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Energy communities are emerging across Europe, and each country is currently in the process of forming regulations for their integration into the electricity grid. The efficacy of energy communities depends upon various factors, including member demographics, technological aspects, load profiles, solar irradiation, and spot prices within the community’s geographical location. Notably, existing studies on energy communities predominantly focus on residential load profiles, with limited exploration into their impact on the distribution grid. This article aims to contribute to the existing literature by investigating the benefits of energy communities and their grid impact under diverse member configurations. Our approach involves the development of an optimisation model incorporating battery energy storage and shiftable loads, aimed at minimising the operational costs of energy communities over a one-year period. Case studies in Norway and Spain, with different load configurations: residential, commercial, and mixed load, are undertaken, utilising real hourly measurements to identify operational variations influenced by geographical location and seasonal fluctuations in load and photovoltaic (PV) generation. Additionally, we quantify the costs, CO2 emissions, and self-consumption rates for energy communities. Furthermore, we assess the distribution grid impact in terms of import and export dynamics. The results underscore the substantial influence of load configurations on member benefits and distribution grid impacts, attributable to the inherent correlation between load and PV generation. In the context of energy community benefits, commercial loads demonstrate the best outcomes in Norway, whereas residential loads exhibit superior results in Spain. Conversely, concerning distribution grid impact, commercial loads prove most advantageous in Norway, while mixed loads yield the best results in Spain. Overall, our findings indicate that Spanish energy communities consi, The authors would like to thank Harald T. Walnum, Synne K. Lien, Karen B. Lindberg and Igor Sartori for access to and discussions regarding the PROFet tool. We also thank Ida Fuchs for discussions regarding PV modelling and Marte F. Dynge for proofreading. This work was supported by the project consortium of the research project FINE (Flexible Integration of Local Energy Communities into the Norwegian Electricity Distribution System), financed by the Research Council of Norway [project number 308833]., Peer Reviewed, Postprint (published version)

This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at https://github.com/s-guenther/estss and https://zenodo.org/records/10213145 .

Successful energy system planning is dependent on detailed electricity demand information. Especially in developing countries, pre-generated load profiles are often unsuitable as appliance ownership and usage vary significantly across borders, between urban and rural areas, and on household and industry levels. Synthesizing load profiles is often hindered by the inaccessibility of tools due to cost barriers, global unavailability, or required technical knowledge. As currently, no easily accessible and usable tool is available during energy system planning in rural areas of developing countries, we incorporate the open-source load profile generator RAMP into our web-based energy system simulator NESSI4Dweb+ to provide an intuitive user interface. We conduct an applicability check with self-collected data from a guesthouse in Sri Lanka, analyzing the impact of load distribution and magnitude on the economic, environmental, and reliable energy supply, that validates the artifact's relevance and ability to empower local decision-makers.

The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results. Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies.

This thesis explored different approaches of clustering residential data. The goal was to develop a model with applications in load forecasting contexts, specifically in situations where only a limited amount of residential data is available. Four different types of approaches were explored, one of which utilised not only data pertaining to the load profile but also data related to the residency. Effects of seasonal and weekly variations were studied to identify how the load profiles were affected by these parameters. In the end the developed clusters were evaluated using silhouette scores as well as using load forecasting models developed outside of the thesis.

This thesis explored different approaches of clustering residential data. The goal was to develop a model with applications in load forecasting contexts, specifically in situations where only a limited amount of residential data is available. Four different types of approaches were explored, one of which utilised not only data pertaining to the load profile but also data related to the residency. Effects of seasonal and weekly variations were studied to identify how the load profiles were affected by these parameters. In the end the developed clusters were evaluated using silhouette scores as well as using load forecasting models developed outside of the thesis.

Electrification of mobility is paving the way in decreasing emissions from the transport sector; nevertheless, to achieve a more sustainable and inclusive transport system, effective and long-term planning of electric vehicles charging infrastructure will be crucial. Developing an infrastructure that supports the substitution of the internal combustion engine and societal needs is no easy feat; different modes of transport and networks require specific analyses to match the requirements of the users and the capabilities of the power grid. In order to outline best practices and guidelines for a cost-effective and holistic charging infrastructure planning process, the authors have evaluated all the aspects and factors along the charging infrastructure planning cycle, analysing different methodological approaches from scientific literature over the last few years. The review starts with target identification (including transport networks, modes of transport, charging technologies implemented, and candidate sites), second, the data acquisition process (detailing data types sources and data processing), and finally, modelling, allocation, and sizing methodologies. The investigation results in a decision support tool to plan high-power charging infrastructure for electric vehicles, taking into account the interests of all the stakeholders involved in the infrastructure investment and the mobility value chain (distributed system operators, final users, and service providers, Peer Reviewed, Postprint (published version)

Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network architecture, which is challenging due to the highly variable nature of such profiles. Generated contextual load profiles were conditioned on the month of the year and typical power exchange with the grid. Moreover, the quality of generations was both visually and statistically evaluated. The experimental results demonstrate our proposed CVAE model can capture temporal features of historical load profiles and generate ‘realistic’ data with satisfying univariate distributions and multivariate dependencies., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Electrical Power Grids

Recently, there is increasing interest in detecting electricity thieves for economic benefits for power companies, and many works aim to improve the accuracy of electricity theft detection. Nevertheless, a core obstacle that currently hinders the direct comparison of classifiers for electricity theft detection is the lack of a standard and public dataset, since fraudulent power load profiles are usually difficult to collect for various reasons, including cost, cumber, and confidentiality. Therefore, this paper presents an open-source toolbox, which generates different kinds of fraudulent power load profiles from attack models, and integrates classical classifiers (e.g., support vector machine, multi-layer perceptron, convolutional neural network, long short-term memory, bidirectional long short-term memory) with different performance as baselines for the comparison with new algorithms. Users can easily generate datasets and modify parameters of classical classifiers guided by user friendly interactive interfaces. The codes, toolbox, and user manuals are available online and it is free to use and extend them., Part of proceedings: ISBN 978-1-6654-0064-0QC 20220816

Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications., Information and Communication Technology

Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications., Information and Communication Technology

This thesis explores the use of machine learning as a load classifier in a distribution grid based on the daily consumption behaviour of roughly 1600 loads spread throughout the areas Bromma, Hässelby and Vällingby in Stockholm, Sweden. Two common unsupervised learning methods were used for this, K-means clustering and hierarchical agglomerative clustering (HAC), the performance of which was analysed with different input data sets and parameters. K-means and HAC were unfortunately difficult to compare and there were also some difficulties in finding a suitable number of clusters K with the used input data. This issue was resolved by evaluating the clustering outcome with custom loss function MSE-tot that compared created clusters with subsequent assignment of new data. The loss function MSE-tot indicates that K-means is more suitable than HAC in this particular clustering setup. To investigate how the obtained clusters could be used in practice, two K-means clustering models were also used to perform some cluster-specific peak load predictions. These predictions were done using unitless load profiles created from the mean properties of each cluster and dimensioned using load specific parameters. The developed models had a mean relative error of approximately 8-19 % per load, depending on the prediction method and which of the two clustering models that was used. This result is quite promising, especially since deviations above 20 % were not uncommon in previous work. The models gave poor predictions for some clusters, however, which indicates that the models may not be suitable to use on all kinds of load data in its current form. One suggestion for how to further improve the predictions is to add more explanatory variables, for example the temperature dependence. The result of the developed models were also compared to the conventionally used Velander's formula, which makes predictions based on the loads' facility-type and annual electricity consumption. Velander's

This thesis explores the use of machine learning as a load classifier in a distribution grid based on the daily consumption behaviour of roughly 1600 loads spread throughout the areas Bromma, Hässelby and Vällingby in Stockholm, Sweden. Two common unsupervised learning methods were used for this, K-means clustering and hierarchical agglomerative clustering (HAC), the performance of which was analysed with different input data sets and parameters. K-means and HAC were unfortunately difficult to compare and there were also some difficulties in finding a suitable number of clusters K with the used input data. This issue was resolved by evaluating the clustering outcome with custom loss function MSE-tot that compared created clusters with subsequent assignment of new data. The loss function MSE-tot indicates that K-means is more suitable than HAC in this particular clustering setup. To investigate how the obtained clusters could be used in practice, two K-means clustering models were also used to perform some cluster-specific peak load predictions. These predictions were done using unitless load profiles created from the mean properties of each cluster and dimensioned using load specific parameters. The developed models had a mean relative error of approximately 8-19 % per load, depending on the prediction method and which of the two clustering models that was used. This result is quite promising, especially since deviations above 20 % were not uncommon in previous work. The models gave poor predictions for some clusters, however, which indicates that the models may not be suitable to use on all kinds of load data in its current form. One suggestion for how to further improve the predictions is to add more explanatory variables, for example the temperature dependence. The result of the developed models were also compared to the conventionally used Velander's formula, which makes predictions based on the loads' facility-type and annual electricity consumption. Velander's

In the residential sector, grid parity has been reached or is close to be reached in many countries, which means that the cost of electricity from residential photovoltaic installations is equal to or less than the retail electricity price. However, not all installations are equally profitable. The objective of this paper is to analyze the economic performance of residential self-consumption systems, particularly the effect of electric load profiles in regard to that performance. For this purpose, a simulation model is proposed to perform a comparative analysis of the profitability of different configurations of a grid-connected, residential solar system. The model is applied to several differentiated load profiles, as well as to different configurations of photovoltaic panels and batteries. The analysis shows the impact of different load profiles on self-consumption, autarchy and, ultimately, the grid parity of the installation., Postprint (author's final draft)

[EN] The deployment of Advanced Metering Infrastructure (AMI) is providing to utilities large amounts of energy consumption data from their customers, in form of daily load profiles with energy consumed per hour or a smaller period. These data can yield valuable results when analyzed, in order to extract useful knowledge about the typical patterns of consumption of energy from the customers. The proper mechanisms and tools have to be developed and implemented for this objective. Big Data and Big Data Analytics systems will contribute to analyze this information and help to extract knowledge from the data, summarized in form of patterns or other mining knowledge, that will aid experts in decision support. In the present work a classification of customers based on their temporal load profiles is proposed. This classification procedure could be implemented in the current Big Data Analytics software systems, providing an added value to their statistical analysis options. Previous works in the literature present algorithms that allow to classify load profiles from customers by processing batch datasets and obtaining static patterns of load profiles. The proposed technique allows to analyze patterns not only in shape but also in their evolution or trend of energy consumption at each hour of the day through time. Specific quantitative indicators that characterize the patterns (and the consumers associated to them) are described and tested for this purpose.

[EN] The deployment of Advanced Metering Infrastructure (AMI) is providing to utilities large amounts of energy consumption data from their customers, in form of daily load profiles with energy consumed per hour or a smaller period. These data can yield valuable results when analyzed, in order to extract useful knowledge about the typical patterns of consumption of energy from the customers. The proper mechanisms and tools have to be developed and implemented for this objective. Big Data and Big Data Analytics systems will contribute to analyze this information and help to extract knowledge from the data, summarized in form of patterns or other mining knowledge, that will aid experts in decision support. In the present work a classification of customers based on their temporal load profiles is proposed. This classification procedure could be implemented in the current Big Data Analytics software systems, providing an added value to their statistical analysis options. Previous works in the literature present algorithms that allow to classify load profiles from customers by processing batch datasets and obtaining static patterns of load profiles. The proposed technique allows to analyze patterns not only in shape but also in their evolution or trend of energy consumption at each hour of the day through time. Specific quantitative indicators that characterize the patterns (and the consumers associated to them) are described and tested for this purpose.

In the energy sector a transition towards smart grid is now taking place as a step towards a sustainable energy distribution. In addition to many other solutions, this transition will depend upon extended measurements and data management to increase the knowledge about load flows (i.e electricity use) in the network. This thesis will concentrate on data management and statistical analysis of measurements based on a smart grid project in Hyllie Malm¨o’s largest development area with extensive environmental goals. The purpose of this thesis is to develop models for describing and forecasting the load in secondary substations as accurate as possible. In order to fulfill this purpose, measurements have been collected from secondary substationsinHyllie. Forcomparison,dataisalsocollectedfromFigeholm,wheremeasuringhas been going on for a longer time. Together with weather data from SMHI, the load data has beenusedtocreateastatisticalmodel-ageneralizedadditivemodel(GAM).GAMisatype ofregressionmodeldescribingtheload(activepower)inthesecondarysubstationbasedon a number of explanatory variables. These parameters are mainly weather variables, such as temperature and wind speed, and calendar variables; as time of day, day of week and time of year. The models also take into account the load and temperature of the days before, by including the lagged values as explanatory parameters. The data from Figeholm has been used for better detection of the annual pattern, since this data covers a whole year. All of the models show a significant relation between the load and the time of the day as well as day of week. For the stations in Figeholm a distinct annual pattern is also visible. This is not as pronounced for Hyllie, due to the shorter measure period. Furthermore, the laggedvaluesalsoseemtohaveinfluenceontheload. Consideringtheweatherdependence, all stations show a significant relation between the load and the temperature. For the Hyllie stations,therealsoexistarelationbetweentheloadand

In the energy sector a transition towards smart grid is now taking place as a step towards a sustainable energy distribution. In addition to many other solutions, this transition will depend upon extended measurements and data management to increase the knowledge about load flows (i.e electricity use) in the network. This thesis will concentrate on data management and statistical analysis of measurements based on a smart grid project in Hyllie Malm¨o’s largest development area with extensive environmental goals. The purpose of this thesis is to develop models for describing and forecasting the load in secondary substations as accurate as possible. In order to fulfill this purpose, measurements have been collected from secondary substationsinHyllie. Forcomparison,dataisalsocollectedfromFigeholm,wheremeasuringhas been going on for a longer time. Together with weather data from SMHI, the load data has beenusedtocreateastatisticalmodel-ageneralizedadditivemodel(GAM).GAMisatype ofregressionmodeldescribingtheload(activepower)inthesecondarysubstationbasedon a number of explanatory variables. These parameters are mainly weather variables, such as temperature and wind speed, and calendar variables; as time of day, day of week and time of year. The models also take into account the load and temperature of the days before, by including the lagged values as explanatory parameters. The data from Figeholm has been used for better detection of the annual pattern, since this data covers a whole year. All of the models show a significant relation between the load and the time of the day as well as day of week. For the stations in Figeholm a distinct annual pattern is also visible. This is not as pronounced for Hyllie, due to the shorter measure period. Furthermore, the laggedvaluesalsoseemtohaveinfluenceontheload. Consideringtheweatherdependence, all stations show a significant relation between the load and the temperature. For the Hyllie stations,therealsoexistarelationbetweentheloadand

Buildings are a key source of energy flexibility due to their high energy demand. Harnessing the energy flexibility of buildings, however, demands that buildings be considered collectively. This paper presents preliminary results to discover building's energy flexibility, from two different neighborhoods in the Netherlands. The energy demand profiles of three large buildings in each neighborhood are analyzed to identify possible useful simultaneous heating and cooling loads. Finally, the possibility for energy exchange between these buildings is explored.

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en samman, QC 20160329

Zero Energy Buildings (ZEBs) are considered as one of the key elements to meet the Energy Strategy of the European Union. This paper investigates cost-optimal solutions for the energy system design in a ZEB and the subsequent grid impact. We use a Mixed Integer Linear (MILP) optimisation model that simultaneously optimises the building's energy system design and the hourly operation. As a ZEB have onsite energy generation to compensate for the energy consumption, it is both importing and exporting electricity. The hourly time resolution identifies the factors that influence this import/export situation, also known as the building's grid impact. An extensive case study of a multi-family house in Germany is performed. The findings show that the energy system design and the grid impact greatly depend on the ZEB definition, the existing policy instruments and on the current energy market conditions. The results indicate that due to the feed-in-tariff for PV, the cost-optimal energy design is fossil fuelled CHP combined with a large PV capacity, which causes large grid impacts. Further, we find that heat pumps are not a cost-optimal choice, even with lower electricity prices or with increased renewables in the electric power system., QC 20161012

According to EU's Energy Performance of Buildings Directive (EPBD), all new buildings shall be nearly Zero Energy Buildings (ZEB) from 2018/2020. How the ZEB requirement is defined has large implications for the choice of energy technology when considering both cost and environmental issues. This paper presents a methodology for determining ZEB buildings' cost optimal energy system design seen from the building owner's perspective. The added value of this work is the inclusion of peak load tariffs and feed-in tariffs, the facilitation of load shifting by use of a thermal storage, along with the integrated optimisation of the investment and operation of the energy technologies. The model allows for detailed understanding of the hourly operation of the building, and how the ZEB interacts with the electricity grid through the characteristics of its net electric load profile. The modelling framework can be adapted to fit individual countries' ZEB definitions. The findings are important for policy makers as they identify how subsidies and EPBD's regulations influence the preferred energy technology choice, which subsequently determines its grid interaction. A case study of a Norwegian school building shows that the heat technology is altered from HP to bio boiler when the ZEB requirement is applied., QC 20161012

In 2013 83% of energy in-the German residential sector is used for the preparation of domestic hot water (13%) and space heating (70%). Thermal demand profiles are essential to correctly determine operation and sizing of heating technologies. In this work, the stochastic bottom-up approach for electric loads is extended to cover domestic hot water (DHW) and space heating demands. The approach is presented for individual buildings and residential areas, validated and compared to currently used approaches. A behavioural model is used to determine DHW tappings, electric appliance use and temperature settings of the building. Building heat load is calculated using a simplified physical model, to allow for realistic energy demand profiles, efficient model parametrisation and fast computation. A randomisation approach for building heat load based on a clustered building typology, a variation of building parameters and heating settings is presented which allows the simulation of larger quantities of similar buildings. Validation against measured data for German single family houses shows a correlation of the typical daily load profile for DHW consumption of 0.92 and a mean relative error of 3% and for space heating 0.89 and 9% respectively., QC 20160721

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en samman, QC 20160329

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en samman, QC 20160329

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en samman, QC 20160329

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en samman, QC 20160329

Todays change of technical equipment in private households leads to altered domestic electric load profiles. This work investigates the change of residential electric load profiles when introducing PV, Air-Source-Heat-Pumps, CHP units and electric vehicles (EV). A modular modelling approach is presented, which allows for application on a larger scale, e.g. for simulations of distribution grid or gas and district heating network in residential areas. Results of a one year simulation for a representative German household show that load peaks can vary heavily between 4.05 kW (PV being present) and 24.67 kW (EV being present), i.e. depending on the technology present in a household. The net yearly elctricity demand varies from -1 094 kWh (PV being present) to 14 936 kWh (ASHP being present). This information should be considered when discussing gird planing, energy supply and DSM concepts for residential areas., QC 20160205

Approximately 27% of the European energy consumption is caused by the domestic sector, where 19% of the end use energy demand is caused by electric devices. To investigate the factors at play, a stochastic bottom-up model for the generation of electric load profiles is introduced in this paper. The model is designed for investigating the effects of occupant behaviour, appliance stock and efficiency on the electric load profile of an individual household. For each activity of a person in the household, an electric appliance is used, and its electricity consumption is linked to measured electric load traces with a time resolution of 10 s. Probability distributions are incorporated for when and how often an appliance is operated. Duration of operation is given as probability density conditional on the start time. Shared use of an appliance by multiple persons is included in the model. Seasonal effects are considered by using changing probability sets during the course of the year. For validation, seven subgroups, which reflect typical household configurations, were formed and tested against measured field data from 430 households in 9 different cities across Germany. The results showed an accuracy of 91% and a correlation of up to 0.98., QC 20150507

Electric vehicles (EVs) may soon enter the vehicle market in large numbers and change the overall fuel usage within the passenger transport sector. With increased variable consumption from EVs together with anticipated increased production from variable sources, due to renewable wind and solar power, also the balancing of the electric power system incur increased attention. This emphasizes the importance of developing models to estimate and investigate the stochasticity of personal car travel behavior and induced EV charging load. Several studies have been made in order to model the stochasticity of passenger car travel behavior but none have captured the charging behavior dependence of the type-of-trip conducted. This paper proposes a new model for plug-in-hybrid electric vehicle (PHEV) utilization and recharging price sensitivity, to determine charging load profiles based on driving patterns due to the type-of-trip and corresponding charging need. This approach makes it possible to relate the type-of-trip with the consumption level, the parking location, and the charging opportunity. The proposed model is applied in a case study using Swedish car travel data. The results show the charging load impact and variation due to the stochastic PHEV type-of-trip mobility, allowing quantification of the PHEV charging impact on the system., QC 20140211. Updated from accepted to published.

The purpose of this paper is to describe a new and innovative method for the calculation of societal consequences from breakdowns in critical infrastructures. Both a Social Impact Cost is calculated as lost Gross Domestic Product and non-economic consequences are quantified. For the non-economic consequences a new measure is introduced called Social Impact Magnitude that resembles the Richter scale. The paper describes the methods used and the design of the software tool developed for this purpose. The second part of the paper includes some practical examples how the societal consequences have been calculated for a number of power outage scenarios. Future potential development to cover other types of critical infrastructures and dependencies between infrastructures are indicated at the end., QC 20130111

This report describes the impact of future residential loads and generators on the loading of electricity grids by applying a new load modelling technique to a large set of existing medium voltage (MV) networks. Different future scenarios with resulting load profiles are tested on the MV networks., Electrical Power Engineering, Electrical Sustainable Energy | Electrical Power Systems, Electrical Engineering, Mathematics and Computer Science

This report describes the impact of future residential loads and generators on the loading of electricity grids by applying a new load modelling technique to a large set of existing medium voltage (MV) networks. Different future scenarios with resulting load profiles are tested on the MV networks., Electrical Power Engineering, Electrical Sustainable Energy | Electrical Power Systems, Electrical Engineering, Mathematics and Computer Science

A new generalised sizing method for photovoltaic hybrid system (PVHS) based on desired annual generator hours has been proposed and tested with varying monthly energy demands (M1) and a constant daily profile of 72.6 kWh/d (M2). The PVHS options were determined using these load profiles and the monthly energy outputs of PV modules that have been computed for Garoua situated in north Cameroon. The results obtained showed that the PV array sizes computed with M1 were higher that those computed with M2 at renewable energy fractions greater than 84.6%. Power options with PV array sizes in the ranges 9.0 - 24.12 kWp and 9.0 - 21.96 kWp were found for M1 and M2 respectively and the renewable energy fractions were in the range 52 - 100%. The annual generator hours were in the ranges 0 - 1784 hours and 0 - 1787 hours for M1 and M2 respectively. The PV array sizes for a renewable energy fraction of about 60% was found to be 41.8% and 45.9% of the PV size required in standalone options for M1 and M2 respectively. This new method can be used for the sizing of PV hybrid systems irrespective of the monthly energy demand profiles.

Time-use data, describing in detail the everyday life of household members as high-resolved activity sequences, have a largely unrealized potential of contributing to domestic energy demand modelling. A model for computation of daily electricity and hot-water demand profiles from time-use data was developed, using simple conversion schemes, mean appliance and water-tap data and general daylight availability distributions. Validation against detailed, end-use specific electricity measurements in a small sample of households reveals that the model for household electricity reproduces hourly load patterns with preservation of important qualitative features. The output from the model, when applied to a large data set of time use in Sweden, also shows correspondence to aggregate profiles for both household electricity and hot water from recent Swedish measurement surveys. Deviations on individual household level are predominantly due to occasionally ill-reported time-use data and on aggregate population level due to slightly non-representative samples. Future uses and developments are identified and it is suggested that modelling energy use from time-use data could be an alternative, or a complement, to energy demand measurements in households.

Research in solar heating has traditionally focused solely on increasing the system efficiency by improving the technical components. In this thesis the technical methodology and system boundaries are widened to connect the technical aspects with market actors that are highly influential on the implementation of solar technology. The research was focused on how social aspects can be brought into technical studies to improve the understanding of solar heating, and how solar thermal technology can be optimized in a larger energy system. Both heat storage and different system solutions have been investigated. The thesis is built on a number of sub-projects exploring different aspects of solar heating. Improved components and system configurations may result in higher fractional energy savings and thereby make solar energy go from a marginal contribution to be the main energy supplier. Both components and systems are considered in this thesis. The solar heating technology has been shown to work well, also in unique system solutions. Technical possibilities with medium-sized stores for single-family houses and seasonal stores for residential areas are presented. Methods to bring studies of technology and actor studies together are also proposed; domestic hot water use has been modelled based on time-use data, while a multifaceted market situation, in which new system solutions must find their way, has been described by the solar and pellet industries. The complexity of assessing installation and use of a particular heating system in relation to the overall energy system is also discussed. Overall, this thesis shows that successful use of solar heating does not only come down to proper technical solutions, but also depends on the interaction between technology and market actors. A widened perspective, including the social context in which the heating system appears, is then essential. This thesis constitutes a step in that direction.

Research in solar heating has traditionally focused solely on increasing the system efficiency by improving the technical components. In this thesis the technical methodology and system boundaries are widened to connect the technical aspects with market actors that are highly influential on the implementation of solar technology. The research was focused on how social aspects can be brought into technical studies to improve the understanding of solar heating, and how solar thermal technology can be optimized in a larger energy system. Both heat storage and different system solutions have been investigated. The thesis is built on a number of sub-projects exploring different aspects of solar heating. Improved components and system configurations may result in higher fractional energy savings and thereby make solar energy go from a marginal contribution to be the main energy supplier. Both components and systems are considered in this thesis. The solar heating technology has been shown to work well, also in unique system solutions. Technical possibilities with medium-sized stores for single-family houses and seasonal stores for residential areas are presented. Methods to bring studies of technology and actor studies together are also proposed; domestic hot water use has been modelled based on time-use data, while a multifaceted market situation, in which new system solutions must find their way, has been described by the solar and pellet industries. The complexity of assessing installation and use of a particular heating system in relation to the overall energy system is also discussed. Overall, this thesis shows that successful use of solar heating does not only come down to proper technical solutions, but also depends on the interaction between technology and market actors. A widened perspective, including the social context in which the heating system appears, is then essential. This thesis constitutes a step in that direction.

Research in solar heating has traditionally focused solely on increasing the system efficiency by improving the technical components. In this thesis the technical methodology and system boundaries are widened to connect the technical aspects with market actors that are highly influential on the implementation of solar technology. The research was focused on how social aspects can be brought into technical studies to improve the understanding of solar heating, and how solar thermal technology can be optimized in a larger energy system. Both heat storage and different system solutions have been investigated. The thesis is built on a number of sub-projects exploring different aspects of solar heating. Improved components and system configurations may result in higher fractional energy savings and thereby make solar energy go from a marginal contribution to be the main energy supplier. Both components and systems are considered in this thesis. The solar heating technology has been shown to work well, also in unique system solutions. Technical possibilities with medium-sized stores for single-family houses and seasonal stores for residential areas are presented. Methods to bring studies of technology and actor studies together are also proposed; domestic hot water use has been modelled based on time-use data, while a multifaceted market situation, in which new system solutions must find their way, has been described by the solar and pellet industries. The complexity of assessing installation and use of a particular heating system in relation to the overall energy system is also discussed. Overall, this thesis shows that successful use of solar heating does not only come down to proper technical solutions, but also depends on the interaction between technology and market actors. A widened perspective, including the social context in which the heating system appears, is then essential. This thesis constitutes a step in that direction.

Research in solar heating has traditionally focused solely on increasing the system efficiency by improving the technical components. In this thesis the technical methodology and system boundaries are widened to connect the technical aspects with market actors that are highly influential on the implementation of solar technology. The research was focused on how social aspects can be brought into technical studies to improve the understanding of solar heating, and how solar thermal technology can be optimized in a larger energy system. Both heat storage and different system solutions have been investigated. The thesis is built on a number of sub-projects exploring different aspects of solar heating. Improved components and system configurations may result in higher fractional energy savings and thereby make solar energy go from a marginal contribution to be the main energy supplier. Both components and systems are considered in this thesis. The solar heating technology has been shown to work well, also in unique system solutions. Technical possibilities with medium-sized stores for single-family houses and seasonal stores for residential areas are presented. Methods to bring studies of technology and actor studies together are also proposed; domestic hot water use has been modelled based on time-use data, while a multifaceted market situation, in which new system solutions must find their way, has been described by the solar and pellet industries. The complexity of assessing installation and use of a particular heating system in relation to the overall energy system is also discussed. Overall, this thesis shows that successful use of solar heating does not only come down to proper technical solutions, but also depends on the interaction between technology and market actors. A widened perspective, including the social context in which the heating system appears, is then essential. This thesis constitutes a step in that direction.

Time-use data, describing in detail the everyday life of household members as high-resolved activity sequences, have a largely unrealized potential of contributing to domestic energy demand modelling. A model for computation of daily electricity and hot-water demand profiles from time-use data was developed, using simple conversion schemes, mean appliance and water-tap data and general daylight availability distributions. Validation against detailed, end-use specific electricity measurements in a small sample of households reveals that the model for household electricity reproduces hourly load patterns with preservation of important qualitative features. The output from the model, when applied to a large data set of time use in Sweden, also shows correspondence to aggregate profiles for both household electricity and hot water from recent Swedish measurement surveys. Deviations on individual household level are predominantly due to occasionally ill-reported time-use data and on aggregate population level due to slightly non-representative samples. Future uses and developments are identified and it is suggested that modelling energy use from time-use data could be an alternative, or a complement, to energy demand measurements in households.

Research in solar heating has traditionally focused solely on increasing the system efficiency by improving the technical components. In this thesis the technical methodology and system boundaries are widened to connect the technical aspects with market actors that are highly influential on the implementation of solar technology. The research was focused on how social aspects can be brought into technical studies to improve the understanding of solar heating, and how solar thermal technology can be optimized in a larger energy system. Both heat storage and different system solutions have been investigated. The thesis is built on a number of sub-projects exploring different aspects of solar heating. Improved components and system configurations may result in higher fractional energy savings and thereby make solar energy go from a marginal contribution to be the main energy supplier. Both components and systems are considered in this thesis. The solar heating technology has been shown to work well, also in unique system solutions. Technical possibilities with medium-sized stores for single-family houses and seasonal stores for residential areas are presented. Methods to bring studies of technology and actor studies together are also proposed; domestic hot water use has been modelled based on time-use data, while a multifaceted market situation, in which new system solutions must find their way, has been described by the solar and pellet industries. The complexity of assessing installation and use of a particular heating system in relation to the overall energy system is also discussed. Overall, this thesis shows that successful use of solar heating does not only come down to proper technical solutions, but also depends on the interaction between technology and market actors. A widened perspective, including the social context in which the heating system appears, is then essential. This thesis constitutes a step in that direction.

Distributed generation (DG) of combined cooling, heat, and power (CCHP) has been gaining momentum in recent years as an efficient, secure alternative for meeting increasing power demands in the world. One of the most critical and emerging markets for DG-CCHP systems is commercial and institutional buildings. The present study focuses analysis on the main economic, energy-efficiency, and environmental impacts of the integration of three types of advanced DG technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar panels) into four types of representative generic commercial building templates (small office building, medium office building, hospital, and college/school) in southern California (e.g., mild climate), using eQUEST as energy simulation tool. Detailed load profiles for the four commercial building types during times of peak electric and peak gas consumption were analyzed and complementary strategies to further increase overall building energy efficiencies such as energy efficiency measures (e.g., day lighting, exterior shading, improved HVAC performance) and thermally activated absorption cooling were also investigated. Results show that the high-temperature fuel cell (HTFC) performance is best matched with the hospital energy loads, resulting in a 98% DG capacity factor, 85% DG heat recovery factor, and $860,000 in energy savings (6 years payback). The introduction of thermally driven double-effect absorption cooling (AC) in the college building with HTFC reduces significantly the building electricity-to-thermal load ratio and boosts the heat recovery factor from 37% to 97%. © 2007.

Distributed generation (DG) of combined cooling, heat, and power (CCHP) has been gaining momentum in recent years as an efficient, secure alternative for meeting increasing power demands in the world. One of the most critical and emerging markets for DG-CCHP systems is commercial and institutional buildings. The present study focuses analysis on the main economic, energy-efficiency, and environmental impacts of the integration of three types of advanced DG technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar panels) into four types of representative generic commercial building templates (small office building, medium office building, hospital, and college/school) in southern California (e.g., mild climate), using eQUEST as energy simulation tool. Detailed load profiles for the four commercial building types during times of peak electric and peak gas consumption were analyzed and complementary strategies to further increase overall building energy efficiencies such as energy efficiency measures (e.g., day lighting, exterior shading, improved HVAC performance) and thermally activated absorption cooling were also investigated. Results show that the high-temperature fuel cell (HTFC) performance is best matched with the hospital energy loads, resulting in a 98% DG capacity factor, 85% DG heat recovery factor, and $860,000 in energy savings (6 years payback). The introduction of thermally driven double-effect absorption cooling (AC) in the college building with HTFC reduces significantly the building electricity-to-thermal load ratio and boosts the heat recovery factor from 37% to 97%. © 2007.

Tato práce se zabývá simulací denních zátěžových profilů pro účely vyhodnocení tarifní struktury. V první části práce je uvedena rešerše ekonomického hlediska dodávky elektřiny, tedy trhy s elektřinou, návrhem tarifů a tarifními strukturami v Česku a dalších evropských zemích. Práce se také zabývá konceptem komunitních energetických systémů. Stěžejní částí této práce je vytvoření modelu pro simulaci denních zátěžových profilů a zavést to k vyhodnocení více scénářů rozúčtování. Teoretická část práce se zabývá trhem s elektřinou, procesem jeho tvorby a prvky, které dohromady tvoří tarifní strukturu. Ceny elektrické energie jsou uvedeny z historického a aktuálního hlediska pro několik evropských zemí. Poté jsou uvedeny klíčové technologické aspekty komunitních energetických systémů. V praktické části této práce je uvedena simulace určená k vygenerování denních profilů spotřeby domácností. Model, implentován v prostředí Matlabu, používá Poissonovo i Binomické rozdělení. Vychází z profilu obsazenosti (aktivity) v každé domácnosti v jednotlivých hodinách a také z náhodného spínání spotřebičů. Na základě modelu byly otestovány scénáře pro výpočet ceny elektrické energie pro jednotlivé typové případy domácností., This thesis deals with simulating daily load profiles for the purpose of evaluating tariff structures. The first part of the thesis examines the economic aspect of electricity supply-that is, electricity markets, tariff design, and tariff structures available in Czechia and other European countries. The thesis also covers the concept of integrated community energy systems. The key part is creating a model simulation for simulating daily load profiles using a bottom-up method and presenting multiple scenarios for determining the price of electricity in different scenarios.

Tato práce se zabývá simulací denních zátěžových profilů pro účely vyhodnocení tarifní struktury. V první části práce je uvedena rešerše ekonomického hlediska dodávky elektřiny, tedy trhy s elektřinou, návrhem tarifů a tarifními strukturami v Česku a dalších evropských zemích. Práce se také zabývá konceptem komunitních energetických systémů. Stěžejní částí této práce je vytvoření modelu pro simulaci denních zátěžových profilů a zavést to k vyhodnocení více scénářů rozúčtování. Teoretická část práce se zabývá trhem s elektřinou, procesem jeho tvorby a prvky, které dohromady tvoří tarifní strukturu. Ceny elektrické energie jsou uvedeny z historického a aktuálního hlediska pro několik evropských zemí. Poté jsou uvedeny klíčové technologické aspekty komunitních energetických systémů. V praktické části této práce je uvedena simulace určená k vygenerování denních profilů spotřeby domácností. Model, implentován v prostředí Matlabu, používá Poissonovo i Binomické rozdělení. Vychází z profilu obsazenosti (aktivity) v každé domácnosti v jednotlivých hodinách a také z náhodného spínání spotřebičů. Na základě modelu byly otestovány scénáře pro výpočet ceny elektrické energie pro jednotlivé typové případy domácností., This thesis deals with simulating daily load profiles for the purpose of evaluating tariff structures. The first part of the thesis examines the economic aspect of electricity supply-that is, electricity markets, tariff design, and tariff structures available in Czechia and other European countries. The thesis also covers the concept of integrated community energy systems. The key part is creating a model simulation for simulating daily load profiles using a bottom-up method and presenting multiple scenarios for determining the price of electricity in different scenarios.

Tato práce se zabývá simulací denních zátěžových profilů pro účely vyhodnocení tarifní struktury. V první části práce je uvedena rešerše ekonomického hlediska dodávky elektřiny, tedy trhy s elektřinou, návrhem tarifů a tarifními strukturami v Česku a dalších evropských zemích. Práce se také zabývá konceptem komunitních energetických systémů. Stěžejní částí této práce je vytvoření modelu pro simulaci denních zátěžových profilů a zavést to k vyhodnocení více scénářů rozúčtování. Teoretická část práce se zabývá trhem s elektřinou, procesem jeho tvorby a prvky, které dohromady tvoří tarifní strukturu. Ceny elektrické energie jsou uvedeny z historického a aktuálního hlediska pro několik evropských zemí. Poté jsou uvedeny klíčové technologické aspekty komunitních energetických systémů. V praktické části této práce je uvedena simulace určená k vygenerování denních profilů spotřeby domácností. Model, implentován v prostředí Matlabu, používá Poissonovo i Binomické rozdělení. Vychází z profilu obsazenosti (aktivity) v každé domácnosti v jednotlivých hodinách a také z náhodného spínání spotřebičů. Na základě modelu byly otestovány scénáře pro výpočet ceny elektrické energie pro jednotlivé typové případy domácností., This thesis deals with simulating daily load profiles for the purpose of evaluating tariff structures. The first part of the thesis examines the economic aspect of electricity supply-that is, electricity markets, tariff design, and tariff structures available in Czechia and other European countries. The thesis also covers the concept of integrated community energy systems. The key part is creating a model simulation for simulating daily load profiles using a bottom-up method and presenting multiple scenarios for determining the price of electricity in different scenarios.

Tato práce se zabývá simulací denních zátěžových profilů pro účely vyhodnocení tarifní struktury. V první části práce je uvedena rešerše ekonomického hlediska dodávky elektřiny, tedy trhy s elektřinou, návrhem tarifů a tarifními strukturami v Česku a dalších evropských zemích. Práce se také zabývá konceptem komunitních energetických systémů. Stěžejní částí této práce je vytvoření modelu pro simulaci denních zátěžových profilů a zavést to k vyhodnocení více scénářů rozúčtování. Teoretická část práce se zabývá trhem s elektřinou, procesem jeho tvorby a prvky, které dohromady tvoří tarifní strukturu. Ceny elektrické energie jsou uvedeny z historického a aktuálního hlediska pro několik evropských zemí. Poté jsou uvedeny klíčové technologické aspekty komunitních energetických systémů. V praktické části této práce je uvedena simulace určená k vygenerování denních profilů spotřeby domácností. Model, implentován v prostředí Matlabu, používá Poissonovo i Binomické rozdělení. Vychází z profilu obsazenosti (aktivity) v každé domácnosti v jednotlivých hodinách a také z náhodného spínání spotřebičů. Na základě modelu byly otestovány scénáře pro výpočet ceny elektrické energie pro jednotlivé typové případy domácností., This thesis deals with simulating daily load profiles for the purpose of evaluating tariff structures. The first part of the thesis examines the economic aspect of electricity supply-that is, electricity markets, tariff design, and tariff structures available in Czechia and other European countries. The thesis also covers the concept of integrated community energy systems. The key part is creating a model simulation for simulating daily load profiles using a bottom-up method and presenting multiple scenarios for determining the price of electricity in different scenarios.

Tato práce se zabývá simulací denních zátěžových profilů pro účely vyhodnocení tarifní struktury. V první části práce je uvedena rešerše ekonomického hlediska dodávky elektřiny, tedy trhy s elektřinou, návrhem tarifů a tarifními strukturami v Česku a dalších evropských zemích. Práce se také zabývá konceptem komunitních energetických systémů. Stěžejní částí této práce je vytvoření modelu pro simulaci denních zátěžových profilů a zavést to k vyhodnocení více scénářů rozúčtování. Teoretická část práce se zabývá trhem s elektřinou, procesem jeho tvorby a prvky, které dohromady tvoří tarifní strukturu. Ceny elektrické energie jsou uvedeny z historického a aktuálního hlediska pro několik evropských zemí. Poté jsou uvedeny klíčové technologické aspekty komunitních energetických systémů. V praktické části této práce je uvedena simulace určená k vygenerování denních profilů spotřeby domácností. Model, implentován v prostředí Matlabu, používá Poissonovo i Binomické rozdělení. Vychází z profilu obsazenosti (aktivity) v každé domácnosti v jednotlivých hodinách a také z náhodného spínání spotřebičů. Na základě modelu byly otestovány scénáře pro výpočet ceny elektrické energie pro jednotlivé typové případy domácností., This thesis deals with simulating daily load profiles for the purpose of evaluating tariff structures. The first part of the thesis examines the economic aspect of electricity supply-that is, electricity markets, tariff design, and tariff structures available in Czechia and other European countries. The thesis also covers the concept of integrated community energy systems. The key part is creating a model simulation for simulating daily load profiles using a bottom-up method and presenting multiple scenarios for determining the price of electricity in different scenarios.