This paper presents two wave energy concepts for small-scale electricity generation. In the presented case, these concepts are installed on the buoy of a heaving, point-absorbing wave energy converter (WEC) for large scale electricity production. In the studied WEC, developed by Uppsala University, small-scale electricity generation in the buoy is needed to power a tidal compensating system designed to increase the performance of the WEC in areas with high tides. The two considered and modeled concepts are an oscillating water column (OWC) and a heaving point absorber. The results indicate that the OWC is too small for the task and does not produce enough energy. On the other hand, the results show that a hybrid system composed of a small heaving point absorber combined with a solar energy system would be able to provide a requested minimum power of around 37.7W on average year around. The WEC and solar panel complement each other, as the WEC produces enough energy by itself during wintertime (but not in the summer), while the solar panel produces enough energy in the summer (but not in the winter).
Net Zero Energy Buildings, Net ZEBs, is one of many necessary measures for climate change mitigation as they may reduce the energy consumption in the building sector. The Net ZEB interacts with a grid infrastructure. It is therefore important to consider the interaction with the grid in the design phase.
This paper reports an evaluation of a proposed designof a Net ZEB in the south of Sweden evaluating load matching and grid interaction using simulated datasets with hourly resolution. The aim was to find a design with as high load matching and as low grid interaction as possible.
The results show difficulties of achieving a high load matching between the building load and on-site generation, due to the Nordic climate and the relatively low loads during daytime, when the availability of solar energy is high. The building is likely to accomplish the goal of a Net ZEB balance. If higher flexibility is sought, a larger energy storage should be considered.
Due to rapid urbanisation and the significant contribution of cities to worldwide energy use and greenhouse gas emissions, urban energy system planning is growing more important. Urban building energy modelling (UBEM) draws increasing attention in the energy modelling field due to its inherent capacities for modelling entire cities or building stocks, and the potential of varying data inputs, approaches and applications. This review aims to identify best practices and improvements for UBEM applications by examining previous research, with a focus on the currently least established approaches. Different archetype development procedures are analysed for common problems, six main under-developed input approaches or parameters are identified, and applications for future scenario development are surveyed. By analysing previous studies in related fields, this paper provides an overview of gaps in the published research and possible additions to future UBEM projects that can help expanding the existing modelling procedures. Comprehensive human behaviour models with additional aspects beyond occupant presence are identified as a major point of interest. Further research on socio-economic parameters, such as household income and demographics, are also suggested to further improve modelling. This study also underlines the potential for utilising UBEM as a tool for evaluating future climate change scenarios.
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.
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.
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.
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.
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.
Along with sustainable urban development and energy efficien-cy initiatives, city-scale energy modelling of buildings has been receiving an increasing attention as a tool for planning and eval-uating future viable cities. However, due to the lack of software developed specifically for urban building energy modelling, computer-based modelling tools, previously applied for evalua-tion of the energy use and performance of single buildings, are widely utilized. The indoor climate and energy simulation tool IDA ICE, the transient system simulation software TRNSYS, and the building energy simulation programs EnergyPlus and VIP-Energy are some of the most commonly used examples of such tools. Although these simulation tools should in princi-ple be possible to use for large-scale applications, there is no comprehensive study that reflects on the modelling procedure, inputs, outputs and validity of these tools compare to each other.Thus, to investigate the capabilities of these tools for urban building energy simulations and to identify their advantages and disadvantages, in this study, a detailed energy model of a neighborhood including 32 district-heated buildings located in Sweden was developed in each tool. Hourly and annual simulation results were compared to each other and validated against thermal energy measurement data. The results of the study show that the simulated heat demand is a reasonable ap-proximation of the real one in all of the four tools. The annual deviation from the measured heat demand is +18 % for IDA ICE, +15 % for TRNSYS and about -13 % and -16 % for Ener-gyPlus and VIP-Energy, respectively. However, the simulated
results from EnergyPlus, TRNSYS and VIP-Energy indicated higher correlations with the hourly measurements compared to IDA ICE. The findings of this study suggest that EnergyPlus and TRNSYS are the most applicable tools for use in UBEM.
During recent years, urban building energy modeling has become known as a novel approach for identification, support and improvement of sustainable urban development initiatives and energy efficiency measures in cities. Urban building energy models draw the required information from the energy analysis of buildings in the urban context and suggest options for effective implementation of interventions. The growing interest in urban building energy models among researchers, urban designers and authorities has led to the development of a diversity of models and tools, evolving from physical to more advanced hybrid models. By critically analyzing the published research, this paper incorporates an updated overview of the field of urban building energy modeling and investigates possibilities, challenges and shortcomings, as well as an outlook for future improvements. The survey of previous studies identifies technical bottlenecks and legal barriers in access to data, systematic and inherent uncertainties as well as insufficient resources as the main obstacles. Furthermore, this study suggests that the main route to further improvements in urban building energy modeling is its integration with other urban models, such as climate and outdoor comfort models, energy system models and, in particular, mobility models.
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%.
Utility-scale solar photovoltaic (PV) parks have dominated the international market for the past few years. However, in some countries, like Sweden, utility-scale PV is on the verge to economic viability. Using existing infrastructure in a resource-efficient manner could be a crucial strategy for a successful implementation at scale. In this study, a new methodology for a utility-scale solar guide is developed by studying the hosting capacity in the local grid and identifying land appropriate for PV parks. The method is applied on a rural municipality in Sweden (512 km2) with a local distribution grid (5,000 customers). The impact on the grid, if connecting a PV park to a substation, was analyzed through power flow simulations and the geographical assessment was done using multi-criteria analysis with a Boolean approach. Three different sizes of PV parks, 1, 3, and 5 MWp, were analyzed. Results showed that 3.7% of the studied area is qualified for locating 1 MWp PV parks. However, if introducing a maximum distance threshold to the nearest substation that can host the PV generation from the park, the potential is further reduced (e.g., to 1% for a 750 m threshold). Furthermore, parts of the grid can host PV parks of 3 and 5 MWp, but only near urban areas, where qualified land is lacking. The results highlight that the proposed methodology can function as a tool in the dialog between utility companies, municipalities, PV companies, land-owners and other stakeholders in order to find resource- and system-efficient locations for PV parks.
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.
LiDAR (Light Detection and Ranging) data have recently gained popularity for use in solar resource assessment and solar photovoltaics (PV) suitability studies in the built environment due to robustness at identifying building orientation, roof tilt and shading. There is a disparity in the geographic coverage of low- and high-resolution LiDAR data (LL and LH, respectively) between rural and urban locations, as the cost of the latter is often not justified for rural areas where high PV penetrations often pose the greatest impact on the electricity distribution network. There is a need for a comparison of the different resolutions to assess capability of LL. In this study, we evaluated and improved upon a previously reported methodology that derives roof types from a LiDAR-derived, low-resolution Digital Surface Model (DSM) with a co-classing routine. Key improvements to the methodology include: co-classing routine adapted for raw LiDAR data, applicability to differing building type distribution in study area, building height and symmetry considerations, a vector-based shading analysis of building surfaces and the addition of solar resource assessment capability.
Based on the performance of different LiDAR resolutions within the developed model, a comparison between LL (0.5-1 pts/m(2)) and LH (6-8 pts/m(2)) LiDAR data was applied; LH can confidently be used to evaluate the applicability of LL, due to its significantly higher point density and therefore accuracy. We find that the co-classing methodology works satisfactory for LL for all types of building distributions. Roof-type identification errors from incorrect co-classing were rare (< 1%) with LL. Co-classing buildings using LL improves accuracy of roof-type identification in areas with homogeneous distribution of buildings, here from 78% to 86% in accuracy. Contrastingly, co-classing accuracy using LH is marginally reduced for all building distributions from 94.8% to 94.4%. We adapt the Hay and Davies solar transposition model to include shading. The shading analysis demonstrates similarity of results between LL and LH. We find that the proposed methodology can confidently be used for solar resource assessments on buildings when only LiDAR data of low-resolution (< 1 pts/m(2)) is available.
År 2040 ska 5-10 procent av den svenska elmixen komma från sol- energi. En stor del av dagens sol- cellsinstallationer är uppförda på byggnader och det är troligt att den trenden kommer att hålla i sig. Samtidigt omfattas en stor del av byggnadsbeståndet av olika lag- skydd på grund av kulturhistoriska värden vilket kräver särskild varsamhet när man installerar sol- celler på dessa byggnader. I ett nystartat forskningsprojekt kom- mer verktyg och riktlinjer tas fram som tar hänsyn till både de tekniska och arkitektoniska aspekterna av solcellsinstallationer på kulturhistoriskt värdefulla byggnader.
Solar energy applications have, in recent years, become a common element in the urban landscape, especially on roofs and facades. However, it is important that the integration of solar energy in the built environment do not distort the fabric or expression of the existing building envelope, not at least in areas of high cultural-heritage values. The aesthetics depend, to a large extent, on how visible the new technology, such as photovoltaic (PV) panels, is. This paper describes a method for visibility assessment of building envelopes. It is referred to as target-based as it, in contrast to previously reported methods, bases the assessment from the perspective of the building envelope itself, rather than possible vantage points on the ground. The method was evaluated for two Swedish cities; Stockholm and Visby. In Stockholm, each building was evaluated based on its cultural-heritage values, solar irradiation and visibility. Deploying PV only on the roofs with the lowest cultural-heritage values, with insolation > 900 kWh/m2, and with no visibility from ground, results in a total PV yield of up to 2% of the total electricity demand. In Visby, various definitions of the vantage area were evaluated, from which the building envelope can be seen. It was found that the choice of vantage area greatly impacts the solar energy potential. If the vantage area is defined by the public domain, i.e., streets and other public open spaces, the non-visible roof area doubles compared to if all ground/terrain defines it. Compared to previous studies, the use of a vantage area, instead of discrete vantage points, seems to result in higher visibility of the roofs.
Detailed simulations of large amounts of PV production in Swedish rural power grids show that as module and system prices keep declining and thus increasing the profitability and demand for solar power, current grid performance will limit the potential. Simulations have been made on a case distribution grid (10 kV) with actual hourly load data for 2014 and calculated hourly production with respect to building roof area, tilt and azimuth together with irradiation data. At high production, especially voltage rises along cables in the outer part of the grid is problematic, but also currents in cables close to transformer buses increases substantially at these conditions. Resulting hosting capacity for the case grid is 32%, as of annual production compared to annual demand. What is limiting the hosting capacity is the tolerated voltage rise, which is set to 5% of nominal grid voltage. Through smart allocation of PV systems to the strongest nodes in the grid the hosting capacity of the same grid can be increased to 74%.
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.
The impact of photovoltaics (PV) on the power system becomes increasingly important to study as the penetration of PV has increased rapidly over the last decade. A physical model for aggregated PV generation has been developed for the Swedish spot market areas. Information about PV systems within the Swedish electricity certificate system and irradiance data from the meteorological model STRÅNG were used as inputs. The model was trained and validated against production data reported to the Swedish transmission system operator. Our model shows high correlation (0.95-0.99) to reported historical production data. However, it overestimates extreme 1h ramp rates, which are -20% and 22% for down- and up-ramps respectively, compared to -13% and 14% for the reported data. Furthermore a weighting function was developed, which takes demography, available solar irradiance and today's PV deployment into account, to model likely deployment in a Swedish high penetration scenario, where PV covers 6% of the total annual power demand. The difference in extreme 1 and 4 hour step changes before and after introducing PV is small. The model could thus be used with confidence to model the impact on the power system for future scenarios of high PV penetration.
A Geographic Information System (GIS) is apowerful tool for studying the impact of building-applied PV systems on the power system. The grid operator can be advised on exactly which parts of the grid may suffer from problems with operational performance (e.g., over-voltages and over- currents). However, this requires the PV power generation to be realistically modelled, for which a crucial first step is to find representative building models. In this study, we compare the accuracy of two types of aerial data, LiDAR (Light Detection And Ranging) and photogrammetry data, and two methods for identifying building models from these data, a raster- based and a vector-based model. The results show that with photogrammetry data, the roof topology of buildings is not identified correctly as often as with LiDAR data, and the vector- based method gives a far better representation of the roofs than the raster-based method. We exemplify this by comparing the results of power-flow simulations on a distribution grid with about 5000 customers where PV systems have been deployed on the roofs according to the two different methods. For the raster-based method the PV power potential is almost four times higher than for the vector-based method, which overestimates impacts on the simulated performance of the grid. The conclusion of the study is therefore that for accurate simulations of the impact of building-applied PV on grid performance based on GIS data, the proposed vector-based method should be used, rather than the raster-based, and it should be based on LiDAR data rather than photogrammetry data.
Under de senaste åren har det skett en mycket snabb utbyggnad av solenergi i bebyggelsen i många länder, initialt i europeiska länder som Tyskland, Italien och Spanien men idag sker en snabb utveckling i de flesta länder i världen. I Sverige har användningen av solenergi tagit fart lite senare än i de södra grannländerna, mycket på grund av låga elkostnader från vatten- och kärnkraft. I samband med att investeringsstöd för solceller introducerades 2009 har dock solcellsmarknaden tagit fart om än från en låg nivå (se Figur 1). Från att solenergi framförallt nyttjats för uppvärmning eller för elgenerering vid fritidshuset eller båten sker nu installeringar av solcellsystem på framförallt villatak och lokaler i Sverige.För att möta både internationella såväl som lokala målsättningar att minskaklimatpåverkan är det viktigt att få en bild av potentialen för olika typer av förnybar energitillförsel. Den här studien är genomförd på uppdrag av Länsstyrelsen i Skåne med syfte att bedöma potentialen för solenergi på takytor inom hela länet, men även uppdelat på kommunnivå, både i närtid och med en längre tidshorisont. Utifrån trenden med sjunkande priser på solceller har potentialen för solvärme bedömts mycket liten, men kan delvis vara intressant för småhus.Potentialberäkningarna utgår från byggnadsbeståndet i fastighetskartan, uppdelat på olika byggnadstyper, där kvalificerade antaganden gjorts för vilken grad takytor för olika byggnadstyper kan utnyttjas för solenergi.Resultaten visar att det finns ungefär 105 km2 tillgänglig takyta för solenergi. Den realiserbara potentialen bedöms vara 3.1 TWh i scenario A samt 7.5 TWh i scenario B. Detta kan jämföras med elkonsumtionen i Skåne län som uppgick till 12.1 TWh 2014.
This paper investigates the realizable potential for photovoltaic (PV) systems in Swedish agriculture. Marginal lands and available building areas for PV systems are quantified, and factors limiting the potential are analyzed. It is shown that the potential for PV in Swedish agriculture is high, but what is fully realizable is limited by the capacity of the rural power grid. A case study in the rural municipality of Herrljunga was conducted and scaled to national level. The study shows that the risk of surges in the medium voltage grid (10 kV) in rural areas are small in case where all roof surfaces with an annual solar irradiance of over 950 kWh/m2 are used for solar power. The total electricity production from the Swedish agriculture, if all roof areas with this irradiance level were used, is estimated to 4 TWh annually. With solar power on all roof surfaces with an annual irradiance of at least 900 kWh per m2 problems with voltage rise and overloads in the electricity grid might occur. The electrical grid capacities thus substantially limit how much solar power can be installed. Our results also show that the profitability limits the potential to 0.2 TWh on a national level, but that it could increase if more optimistic economic conditions are assumed.
The study of solar irradiance is of vital importance in order to quantify the fluctuation that the transmission system needs to handle in case of large scale solar power integration. In this paper, early results from a low-cost solar irradiance logger network are presented. The main advantages of the solar logger are its low cost (~$50) and mobility. Our results confirm previous models of the station pair correlation of clear sky index step changes, with the modification that the isotropic correlation appears to decrease exponentially as a function of Δt-1/2 rather than Δt-1 as has been proposed earlier.
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.
With decreasing subsidies for PV systems, increased self-consumption of PV electricity could improve the profitability of grid-connected residential PV systems. Self-consumption is in this paper defined as the share of the PV production consumed in a building on an hourly basis. With higher prices for buying than selling electricity, the revenue due to self-consumption is higher than the profit of selling electricity to the grid. The focus of this paper is the potential to increase the self-consumption with alternative PV system layouts, i.e. several different azimuth and tilt angles, called 3DPV. Hourly data from an existing PV system on a detached house outside Västerås, Sweden, combined with meteorological and spot price data of electricity has been used, all from 2011. The results of one-year simulations show increased self-consumption and decreased PV production with 3DPV compared to a south-oriented PV system. The revenue decreases with 3DPV when using historical hourly spot market data. However, there are other benefits with 3DPV such as decreased rated power of the inverter due to lower PV peak production.
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.
The interest in self-consumption of PV electricity from grid-connected residential systems is increasing among PV system owners and in the scientific community. Self-consumption can be defined as the share of the total PV production directly consumed by the PV system owner. With decreased subsidies for PV electricity in several countries, increased self-consumption could raise the profit of PV systems and lower the stress on the electricity distribution grid. This review paper summarizes existing research on PV self-consumption and options to improve it. Two options for increased self-consumption are included, namely energy storage and load management, also called demand side management (DSM). Most of the papers examine PV-battery systems, sometimes combined with DSM. The results show that it is possible to increase the relative self-consumption by 13-24% points with a battery storage capacity of 0.5-1. kW. h per installed kW PV power and between 2% and 15% points with DSM, both compared to the original rate of self-consumption. The total number of papers is however rather limited and further research and more comparative studies are needed to give a comprehensive view of the technologies and their potential. Behavioral responses to PV self-consumption and the impact on the distribution grid also need to be further studied.
This paper aims to visualize how the planned electrification of heat demand together with the utilization of energy flexibility in buildings will influence the performance of local electricity network. Thereby, the work contributes to the research on smart energy system in the residential sector. The flexibility service is provided by two demand-side-management strategies a) activation of the thermal mass to modulate load profile of a heat pump, b) control of household appliances' starting times. Three configurations of load mix in the local electricity network are investigated: a) domination of non-renovated houses, b) with equal share of high and low heat demand houses, c) domination of energy efficient houses. The model is soft-coupled and anchored in existing low voltage (LV) network and existing residential buildings. The energy flexible buildings enhance the LV network performance, by decreasing the afternoon peaks, without compromising the occupants' thermal comfort. The highest impact is for the LV network dominated by energy efficient houses. There are also new challenges, namely the newly created peak loads and transformer overloading during night time. It is a consequence of uniform price signal sent to all flexible customers and electrification of heating demand without parallel improvement of energy performance.
With an increased ambition of implementing renewable electricity production in our energy systems follows the need of handling the inherent variability from some of these production sources (e.g. wind and solar). This could be via curtailments, infrastructural reinforcements of the power grid, and/or increased utilization of power system reserves. The aim of this study was to investigate if power surplus and deficit due to mismatch between intermittent power generation and power demand could be reduced with electric heat pumps (used for power-to-heat purposes), combined heat and power (CHP) production (for power balancing), and seasonal thermal energy storage (STES) (as buffering capacity). A residential area consisting of buildings refurbished for improved energy performance, roof top solar photovoltaic (PV) systems, a local heat distribution system, a small-scale CHP plant, central heat pumps, and a STES, was simulated. The heat pumps were given priority to use surplus power from roof top PV generation or surplus from the grid (e.g. wind power). The CHP plant produced power during power deficits. Surplus heat from the CHP plant as well as from the heat pumps was stored in the STES. The results showed a reduction of the surplus power from the local PV systems towards the upstream power grid. Also, the possibility to offer regulative service towards upstream power grid by using CHP was demonstrated. The conclusion is that power-to-heat and CHP can significantly reduce the mismatch between variable power generation and power demand.