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, along with energy-use statistics from national databases. The validation shows that the modeling approach is sound and can provide reasonably accurate load profiles as the error results are in alignment with related models from other research groups.

This thesis is a composite thesis of five papers. Paper 1 presents a bottom-up simulation model to generate load profiles from space heating, hot water and appliances in detached houses. Paper 2 presents a data analytic framework for analyzing electricity-use from heating ventilation and air conditioning (HVAC) loads and appliance loads in an office building. Paper 3 presents a non-homogeneous Markov chain model to simulate representative occupancy profiles in single office rooms. Paper 4 utilizes the results in paper 2 and 3 to describe a bottom-up simulation model that generates load profiles in office buildings including HVAC loads and appliances. Paper 5 uses the model in paper 1 to analyze the technical feasibility of using DR to solve congestion problems in a distribution grid.

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 sammanläggningsavhandling bestående av fem artiklar. Artikel 1 presenterar botten-upp-simuleringsmodellen för genereringen av elförbrukningsprofiler från uppvärmning, varmvatten och apparater i småhus. Artikel 2 presenterar ett dataanalytiskt ramverk för analys av elanvändningen från uppvärmning, ventilation, och luftkonditioneringslaster (HVAC) och apparatlaster i en kontorsbyggnad. Artikel 3 presenterar en icke-homogen Markovkedjemodell för simulering av representativa närvaroprofiler i enskilda kontorsrum. Artikel  4 använder resultaten i artiklarna  2 och 3 för att beskriva en botten-upp-simuleringsmodell för generering av elförbrukningsprofiler från HVAC-laster och apparater i kontorsbyggnader. Artikel  5 använder modellen i artikel 1 för att analysera den tekniska möjligheten att använda efterfrågeflexibilitet för att lösa överbelastningsproblem i ett eldistributionsnät.

The integration of large shares of renewable energy sources in distribution grids runs the risk of outpacing the capacity of the network. Thus, high investment costs are expected at distribution system level to expand the existing grid to manage, among other challenges, anticipated congestion. This paper involves a study of the technical feasibility of using an ancillary service toolbox including day- and hour-ahead demand-response and battery energy storage as an alternative to grid expansion. The ancillary service toolbox is applied on radial distribution grids having large shares of renewable generation, controllable loads and power export capability to the overlaying power grid. The toolbox is simulated for a real use case presenting results on required demand-response participants and operation of flexibility resources for different congestion scenarios. The study concludes that the ancillary service solution is technically feasible for the use case, which may imply network investment deferral for distribution system operators.

An important aspect of Demand Response (DR) is to make accurate predictions for the consumption in the short term, in order to have a benchmark load profile which can be compared with the load profile influenced by DR signals. In this paper, a data analysis approach to predict electricity consumption on load level in office buildings on a day-ahead basis is presented. The methodology is: (i) exploratory data analysis, (ii) produce linear models between the predictors (weather and occupancies) and the outcomes (appliance, ventilation, and cooling loads) in a step wise function, and (iii) use the models from (ii) to predict the consumption levels with day-ahead prognosis data on the predictors. The data has been collected from a Swedish office building floor. The results from (ii) show that occupancy is correlated with appliance load, and outdoor temperature and a temporal variable defining work hours are connected with ventilation and cooling load. It is concluded from the results in (iii) that the error rate decreases if fewer predictors are included in the predictions. This is because of the inherent forecast errors in the day-ahead prognosis data. The achieved error rates are comparable with similar prediction studies in related work.

Due to an expanding integration of renewable energy resources in the power systems, mismatches between electricity supplyand demand will increase. A promising solution to deal with these issues is Demand Response (DR), which incentives end-users to be flexible in their electricity consumption. This paper presents a bottom up simulation model that generates office building electricity load profiles representative for Northern Europe. The model connects behavioral aspects of office workers with electricity usage from appliances, and physical representation of the building to describe the energy use of the Heating Ventilation and Air Conditioning systems. To validate the model, simulations are performed with respect to two data sets, and compared with real load measurements. The validation shows that the model can reproduce load profiles with reasonable accuracy for both data sets. With the presented model approach, it is possible to define simple portfolio office building models which subsequently can be used for simulation and analysis of DR in the power systems.

Demand Response (DR) is a promising solution todeal with supply/demand imbalances in the power systems. To appreciate the availability of DR, it is important to include enduser behavior in the analysis. In this paper, a model that can generate representative occupancy profiles in single office rooms is presented. The used method is non-homogeneous Markov chain modeling, along with exploratory data analysis of occupancy data, and an estimation of occupancy levels for different months and weekdays. The Markov chain model is calibrated with occupancy sensor data from 24 single rooms collected from an office building floor in Sweden. The simulation results have been statistically compared with a test data set consisting of occupancy data in 23 other rooms on the same floor. It was shown that the model could reproduce key occupancy properties of the test data.

In this paper, a simulation model that forecasts electricity load profiles for a population of Swedish households living in detached houses is presented. The model is constructed of three separate modules, namely appliance usage, Domestic Hot Water (DHW) consumption and space heating. The appliance and DHW modules are based on non-homogenous Markov chains, where household members move between dierent states with certain probabilities over the days. The behavior of individuals is linked to various energy demanding activities at home. The space heating module is built on thermodynamical aspects of the buildings, weather dynamics, and the heat loss output from the aforementioned modules. Subsequently, a use case for a neighborhood of detached houses in Sweden is simulated using a Monte Carlo approach. For the use case, a number of justified assumptions and parameter estimations are made. The simulations results for the Swedish use case show that the model can produce realistic power demand profiles. The simulated profile coincides especially well with the measured consumption during the summer time, which confirms that the appliance and DHW modules are reliable. The deviations increase for some periods in the winter period due to, e.g. unforeseen end-user behavior during occasions of extreme electricity prices.

In order to limit the environmental impact of electricity production, renewable energy sources are expected to expand significantly. The current grid and production structure are not designed to absorb such quantities of intermittent power output and smart grids can provide promising solutions, like demand response. This paper presents the technical advantages of managing flexible demand through Aggregators in a centralized fashion. An optimization model is developed to evaluate the economic benefits induced by adapting instantaneous electricity consumption to renewable generation. The model presented can easily be adapted in a more general context, and tested for different scenarios. Further, a use case related to the Smart Grids project on the Swedish island of Gotland is simulated. The simulation results show that the Aggregator solution is technically feasible, but that the current market design is a barrier for a successful implementation.

In a spot market, price prediction plays an indispensable role in maximizing the benefit of a producer as well as optimizing the utility of a consumer. This paper develops a seasonal ARIMA model with exogenous variables (SARIMAX) to predict day-ahead electricity prices in Elspot market, the largest day-ahead market for power trading in the world. Compared with the basic ARIMA model, SARIMAX has two distinct features: 1) A seasonal component is introduced to cope with weekly effect on price fluctuations. 2) Exogenous variables that exert influence on electricity prices are incorporated to make price predictions in the context of an integrated energy market. A detailed implementation of SARIMAX for Elspot market in Sweden is presented.

In this paper, a load reduction Aggregator participating on the Swedish Peak Power Reserve (PPR) market was analyzed. The load reduction was provided by electric heating systems in larger dwellings (e.g., office buildings). Two simulation models were developed to assess the market performance of the Aggregator. From the simulation results it was concluded that the PPR market was not optimal for the proposed Aggregator. Among other things, the market was too inflexible and the profits were just a small subvention of the larger day-ahead electricity purchases. Also, it was concluded that the Aggregator was exposed to several risks by participating on the market, e.g. situations of shortage in available power capacity. A number of countermeasures to mitigate these risks were presented.

This paper investigates the operation of a domestic heat load aggregator on the Swedish island of Gotland. In the considered business case the aim of the aggregator is to minimize the losses on the HVDC connection with the mainland matching local wind generation and load. A network model is implemented and static power flow simulations are performed to evaluate the aggregator profit and the effects of its actions on the network behavior. Various aggregator strategies are simulated in different wind penetration scenarios, several indices are calculated to compare the different cases on a quantitative base.