The Swedish electricity market has seen a surge in demand over the past fifty years. Since the deregulation in 1996, both the demand and supply side of the market has evolved into a competitive space for market actors. This has resulted in unique conditions, characterizing the nordic electricity market as being very much dependent on demand predictability. Demand flexibility has been investigated frequently, but as certain sector developments constantly affect electricity consumption, such as price volatility and green technology development, the price- and temperature elasticities should be revised continuously. Hourly-price contracts specifically, are on the rise in price area SE1, with a rise from 6 percent to 8 percent during the middle of 2023, meaning consumption patterns are of interest for the future as the trend continues. This has, to the authors’ knowledge, not been investigated within the municipality of Luleå, which is the basis for this master’s thesis. Two methodologies were adopted to investigate the elasticities, (1) an ordinary least squares (OLS) regression model and (2) two weighted least squares (WLS) regression models. The results indicate that the price elasticity of demand in Luleå is still very inelastic, with temperature bearing the main explanatory power for household electricity consumption patterns.

When consumers are able to buy electricity on an hourly instead of monthly basis, the demand side flexibility is likely to increase. One way to lower the cost of electricity is to move consumption from peak price hours to low price hours, a sort of inter-temporal substitution were the net energy use is unaffected. By simulating one example of inter-temporal substitution in the Swedish spot market during 2008-2010, we show that the general welfare effects are small in terms of a more efficient energy production, but that the transfer of resources from producers to consumers is large. Whether the welfare effect is positive or negative is highly dependent on future electricity prices, the introduction of renewable energy resources, and the price of the new technology needed for the demand side regulation. If 2010 is used as a reference case, the results from our specific case concludes that a natural investment equilibrium is reached when approximately 150 000 households invest in the proposed demand side regulation technology. Using the same reference year, we see that if 70 000 households participates the Net Present Welfare benefit is around 10% of the necessary investment cost; to be compared with the transfer of benefits from producers to consumers which estimates roughly 2100% of the necessary investment cost. We argue that this imbalance in potential welfare benefits between producers and consumers might slow down the process of increasing the general welfare.

Today, power grids are designed as a traditional large-scale centralized generation. However, as more renewable asynchronous resources are integrated, the share of rotating synchronous machines decreases. These asynchronous resources lack the inherent inertia from conventional generation, resulting in new challenges in power grid management. Without proper measures, the frequency might deviate significantly during system disruptions. During a frequency deviation, the power system operator activates power reserves to mitigate and maintain the balance. Currently, these reserves the vast majority of hydropower. New technologies for providing these reserves are needed with increasing intermittent power generation. One potential technical asset to reduce these challenges resides in demand-side energy flexibility. Demand-side energy flexibility for residential has mostly been large, but disregarded resources since small individual consumers lack the required capacity to provide ancillary services. This necessitates the need for aggregation as a possible solution.  This master thesis aims to investigate the economic potential for utilizing demand response, battery energy storage systems, and electric vehicle charging as a resource for frequency containment reserves to stabilize the power system. The thesis investigates the demand-flexibility for each asset based on residential behavior to determine the complete potential regarding requirements set by the system operator. The study also investigates how ABB AbilityTM OPTIMAX®, as an optimization tool, could optimize the model’s energy demand to generate increased energy cost savings.  The results of the investigation’s indicate great potential for all assets when aggregated, especially the aggregation of the battery energy storage system and the wastewater heat pump system. The study showed the tool ABB AbilityTM OPTIMAX®’s energy-saving potential, especially when integrating the model with photovoltaic generation with battery energy storage systems. Observations that led to further discussions included whether oversizing assets could be an option to increase demand flexibility and revenue. / Dagens elnät är konstruerat som en storskalig centraliserad production. Men,när integration av förnyelsebar och asynkrona prodcunter ökar, minskar andelenroterande synkrona generatorer. Dessa asynkrona system tillhandahåller dennaturliga trögheten som konventionella system med roterande massa. Dettamedför nya utmaningar för hantering och kontroll av elnätet. Utan tillräckligaåtgärder kan frekvensen avvika betynade vid ett systemfel. Vid händele av ettsystemfel aktiverar kraftsystemoperatören olika stödtjänster för att minimeraoch balansera frekvensen. I dagsläget står vattenkraften för den absolut störstaandelen av stödtjänster. Med en fortsatt ökande andel av förnyelsebara energislag,kommer det att krävas ytterligare teknologier. En möjligt teknisk lösning äratt utnyttja den redan befintliga förbrukningsflexibiliteten i bostadshus. Närman tidigare räknat på förbrukningsflexibiliteten i bostadshus har man oftabortsett från små och mindre laster då dessa enskilda laster ofta saknar denkapacitet som krävs för att bidra på balansmarknade, vilket kan lösas genomatt aggregera dessa mindre, individuella laster. Målet med detta examensarbeteär att undersöka den ekonomiska potentialen genom att utnyttja den befintligaoch outnyttjade förbrukningsflexibiliteten hos laster i ett bostadsområde genomatt som en resurs bidra med stödtjänster till elnätet . Dessa laster är ventilation,spillvattenvärmepump, belysning, elbilsladdning och batterilagring. För attbestämma den total tillgängliga kapaciteten undersöker även studien varje enskildtillgångs förbrukningsflexibilitet, vilket beror av boendes vanor, väderlek, mm.Slutligen undersöker även studien möjliga energikostnadsbesparingar med attanvända ABBs optimeringsprogram ABB Ability™ OPTIMAX®. Resultatenfrån stuiden visar på stora potentiella ekonomiska inkomster för alla tillgångar,speciellt för batteri system samt spillvattenvärmepumpen. Studien visade ävenpå goda energikostnadsbesparingar genom att använda ABB Ability™ OPTIMAX®,speciellt vid integrering av solcellsproduktion och batteri systemen. Observationersom ledde till ytterligare diskussioner var om överdimensionering av tillgångarkan vara ett alternativ för att öka förbrukningsflexibiliteten och de potentiellaintäkterna.3

The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated. / Doctor of Philosophy / The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.

The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

Paper [I] Using a unique and highly detailed data set on energy consumption at the appliance-level for 200 Swedish households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e.g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from "expensive" to "cheap" hours, using the Nord Pool spot prices as a proxy for a dynamic price, are computed to be very small; roughly 2-4% reduction in total daily costs from shifting load up to five hours ahead, indicating small incentives for households (and retailers) to adopt dynamic pricing of electricity. Paper [II] Using a detailed data set on appliance-level electricity consumption at the hourly level, we provide the first estimates of hourly and end-use-specific income elasticities for electricity. Such estimates are informative about how consumption patterns in general, and peak demand in particular, will develop as households’ income changes. We find that the income elasticities are highest during peak hours for kitchen and lighting, with point estimates of roughly 0.4, but insignificant for space heating. Paper [III] In this paper, I estimate the price elasticity of electricity as a function of the choice between fixed-price and variable-price contracts. Further, assuming that households have imperfect information about electricity prices and usage, I explore how media coverage of electricity prices affects electricity demand, both by augmenting price responsiveness and as a direct effect of media coverage on electricity demand, independent of prices. I also address the endogeneity of the choice of electricity contract. The parameters in the model are estimated using unique and detailed Swedish panel data on monthly household-level electricity consumption. I find that price elasticities range between −0.025 and −0.07 at the mean level of media coverage, depending on contract choice, and that households with monthly variation in electricity prices respond more to prices when there is extensive media coverage of electricity prices. When media coverage is high, for example 840 news articles per month (which corresponds to the mean plus two standard deviations), the price elasticity is −0.12, or 1.7 times the elasticity at the mean media coverage. Similarly, media coverage is also found to have a direct effect on electricity demand. Paper [IV] I explore how households switch between fixed-price and variable-price electricity contracts in response to variations in price and temperature, conditional on previous contract choice. Using panel data with roughly 54000 Swedish households, a dynamic probit model is estimated. The results suggest that the choice of contract exhibits substantial state dependence, with an estimated marginal effect of previous contractchoiceof0.96, andthattheeffectofvariationinpricesandtemperatureonthechoice of electricity contract is small. Further, the state dependence and price responsiveness are similar across housing types, income levels and other dimensions. A plausible explanation of these results is that transaction costs are larger than the relatively small cost savings from switching between contracts.

Dans un contexte de croissance démographique dans lequel certaines ressources naturelles sont de plus en plus limitées, une gestion optimisée et conjointe des réseaux publics de l’eau et de l’électricité s’impose. L’ouverture progressive des marchés de l’électricité à la concurrence et les changements de réglementation dans plusieurs pays ont contribué au développement des mécanismes de la flexibilité de la demande, permettant d’impliquer directement les consommateurs dans la gestion de l’équilibre offre-demande du réseau électrique. Les systèmes d’eau potable, étant de grands consommateurs d’électricité, disposent d’une flexibilité grâce à la présence d’ouvrages de stockage d’eau (bâches et réservoirs) et de pompes à vitesse variable. Cette flexibilité, souvent exploitée uniquement à des fins de sécurisation des demandes en eau, peut être valorisée pour permettre une meilleure gestion de l’équilibre du réseau électrique. L’objectif de cette thèse est l’évaluation des valeurs économiques et écologiques relatives à l’intégration de la flexibilité des systèmes d’eau potable dans la gestion opérationnelle du système électrique français. Une étude de l’architecture des marchés de l’électricité en France est d’abord menée pour identifier les mécanismes de flexibilité de la demande les plus adaptés aux contraintes d’exploitation des systèmes d’eau. Des modèles mathématiques d’optimisation sont ensuite proposés et résolus à travers certaines heuristiques, en intégrant les incertitudes relatives aux consommations d’eau, aux prix des marchés ainsi qu’à la disponibilité des équipements de pompage. Les résultats numériques, discutés en se basant sur trois systèmes d’eau potable réels en France, intègrent les aspects économiques (en considérant les risques associés), opérationnels et écologiques. Des réductions importantes des coûts d’exploitation des systèmes d’eau sont estimées à travers la valorisation de l’énergie non consommée pendant les moments de pointe sur le marché spot de l’électricité. En parallèle, la considération des incertitudes permet de sécuriser l’opération des systèmes d’eau en temps réel, et de maîtriser les risques économiques relatifs à l’équilibrage du réseau électrique. De plus, des réductions importantes des émissions de CO2, estimées à environ 400 tonnes par jour en France, peuvent être réalisées en réduisant les volumes d’électricité issus des sources fossiles. / In a context of demographic growth in which natural resources are more and more limited, optimized management of water and power networks is required. Changes in electricity markets regulation in several countries have recently enabled effective integration of Demand Response mechanisms in power systems, making it possible to involve electricity consumers in the real-time balance of the power system. Through its flexible components (variable-speed pumps, tanks), drinking water systems, which are huge electricity consumers, are suitable candidates for energy-efficient Demand Response mechanisms. However, these systems are often managed independently of power system operation, for both economic and operational reasons. In this thesis, the objective is the evaluation of the economic and the ecological values related to the integration of drinking water systems flexibility into power system operation through french demand response mechanisms. An analysis of the architecture of french electricity markets is first conducted, allowing to target the most suitable demand response mechanisms considering water systems operating constraints. Some mathematical models to optimize water systems flexibility are then proposed and solved through original heuristics, integrating uncertainties about water demands, market prices and pumping stations availability. Numerical results, which are discussed using three real water systems in France, integrate the economic aspects inclunding risks, operational and ecological aspects. Significant reductions in water systems operating costs are estimated through the optimization of demand response power bids on the French spot power market during peak times. In parallel, uncertainties consideration secures the operation of water systems in real time, and makes it possible to manage economic risks related to the power grid balancing. In addition, significant savings in CO2 emissions, estimated to around 400 tons per day in France, can be achieved by reducing electricity production from fossil sources.

The Swedish energy power system is in the middle of a paradigm shift where the increased share of intermittent energy sources place higher demand on the ability to regulate and balance the generation and consumption of electricity. Demand flexibility, which means that consumers can adjust their energy consumption, is a promising solution to manage the imbalance in the power system. Electric heat pumps in residential buildings are recognized to have potential to serve as a flexible load. In this thesis, an aggregated multi-family residential building model is developed to generate heat load profiles for a larger number of buildings which facilitate an assessment of the heat pump flexibility. The flexibility assessment is performed for a local distribution grid area with 174 buildings and an electricity price region in Sweden with 10 146 buildings with heat pumps. The flexibility assessment analyses the heat pump load deviation between a base load case and a case where the heat pumps receive an off-signal. The assessment takes into consideration seven flexibility parameters and is conducted for ambient temperatures between -20°C and 15°C. The thermal inertia of multi-family residential buildings facilitates a load shift with a duration of 4.4 to 9.8 hours depending on the ambient temperature. The maximal average power reduction for one hour of 10 MW in a distribution grid and 169 MW in an electricity price region illustrates the potential of using heat pumps as a demand flexibility solution in the electricity grid.

Sweden is undergoing energy transition to become a zero-carbon economy with electricity production aims at 100% from renewable resources by 2040. Sweden also has a national goal to have fossil-free vehicle fleet by 2030. The increasing share of intermittent renewable resources creates growth in mismatches between electricity supply and demand. Demand flexibility provides solution to imbalances in power system where the prosumers can regulate their energy consumption. Demand response (DR) mechanism could be beneficial to power gird stability. Electric heat pumps serve as a pool of flexible load meanwhile the thermal inertia of the residential buildings serves as thermal energy storage. In this thesis, a techno-economic analysis of demand flexibility from heat pumps for residential buildings located in central Örebro is carried out with assistance of building energy simulations. This thesis aims to improve the intelligence of this existing buildings by comprehending the size of thermal inertia availability according to different heat demand, building envelope materials, ventilation systems, weather conditions and user behaviors. Two multi-family residential buildings, Klockarängsvägen and Pärllöken, are selected for case study and compared in terms of thermal inertia and avoided peak power fees in avoided peak power fee from flexible heat pump loads. Both buildings use heat pumps for space heating and domestic hot water supply. Electricity billings are subscribed to power tariff scheme, which makes peak power shifting more profitable. On the coldest day scenario when the ambient temperature is -20°C, Pärllöken’s indoor temperature drops from 21°C to 19.1°C if heat pump is turned off for an hour. Klockarängsvägen’s indoor temperature drops from 21°C to 16.6°C if heat pump is turned off for an hour. At the lowest indoor temperature setpoint of 18°C, Pärllöken demonstrates a maximum power-shift capacity of 25 kW and heatshift capacity of 75 kWh on the coldest day. That of Klockarängsvägen is a maximum power-shift capacity of 20 kW and heat-shift capacity of 20 kWh. With larger building thermal inertia and more power-shift capacity, Pärllöken is undoubtedly the winner thanks to concrete wall materials, heavier building thermal mass, balanced ventilation, heat recovery system, and higher window class. In economic analysis, based on the proposed energy models, two control strategy options in Pärllöken are considered. Economic analysis focuses on winter season from October to March. Option 1 operates heat pump in variable capacity control mode at part load capacity. Option 2 operates in fixed capacity on/off -4- control. In winter season, Pärllöken saves 1 646 SEK in Option 1 and 2 273 SEK in Option 2. Klockarängsvägen only considers Option 1 for economic analysis, which results in 20 948 SEK avoided peak power fee. Option 2 for Klockarängsvägen exceeds indoor temperature setpoint very quickly mainly due to poorer building envelope insulation in which conserves lower thermal inertia. / Sverige genomgår en energitransformation för att bli en fossilfri ekonomi som siktar på att ha en elproduktion från 100% förnybara resurser år 2040. Sverige har också ett nationellt mål att ha en fossilfri fordonsflotta till 2030. Den ökande andelen av intermittenta förnybara resurser bidrar till ökning av obalans mellan produktion och efterfråga av elektricitet. Efterfrågeflexibilitet ger en lösning på problemet med obalanser i energisystemet där prosumenter kan reglera sin energiförbrukning. Efterfrågeflexibilitet kan vara fördelaktigt för kraft- och nätstabilitet. Elektriska värmepumpar kan agera som en stor flexibel last samtidigt som fastighetens termiska tröghet fungerar som värmeenergilagring. I denna avhandling utförs en teknisk-ekonomisk analys av efterfrågeflexibilitet från värmepumpar för två bostadshus beläget i centrala Örebro med hjälp av energisimuleringar av fastigheten. Genom denna avhandling syftar författaren på att höja intelligensen av de befintliga fastigheterna genom att undersöka storleken av den termiska trögheten som finns tillgänglig med avseende på olika värmescenario, byggnadsmaterial, ventilationssystem, väderförhållanden och användarbeteenden. Två flerfamiljshus, Klockarängsvägen och Pärllöken, väljs för jämförelse med avseende på den termisk tröghet som bidrar mest till efterfrågeflexibiliteten. De två utvalda fastigheterna använder värmepumpar för värme och varmvatten. Båda fastigheterna faktureras enligt effektabonnemang, vilket gör effektutjämning mer lönsamt. I det kallaste scenariot, när omgivningstemperaturen är -20°C, faller Pärllökens inomhustemperatur från 21°C till 19,1°C och Klockarängsvägens inomhustemperatur sjunker till 16,6°C om värmetillförseln stängs av i en timme. Under det lägsta börvärdet för inomhustemperatur på 18°C visar Pärllöken en maximal effektförskjutningskapacitet på 25 kW och för Klockarängsvägen-byggnader 20 kW. Med hänsyn till fastighetens termiska tröghet är Pärllöken utan tvekan vinnaren på grund av betong som väggsmaterial, högre termisk massa, balanserad ventilation, värmeåtervinningssystem och högre energiklass på fönsterglasen. Ovanstående skäl gör att Pärllökens termiska tidskonstant är minst tre gånger längre innan temperaturen når det lägsta börvärdet på 18°C, jämfört med Klockarängsvägen. Detta ger att Pärllöken har en högre förskjutningskapacitet av värme på 75 kWh jämfört med Klockarängsvägens maximala förskjutningskapacitet på 20 kWh. I en ekonomisk analys, baserat på författarens framtagna energimodeller, beaktas två styrstrategier i Pärllöken. Den ekonomiska analysen fokuserar på vintersäsongen från oktober till mars. Alternativ 1 driver värmepumpen med partiell kapacitet enligt reglerbar effekt. Alternativ 2 stänger av värmepumpen helt. Under vintersäsongen sparar Pärllöken 1 646 SEK med Alternativ 1 och 2 273 SEK med Alternativ 2. Klockarängsvägen använder sig endast av Alternativ 1 för en ekonomisk analys, vilket resulterar i en kostnadsbesparing på 20 948 SEK. En förstudie med värmepump i kombination med andra förnybara tekniker så som solceller på Klockarängsvägen genomförs för att undersöka potentialen av energibesparing. Kombinationen ger dock inte en positiv effekt på grund av den låga solinstrålningen under vintertid.