the net consumption during an overload,the reason of the overload could only be moved in time and not removed as inthe case of reducing the net production. And since often the period duringwhich the power exceeds the limit is longer than the number of hours possibleto move the energy, sometimes moving the energy had an adverse effect.The model used for controlling the net consumption needs furtherdevelopment, but it is still possible to draw the conclusion that this type ofcontrol offers only limited possibilities for mitigating overload or undervoltage.The effects of introducing prosumers and more electrical vehicles as defined inthe selected cases did not show any alarming results in this study. However,studies to learn more about the possible consequences of changes atcustomer-side are important to be able to handle the impact of such changeson the network.Further future studies needed: database with load and production data;improved control algorithms; demonstration project; experience from othercountries and studies on reactive power compensation.

In this paper, environmental impact and energy matching assessments for a residential building with a rooftop photovoltaic (PV)system, battery energy storage system (BESS) and electric vehicles (EV) charging load are conducted. This paper studies a real multi-family house with a rooftop PV system in a city located on the west-coast of Sweden, as a case study. The environmental impact parameter assessed in this study is CO2 equivalent (CO2-eq) emissions. It should be noted that the CO2-eqemission assessment takes into account the whole life cycle, not only the operational processes. The assessments consider boththe household and transport energy demands for the building’s residents. Results show that, CO2-eq emissions from the building electricity usage are increased by 1.65 ton/year with the integrationof PV-BESS system. This is because the Swedish electricity mix has a lower CO2-eq emissions than the PV-BESS system. The total CO2-eq emissions from the transport needs of the building’s residents are significantly decreased, by 32.9 ton/year, if they switch from fossil-fuel-powered cars to EVs. However, the integration of EVs increases the power demand significantly which could be problematic for the power system. In such scenario, the highly-utilized distributed PV systems, enhanced by BESS, can be a low-carbon solution to address the increased power demand challenges coming from transport electrification.

The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared.

This paper presents an electric vehicle (EV) smart charging scheme at residential buildings based on installed photovoltaic (PV) output and household electricity consumption. The proposed EV charging scheme is designed to determine the optimal EV charging schedules for the purpose of minimizing the load variance or flattening the load profile. When the net-load is taken into account in the smart charging scheme, not only the peak load can be reduced, but also the PV self-consumption in the building can be increased. The charging scheduling problem is formulated and solved with a quadratic programming approach. The departure and arrival time and the distance covered by vehicle in each trip are specifically modelled based on available statistic data from Swedish travel survey. The scheme is applied on simulated typical Swedish detached houses without electric heating. The aggregation of distributed smart charging in multiple housesis conducted and compared to the smart charging in a single house. Numerical results are presented to show the effectiveness of the proposed smart charging scheme. Positive results on both the PV self-consumption and the peak load reduction are achieved.

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. 

Photovoltaic (PV) systems and electric vehicles (EVs) integrated in local distribution systems are considered to be two of the keys to a sustainable future built environment. However, large-scale integration of PV generation and EV charging loads poses technical challenges for the distribution grid. Each grid has a specific hosting capacity limiting the allowable PV and EV share. This paper presents a combined PV-EV grid integration and hosting capacity assessment for a residential LV distribution grid with four different energy management system (EMS) scenarios: (1) without EMS, (2) with EV smart charging only, (3) with PV curtailment only, and (4) with both EV smart charging and PV curtailment. The combined PV-EV hosting capacity is presented using a novel graphical approach so that both PV and EV hosting capacity can be analyzed within the same framework. Results show that the EV smart charging can improve the hosting capacity for EVs significantly and for PV slightly. While the PV curtailment can improve the hosting capacity for PV significantly, it cannot improve the hosting capacity for EVs at all. From the graphical analysis, it can be concluded that there is a slight positive correlation between PV and EV hosting capacity in the case of residential areas.

Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have to be integrated into the power systems and be operated together with already existing loads and generators and, often, into buildings, where they potentially impact the overall energy performance of the buildings. Thus, a high penetration of both PV and EVs poses new challenges. Understanding of the synergies between PV, EVs and existing electricity consumption is therefore required. Recent research has shown that smart charging of EVs could improve the synergy between PV, EVs and electricity consumption, leading to both technical and economic advantages. Considering the growing interest in this field, this review paper summarizes state-of-the-art studies of smart charging considering PV power production and electricity consumption. The main aspects of smart charging reviewed are objectives, configurations, algorithms and mathematical models. Various charging objectives, such as increasing PV utilization and reducing peak loads and charging cost, are reviewed in this paper. The different charging control configurations, i.e., centralized and distributed, along with various spatial configurations, e.g., houses and workplaces, are also discussed. After that, the commonly employed optimization techniques and rule-based algorithms for smart charging are reviewed. Further research should focus on finding optimal trade-offs between simplicity and performance of smart charging schemes in terms of control configuration, charging algorithms, as well as the inclusion of PV power and load forecast in order to make the schemes suitable for practical implementations.

The integration of photovoltaic (PV) systems and electric vehicles (EVs) in the built environment, including at workplaces, has increased significantly in the recent decade and has posed new technical challenges for the power system, such as increased peak loads and component overloading. Several studies show that improved matching between PV generation and EV load through both optimal sizing and operation of PV-EV systems can minimize these challenges. This paper presents an optimal PV-EV sizing framework for workplace solar powered charging stations considering load matching performances. The proposed optimal sizing framework in this study uses a novel score, called self-consumption-sufficiency balance (SCSB), which conveys the balance between self-consumption (SC) and self-sufficiency (SS), based on a similar principle as the F1-score in machine learning. A high SCSB score implies that the system is close to being self-sufficient without exporting or curtailing a large share of local production. The results show that the SCSB performance tends to be higher with a larger combined PV-EV size. In addition to presenting PV-EV optimal sizing at the workplace charging station, this study also assesses a potential SC and SS enhancement with optimal operation through smart charging schemes. The results show that smart charging schemes can significantly improve the load matching performances by up to 42.6 and 40.8 percentage points for SC and SS, respectively. The smart charging scheme will also shift the combined optimal PV-EV sizes. Due to its simplicity and universality, the optimal sizing based on SCSB score proposed in this study can be a benchmark for future studies on optimal sizing of PV-EV system, or distributed generation-load in general.

The integration of electric vehicle (EV) chargingand Photovoltaic (PV) systems at residential buildings has increased in recent years and poses new challenges for the power system. Smart charging of EVs is believed to be one ofthe solutions to problems arising from PV and EV integration since it can improve the synergy between PV generation and EV charging. Accurate forecasts of PV generation plays an important role in smart charging schemes to optimally utilize the PV electricity for EV charging. This paper presents an assessment of a direct forecasting method applied to an EV smart charging scheme. Direct forecasting is a forecasting method which focus directly on the link between the forecast origin and the targeted horizon. The objective of the smart charging in this study is to minimize the net-load variability, which will also increase the self-consumption of PV electricity and reduce the peak loads. The PV self-consumption ratios in different forecast scenarios are compared. Results show that the smart charging with the direct forecast can achieve up to 89% of the PV self-consumption performance of the scheme with perfect forecast

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.

Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify.

In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler.

Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon.

For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.

In order to contribute to the reduction of greenhouse gas emissions, electric vehicles (EVs) should be charged using electricity from renewable energy sources. This paper describes a study of photovoltaics (PV) utilization for EV charging in two Scandinavian cities: Tromsø in Norway and Uppsala in Sweden, with the objective to evaluate self-sufficiency and self-consumption.

The suitable areas for PV were determined using building area statistics and utilization factors. The PV yield was simulated for integration scenarios of 10%-100% of the suitable area. EV charging patterns were generated using a stochastic model based on travel survey data. The scenarios include EV penetration of 10%-100% of the personal vehicle fleet.

The results show that the PV energy yield could cover the EV load in most of the scenarios, but that the temporal load match could be improved. The energy balance was positive for all seasons and EV levels if the PV integration was over 50%. The highest self-sufficiency was achieved in Tromsø during summer, due to the longer days. For high EV penetration and low PV integration, the self-sufficiency was higher in Uppsala, indicating that installed PV power is more important than yield profile above a certain number of EVs.

Plug-in hybrid electric vehicles (PHEVs) have received an increased interest lately since they provide an opportunity to reduce greenhouse gas emissions. Based on the PHEV introduction level in the car park, the charging behaviors in an area will induce changes in the load profiles of the power system. Hence, it becomes important to investigate what impact a given PHEV introduction level has on load profiles due to expected charging behavior of residents. This paper proposes a new model for generating PHEV home-charging patterns by combining PHEV usage with synthetic activity generation of residents' electricity-dependent activities. The synthetic activity data are simulated based on time-use data collected in time diaries, and define the basis for calculations of the PHEV home-charging behavior as well as the resident's electricity consumption. The proposed model is generic and can be used where similar residential time-use data are available. Based on the underlying activities, the model estimates the total load profile due to residential load as well as the variation in the load profile. The resulting load profiles can be used in load shaving studies in order to investigate what type of activities, PHEV usage or other, may be moved to hours with lower demand.

The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to rapidly increase in the future. At aggregated level, the large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures. On the other hand, considered as a resource-efficient and cost-effective demand response resource, EV fleet smart charging control methods have been developed and applied to mitigate power issues of the grid while avoiding expensive upgrade of power grid infrastructure. Until now, there is no systematic study on how different coordination mechanisms affecting the EV fleet's charging demand response performance. Thus, it is still unclear which one may perform better in the increasingly common solar-powered building communities, especially as demand response is increasingly concerned. Aiming to fill in such knowledge gaps, this study conducted systematic comparative studies of three representative control methods selected from the non-coordinated, bottom-up coordinated, and top-down coordinated control categories. Their power regulation performances have been comparatively investigated in two perspectives: minimizing peak power exchanges with the grid and maximizing PV self-utilization, based on a real building community in Sweden. Meanwhile, their computational performances have also been investigated. The study results show that due to the ability to schedule and coordinate all the EVs simultaneously, the top-down coordinated control is superior to the other two control methods in the considered demand response performances. Note that its better performance is realized with a higher computational load, leading to possible convergence difficulties in practice. The study results will help improve understanding of how coordination affect the EV smart charging control performances. It will pave the way for developments of more sophisticated control methods for EV smart charging in more complex scenarios.

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.

Simplification of building energy models is one of the most common approaches for efficiently estimating the energy performance of buildings over the whole city. The abstraction of a building into an information model, and the division of the model into representative thermal zones, are no longer customized based on building-specific conditions but they are generic and applicable to many buildings. Considering the limited research on the performance of such methods, in this study, a comprehensive evaluation of the most relevant assumptions on zoning configurations and levels of details is conducted in three building energy simulation tools IDA ICE, TRNSYS and EnergyPlus. The findings from the evaluation of zoning configuration on building-level and its comparison with the measured energy performance of buildings suggest that a single-zone model of a building gives a very similar result to a multi-zone model with one core zone and perimeter zones for every floor of the building. For the single-zone model, IDA ICE overestimates and EnergyPlus underestimates the energy demand compared to the more complex models, by approximately the same amount, but EnergyPlus is preferred due to the shorter simulation time. It is also proven that higher levels of detail in building models can increase the accuracy of the results by approximately 6% annually. By extending the scope of the study from building- to district-level analysis, it is also noted that in large-scale studies where a somewhat lesser degree of accuracy can be allowed on the individual building level, the simplified models give acceptable results.

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 adoption of domestic photovoltaic systems has in numerous studies been proven to be influenced by peer effects and socioeconomic factors such as income, age, gender, education etc., which has led to irregular spatial installation patterns. Only a few studies regarding domestic solar thermal systems indicate that the same effect exist for this technology. However, the interaction between photovoltaic and solar thermal deployment and the similarities or differences in socioeconomic factors have not been investigated in detail so far. This study identifies the most prominent socioeconomic factors behind both domestic photovoltaic and solar thermal adoption in three different municipalities in Sweden, based on a complete set of 452 photovoltaic and 359 solar thermal collector systems installed until 2020, which was identified and classified by a method that uses machine learning and aerial imagery. A moderate (absolute Pearson correlation, |ρ|, > 0.3) to intermediate (|ρ| > 0.5) correlation between photovoltaic and solar thermal penetration was found on demographic statistical area level, and several of the previously reported influential socioeconomic factors for domestic photovoltaic installation were confirmed also for domestic solar thermal adoption in Sweden.

The interest for co-located wind and solar photovoltaic (PV) parks, also known as hybrid power parks (HPPs), is increasing both in industry and in the scientific community. Co-locating wind and PV can lead to synergies in power production, infrastructure, and land usage, which may lower the overall plant cost compared to single technology systems. This review paper summarizes the existing research on power output modeling related to utility-scale HPPs and identifies knowledge-gaps. The main literature shows that there is a need for improved modeling methodologies accounting for the variability of the combined power production. There is potential for immediate improvement by combining state-of-the-art models that have been developed in separate fields and harmonizing the vocabulary across the different research fields. The study also shows that the total number of peer reviewed studies on utility-scale HPPs is limited and further research, in particular comparative studies, is needed to give a comprehensive view of the benefits and challenges of combining technologies. Other areas such as physical design, control strategies, market participation, and quantification of the possible synergies for physical implementation of HPPs also need to be studied further.

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.

This study presents a model for estimatingbuilding-applied photovoltaic (PV) energy yield and electric ve- hicle (EV) charging temporally over time and spatially on a city scale. The model enables transient assessment of the synergy between EV and PV, thus is called the EV-PV Synergy Model. Spatio-temporal data on solar irradiance is used in combination with Light Detection and Ranging (LiDAR) data to generate realistic spatio-temporal solar power generation profiles. The spatio-temporal EV charging profiles are produced with a stochastic Markov chain model trained on a large Swedish data set of travel patterns combined with OpenStreetMap (OSM) for deterministically identifying parking spaces in cities. The modelled estimates of solar power generation andEV charging are combined to determine the magnitude and correlation between PV power generation and EV charging over time on city scale for Uppsala, Sweden. Two months (January and July) were simulated to represent Sweden’s climate extremes. The EV penetration level was assumed to be 100% and all the roofs with yearly irradiation higher than 1000 kWh/m² were assumed to have PV panels. The results showed that, even in January with the lowestsolar power generation and maximum EV load, there can be a positive net-generation (defined as the integration of PV generation minus EV charging load over time) in some locations within the city. Central locations exhibited a positive temporal correlation between EV charging load and PV generation. Negative temporal correlations were observed in the outskirts of the city, where typically night time home-charging was prevalent. In the highest PV power generation month (July) the solar generation was 16 times higher than the EV charging load. Spatially, the net-generation was positive in almost the entire city. However, the time-series correlation between the EV charging load and the PV generation reached more extreme positive and negative values in comparison with January. This was a result of the higher variability in irradiance during July in comparison with January. In summary, we find that there is a favorable synergy of EV-PV technology within the city center with assumptions of workplace charging behaviors for both winter and summer months. An unfavorable synergy with suburban areas where typically nighttime charging behaviors negatively correlate to PV generation. This suggests that distributed PV should be targeted around city center/workplace EV charging stations.

Grid-connected photovoltaic (PV) systems have been dependent on supporting schemes to be competitive with conventional electricity generation. Selling prices of PV power production are now lower than buying prices in several countries, making it profitable to match generation with household consumption. Self-consumption, calculated as in situ instantaneous consumption of PV power production relative to total power production, can be used to improve the profitability with higher buying than selling prices of electricity. Another measure, self-sufficiency, similar to self-consumption but calculated relative to the yearly consumption, can also be used. Battery storage and electric vehicle (EV) home-charging are interesting alternatives to increase the self-consumption, since the PV power production can be stored for later use. This study uses high-resolution consumption data for 21 single-family houses in Sweden and irradiance data for the year 2008 to examine the potential for battery storage and EV home-charging for communities of single-family houses with PV systems. The aim is to compare how self-consumption and self-sufficiency are affected by individual power grid connections for all households versus one shared grid connection for the whole community. These scenarios are combined with battery storage and EV charging (individual versus centralized). It is found that total consumption profiles level out when several houses are connected together, the self-consumption increases from 52 to 71 % and the self-sufficiency from 12 to 17 %. The size of a centralized storage can be reduced compared to the aggregated size of storages in every house to reach the same level of self-consumption. The potential for EV charging is limited due to mismatch between irradiance and charging patterns. The extra revenue from increased self-consumption with battery storage is too low for all the cases to justify an investment in batteries since the prices are still too high. With dedicated support schemes, higher buying prices of electricity and cheaper battery, PV-battery systems can still be an interesting solution in countries with high solar irradiance throughout the year.

Photovoltaic (PV) power generation is an important component in the future energy system. High penetration of PV power in a distribution power grid might however lead to overvoltage, i.e. +10% of rated voltage, for end-users. This study compares PV power curtailment and decentralized energy storage for overvoltage prevention in a 400V/10 kV distribution grid with large penetration of PV. LiDAR analysis is used to identify rooftops suitable for PV in a Swedish distribution grid with more than 5000 end-users. Results show that power curtailment allows 22% PV electricity (19 GWh) relative to total consumption on a yearly basis without overvoltage. PV production is reduced with 0.35 GWh due to curtailment. Decentralized energy storage of in total 86 MWh capacity achieves the same result.

Renewable distributed generation and electric vehicles (EVs) are two important components in the transitions to a more sustainable society. However, both distributed generation and EV charging pose new challenges to the power system due to intermittent generation and high-power EV charging. In this case study, a power system consisting of a low- and medium-voltage distribution grid with more than 5000 customers, high penetration of roof-top mounted photovoltaic (PV) power systems and a fully electrified car fleet is used to assess the impact of the intermittent PV generation and high-power EV charging loads. Two summer weeks and two winter weeks with and without EV charging and a PV penetration varying between 0% and 100% of the annual electricity consumption are examined using measured and simulated data. Results show that the electricity consumption increases with 9% and 18% during the studied periods, and that EV charging only marginally can contribute to lowering the risk of overvoltage for customers resulting from PV overproduction. The most significant result is the increase in undervoltage in the winter when EV charging is introduced. The share of customers affected by undervoltage increases from 0% to close to 1.5% for all PV penetration levels.

Renewable distributed generation and electric vehicles (EVs) are two important components in the transition to a more sustainable society. However, both pose new challenges to the power system due to the intermittent generation and EV charging load. In this case study, a power system consisting of a low- and medium-voltage rural and urban distribution grid with 5174 customers, high penetration of photovoltaic (PV) electricity and a fully electrified car fleet were assumed, and their impact on the grid was assessed. The two extreme cases of two summer weeks and two winter weeks with and without EV charging and a PV penetration varying between 0 and 100% of the annual electricity consumption were examined. Active power curtailment of the PV systems was used to avoid overvoltage. The results show an increased electricity consumption of 9.3% in the winter weeks and 17.1% in the summer weeks, a lowering of the minimum voltage by 1% at the most, and a marginal contribution by the EV charging to lower the need of PV power curtailment. This shows the minor impact of EV charging on the distribution grid, both in terms of allowing more PV power generation and in terms of lower voltage levels.

Increasing the self-consumption of photovoltaic (PV) power is an important aspect to integrate more PV power in the power system. The profit for the PV system owner can increase and the stress on the power grid can be reduced. Previous research in the field has focused on either self-consumption of PV power in individual buildings or PV power curtailment for voltage control. In this paper self-consumption of residential PV power in a community of several single-family houses was investigated using high-resolution irradiance and power consumption data. Cases with individual or shared battery energy storages for the houses were examined. PV power curtailment was investigated as a method to reduce feed-in power to the grid, i.e. peak shaving. Results indicated that the self-consumption ratio increased when using shared instead of individual storage. Reducing the feed-in power from the community by almost 50% only led to maximum 7% yearly production losses due to curtailment and storage losses. The economics for shared storage are slightly better than for individual ones. These results suggest that residential PV-battery systems should use (i) shared energy storage options if local regulations allow it and (ii) PV power curtailment if there are incentives to lower the feed-in power.

We present basic theoretical constraints on the effects of destruction by supernovae (SNe) and growth of dust grains in the interstellar medium (ISM) on the radial distribution of dust in late-type galaxies. The radial gradient of the dust-to-metals ratio is shown to be essentially flat (zero) if interstellar dust is not destroyed by SN shock waves and all dust is produced in stars. If there is net dust destruction by SN shock waves, the dust-to-metals gradient is flatter than or equal to the metallicity gradient (assuming the gradients have the same sign). Similarly, if there is net dust growth in the ISM, then the dust-to-metals gradient is steeper than or equal to the metallicity gradient. The latter result implies that if dust gradients are steeper than metallicity gradients, that is, the dust-to-metals gradients are not flat, then it is unlikely dust destruction by SN shock waves is an efficient process, while dust growth must be a significant mechanism for dust production. Moreover, we conclude that dust-to-metals gradients can be used as a diagnostic for interstellar dust growth in galaxy discs, where a negative slope indicates dust growth.

Fractal grains have large surface area, which leads to more efficient condensation.The special limit case where the volume-area ratio is constant (correspondingto, e.g., a very rough grain surface or non-compacts aggregates) is particularly interesting,as well as convenient, from a mathematical point of view. If dust grains fromAGB stars have ‘rough surfaces’, it may have important implications for our understandingof dust and wind formation in AGB stars.

Fractal grains have a large surface area, which leads to more efficient condensation. The special limiting case where the volume-area ratio is constant (corresponding to, e.g., a very rough grain surface or non-compact aggregates) is particularly interesting, as well as convenient, from a mathematical point of view. If dust grains from AGB stars have 'rough surfaces,' that may have important implications for our understanding of dust and wind formation in AGB stars.

Understanding the photovoltaic (PV) power generation's temporal and spatial patterns is vital for grid balancing. This study aims to validate a simulation model for historical decentralized PV power generation, extending it to encompass the unique orientation of all PV systems within the Swedish municipality Knivsta. In a previous research project, a Convolutional Neural Network exhibited a 95% accuracy of identifying PV systems within Knivsta. In this project, using Light Detection and Ranging data, the orientation and area of detected PV systems was estimated. By combining this information with local weather and irradiance data, historical PV power generation was simulated. The regression analysis demonstrates strong correspondence between simulated and measured hourly generation for six reference systems, with coefficients of determination between 0.69–0.83. This study derives generic module parameters based on installation year and an average DC-to-AC ratio, enabling municipal-level simulations. Simulations for 2022, considering one scenario with optimal orientation for all PV systems and one scenario with derived real-condition orientations, reveal a smoothing effect in the daily pattern of aggregated PV generation, if considering real orientations. At the peak hour, power generation was found to be 10% lower when considering individual orientations compared to assuming optimal orientation across all facilities.

In this paper we investigate a fractional order logistic map and its discrete time dynamics. After a brief introduction to the discrete-time dynamical systems and fractional dynamics we show some basic properties of the fractional logistic map. We then move on to prove that the special case alpha = 1/2 exhibits a period doubling route to chaos. A bifurcation diagram for the special case of alpha = 1/2 is also included. Finally a discussion concerning the results and open problems is given.

Technological improvements along with falling prices on photovoltaic (PV) panels and electric vehicles (EVs) suggest that they might become more common in the future. The introduction of distributed PV power production and EV charging has a considerable impact on the power system, in particular at the end-user in the electricity grid.

In this PhD thesis PV power production, household electricity use and EV charging are investigated on different system levels. The methodologies used in this thesis are interdisciplinary but the main contributions are mathematical modeling, simulations and data analysis of these three components and their interactions. Models for estimating PV power production, household electricity use, EV charging and their combination are developed using data and stochastic modeling with Markov chains and probability distributions. Additionally, data on PV power production and EV charging from eight solar charging stations is analyzed.

Results show that the clear-sky index for PV power production applications can be modeled via a bimodal Normal probability distribution, that household electricity use can be modeled via either Weibull or Log-normal probability distributions and that EV charging can be modeled by Bernoulli probability distributions. Complete models of PV power production, household electricity use and EV home-charging are developed with both Markov chain and probability distribution modeling. It is also shown that EV home-charging can be modeled as an extension to the Widén Markov chain model for generating synthetic household electricity use patterns. Analysis of measurements from solar charging stations show a wide variety of EV charging patterns. Additionally an alternative approach to modeling the clear-sky index is introduced and shown to give a generalized Ångström equation relating solar irradiation to the duration of bright sunshine.

Analysis of the total power consumption/production patterns of PV power production, household electricity use and EV home-charging at the end-user in the grid highlights the dependency between the components, which quantifies the mismatch issue of distributed intermittent power production and consumption. At an aggregate level of households the level of mismatch is shown to be lower.

The use of open problems in engineering sciences is analysed from a case study perspective for a solar energy course with open problems in a computer lab and a written exam. The open problems are evaluated from the perspective of creative and critical thinking, and a set of guidelines for design of open ended questions is made.

John Nash var ett av de stora matematiska genierna, och hans liv var dramatiskt ända till slutet. Med filmen A Beautiful Mind blev han känd för den stora allmänheten. Innan dess hade han löst flera svåra matematiska problem, lagt grunden för den matematiska grenen spelteori och fått Nobelpriset. Men hans största utmaning i livet var schizofrenin - och den kampen vann han.

Life on Earth is threatened by asteroid impacts, supernovas, volcanic eruptions, ice ages, global warming and ecological disasters – all of which has almost eradicated life on Earth at several occasions in history. Through biological evolution life has risen in new forms after each global near-death experience. Now, in order to withstand the hostile environment of the universe, the process of evolution has forged a new weapon: mankind. With the power of thought and the ability to cooperate, humans have the potential to do something where previous creatures on Earth have failed: to improve conditions for life and to save it by spreading it to the cosmos. Mankind’s Universe stretches from the beginning of the universe, through the origin of life and the history of mankind to the future where we ourselves decide our fate. It is primarily directed to science enthusiasts and young adults with a scientific and technical oriented education. It can be used in educational purpose. 

Technological improvements and falling prices on photovoltaic panels andelectric vehicles suggest that they might become more common in future households.The introduction of a photovoltaic system and an electric vehiclehas considerable impact on the energy balance of a household. This licentiate thesis investigates the power consumption- and productionpatterns associated with the photovoltaic (PV) electricity production,household electricity consumption and home charged plug-in electric vehicle(PEV) electricity consumption. This investigation is carried out on both an individual and aggregate household level. The methodology used in this thesis is interdisciplinary but the maincontributions are mathematical modeling and simulations of the three main components. Theoretical estimates of electricityconsumption were constructed from extensions to the Wid\'{e}n Markov-chain model for generating synthetichousehold electricity use patterns. The main research contribution in thisthesis is the development and analysis of two extensions of this Markov-chain model: (I) Electricity use from a home charged PEV,(II) Flexibility of end-user power use. These two extensions were used in studiesregarding the coincidence - in particular the level of self-consumption - between PV electricityproduction and household electricity use. PV electricity production was modeledfrom high resolution solar irradiance data from the Ångström laboratory.\\Results show that the home charged PEV load would increase the household loadconsiderably. It was also shown that the level of correlation between PEV load and PVelectricity production was low, but that to some extent the PEV load could help increasethe self-consumption of PV power, both on individual and aggregate household level.\\The modeling and simulations of end-user flexibility showed that the householdload profile could be altered to a certain degree. It was also shown thatcertain flexibility setup could improve the self-consumption of PV power production, more so than theintroduction of a PEV.

Asteroidnedslag, supernovaexplosioner, vulkanutbrott, global uppvärmning och ekologiska katastrofer hotar oss alla och har nästan utrotat livet i flera omgångar genom dess historia. Med nöd och näppe har livet rest sig varje gång med hjälp av evolutionens kraft. Evolutionens senaste vapen mot universums ogästvänlighet är människan. Med tankekraft och samarbetsförmåga så har människan en potential att göra något evolutionen tidigare misslyckats med: rädda livet genom att sprida det vidare från jorden ut i kosmos.

Denna bok sträcker sig från universums början, genom livet och mänsklighetens historia till framtiden där vi själva bestämmer vårt öde. Boken riktas främst till vetenskapsintresserade och unga vuxna med naturvetenskaplig och teknisk inriktning.

In this paper we investigate household electricity use, electric vehicle (EV) home-charging and distributed photovoltaic (PV) power production in a case study for the city of Westminster, London. Since it is economically beneficial to maximize PV power self-consumption in the UK context the power consumption/production patterns with/without introducing EV home-charging on the household level is investigated. Additionally, since this might have an effect on the electricity use on an aggregate of households a large-scale introduction of EV charging and PV power production in the entire city of Westminster is also investigated. Household electricity consumption and EV home-charging are modeled with a Markov-chain model. PV power production is estimated from solar irradiation data from Meteonorm for the location of Westminster combined with a model for photovoltaic power production on tilted planes. The available rooftop area is estimated from the UK map geographic information database. EV home-charging increases the household electricity use mainly during evening with a maximum during winter whereas PV produces power during daytime with maximum during summer. On the household level this mismatch introduces variability in power consumption/production, which is shown to be less prominent for the large-scale scenario of the entire city of Westminster.

Devisen "lätt att göra rätt" ska gälla den nya stadsdelen. Djurgårdsstaden, som sakta håller på att växa fram. I projektet har studerats hur människor, solceller och elbilar ska fungera tillsammans.

Photovoltaic (PV) power production and residential power demand are negativelycorrelated at high latitudes on both annual and diurnal basis. If PVpenetration levels increase, methods to deal with power overproduction in the localdistribution grids are needed to avoid costly grid reinforcements. Increased local consumption isone such option. The introduction of a home-chargedplug-in electric vehicle (PEV) has a significant impact on the household load and potentiallychanges the coincidence between household load and photovoltaic power production.This paper uses a stochastic model to investigate the effect on the coincidence between householdload and photovoltaic power production when including a PEV load. The investigationis based on two system levels: (I) individual householdlevel and (II) aggregate household level. The stochastic model produces theoretical high-resolutionload profiles for household load and home charged PEV load over time.The photovoltaic power production model is based on high-resolution irradiance data for Uppsala, Sweden.It is shown that the introduction of a PEV improves the self-consumption of the photovoltaicpower both on an individual and an aggregate level, but the increase is limited due to thelow coincidence between the photovoltaic power production pattern and the charging patterns of the PEV.

Grid-connected photovoltaic (PV) parks are increasing in number and size. For local optimal battery control, electricity market participation and generally for delivering ancillary services to the grid from PV parks, it is important to be able to forecast PV park power generation. This study investigates short-term probabilistic forecasts and scenario-based forecasts on PV park clear-sky index for photovoltaics with two Markov-chain mixture distribution (MCM) models, Persistence Ensemble (PeEn) and Climatology. The models were trained on, and used to forecast, a 5 minute resolution data set of PV park power generation for two years from Vasakronan AB’s PV park in Uppsala, Sweden. The study shows that the MCM models outperform the PeEn and Climatology for five minute ahead forecasts in terms of continuous ranked probability score and in terms of point forecast MAE. It is also concluded that PeEn outperforms the Climatology, which despite lack of accuracy has highest similarity in result output. In terms of scenario-forecasting, where the two MCM models are compared to outputs from the Climatology, all models have similar CDF goodness-of-fit. In terms of autocorrelation, the MCM models are superior. Based on the results, the MCM model, regardless of setting, is recommended as advanced benchmark for very short-term probabilistic PV park power production forecasts.

We suggest a procedure for estimating Nth degree polynomial approximations to unknown (or known) probability density functions (PDFs) based on N statistical moments from each distribution. The procedure is based on the method of moments and is setup algorithmically to aid applicability and to ensure rigor in use. In order to show applicability, polynomial PDF approximations are obtained for the distribution families Normal, Log-Normal, Weibull as well as for a bimodal Weibull distribution and a data set of anonymized household electricity use. The results are compared with results for traditional PDF series expansion methods of Gram–Charlier type. It is concluded that this procedure is a comparatively simple procedure that could be used when traditional distribution families are not applicable or when polynomial expansions of probability distributions might be considered useful approximations. In particular this approach is practical for calculating convolutions of distributions, since such operations become integrals of polynomial expressions. Finally, in order to show an advanced applicability of the method, it is shown to be useful for approximating solutions to the Smoluchowski equation.

This paper presents a high-resolution bottom-up model of electricity use in an average household based on fit to probability distributions of a comprehensive high-resolution household electricity use data set for detached houses in Sweden. The distributions used in this paper are the Weibull distribution and the Log-Normal distribution. These fitted distributions are analyzed in terms of relative variation estimates of electricity use and standard deviation. It is concluded that the distributions have a reasonable overall goodness of fit both in terms of electricity use and standard deviation. A Kolmogorov-Smirnov test of goodness of fit is also provided. In addition to this, the model is extended to multiple households via convolution of individual electricity use profiles. With the use of the central limit theorem this is analytically extended to the general case of a large number of households. Finally a brief comparison with other models of probability distributions is made along with a discussion regarding the model and its applicability.