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.

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.

This work is aimed at studying the profitability of Household Demand Side Management which is a Demand Response (DR) solution aiming at providing power margins to the non-automatic Balancing Market (BM) by load-shedding of domestic electric heaters. After a large literature review aiming at better understanding the balancing market context in Europe, a deterministic model has been applied on the Swedish case in the 2020 timeframe, considering as well the involvement of Electric Vehicles (EVs) flexibility margins. It finally shows that due to investment costs distributed in each household, such solution is not feasible in short-term in locations which are not suffering a significant shortfall risk.

There has been a large body of statements claiming that the large-scale deployment of Distributed Energy Resources (DERs) could eventually reshape the future distribution grid operation in numerous ways. Thus, it is necessary to introduce a framework to measure to what extent the power system operation will be changed by various parameters of DERs. This article proposed a modelling framework for an overview analysis on the correlation between DERs. Furthermore, to validate the framework, the authors described the reference models of different categories of DERs with their unique characteristics, comprising distributed generation, active demand and electric vehicles. Subsequently, quantitative analysis was made on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. Simulations are performed in two typical distribution network models for four seasons. The simulation results show that in general the DER deployment brings in the possibilities to reduce the power losses and voltage drops by compensating power from the local generation and optimizing the local load profiles.

The large scale deployment of distributed generation from renewable energy sources may undermine the network safe operation and may require network reinforcements. In order to avoid or postpone the infrastructural investments it is possible to exploit other resources. In this paper the operation of a load aggregator is simulated on the Swedish island of Gotland in different wind penetration condition to test two different business models, one oriented to the price following and the other oriented to the wind following. The adopted methodology is described and the results presented and commented.

Recent data have shown that it could be profitable on some control markets to use Plug-in Hybrid Electric Vehicles (PHEVs) as control power resources. The concept where battery driven vehicles such as PHEVs provide ancillary service to the grid is commonly referred to as Vehicle to Grid (V2G). As each PHEV has a limited capacity, it is necessary to have a control system that aggregates a large number of vehicles. This paper investigates what is required in order to design such a system. The result is presented as reference architectures and contains propositions of important components, processes and information needs. It is shown that a PHEV can provide control power in two different ways that in turn generate several different system concepts. A mathematical model is presented that allows comparison between the different solutions.

Widespread adoption of onsite distributed generation providing households with their electricity supply could make them very significant actors in terms of their aggregated impact on the wider electric power system. This paper proposes an approach which brings community engagement to the case of distributed energy resource based electricity supply for urban residential buildings. Considering a future case for Stockholm, a community ownership and service-oriented setup is proposed which fosters the creation of a market for both traditional and new stakeholders in the electric power system while ensuring the integrity of control by individual apartments. Salient features include a hypothetical pricing model which encourages local trade of electricity between apartments guided by individual apartment behavior and decision making through bid strategies. A set of research themes devoted to understanding the trends in effect of changes in the integral concept components have been identified through simulations and subjected to initial analysis.

There has been a large body of statements claiming that the large scale deployment of Distributed Energy Resources (DERs) will eventually reshape the future distribution grid operation in various ways. Thus, it is interesting to introduce a platform to interpret to what extent the power system operation will be alternated. In this paper, quantitative results in terms of how the future distribution grid will be changed by the deployment of distributed generation, active demand and electric vehicles, are presented. The analysis is based on the conditions for both a radial and a meshed distribution network. The input parameters are based on the current and envisioned DER deployment scenarios proposed for Sweden.

The concept of Aggregators is receiving large attention when new business cases for Smart grids are presented. Several studies propose different schemes of aggregating load and/or production. However, many of these studies are tailored for one specific market or network, making the generic value of the work small. The objective of the work presented in the paper is to analyze the fundamental needs for aggregating small scale loads or production units from a technical, financial and regulatory point of view. The final results of the analysis can work as a foundation for developing more elaborate aggregation business cases. These can later be analyzed from a technical and economical standpoint by, e.g., simulations.

Future powers system is considering huge flux of information flow due to increase in Renewable Energy Sources. Due to the limited monitoring and management of SCADA systems, inclusion of DER at local distribution level is a challenging task for the Smart Grid. We discuss the non-functional requirement aspects and their implication in trustworthy systems. The paper also illustrates an engineering approach towards trustworthy ICT systems. We present a use case about Injection/Curtailment of DER in distributed network. Further, we argue the importance of Service Level Agreements as coordination tool for information exchange between DSO and DER for the provisioning of trustworthy services. Finally conclude that modeling of SLA using Multi Agent Systems is a viable approach towards Trustworthy future Smart Grid applications.

Previous work has shown that it could be profitableon some control markets to use Plug-in Hybrid ElectricVehicles (PHEV) as control power resources. This concept,where battery driven vehicles such as PHEVs provide ancillaryservice to the grid is commonly referred to as Vehicleto Grid (V2G). The idea is to sell the capacity and energy ofthe parked PHEVs on the control market. Due to the factthat cars on average are parked 92% of the day, the availabilityof this capacity could be very high, even though it willbe highly dependent on commuting patterns in peak hours.Further, as each PHEV has a very small capacity from a gridperspective, it is necessary to implement an aggregating controlsystem, managing a large number of vehicles. This paperpresents a Monte Carlo simulation model where technical issuessuch as the reliability in communication for a large fleetof PHEVs is included. The communication in the simulationmodel is based on data from Automated Meter Reading(AMR) systems, which share important charachteristics withV2G communications. Moreover, the simulation is based onavailable data on the distributions of commuting departuretimes and travel distances, as well as average driving powerconsumption, PHEV battery capacities and the market constraintsof the secondary control market in Germany.

Previous work has shown that it could be profitable on some control markets to use Plug-in Hybrid Electric Vehicles (PHEV) as control power resources. This concept, where battery driven vehicles such as PHEV s provide ancillary service to the grid is commonly referred to as Vehicle to Grid (V2G). The idea is to sell the capacity and energy of the parked PHEVs on the control market. Due to the fact that cars on average are parked 92% of the day, the availability of this capacity could be very high, even though it will be highly dependent on commuting patterns in peak hours. However, as each PHEV has a very small capacity from a grid perspective, it is necessary to implement an aggregating control system, managing a large number of vehicles. This paper presents strategies for an Aggregator to fulfill control bids on the German control markets. These strategies are tested with respect to reliability, efficiency and profitability in a Monte Carlo simulation model. The model is based on available data on the distributions of commuting departure times and travel distances, as well as average driving power consumption, PHEV battery capacities and the market constraints of the secondary control market in Germany. 

Previous work has shown that it could be profitable on some control markets to use plug-in hybrid electric vehicles (PHEV) as control power resources. This concept, where battery driven vehicles such as PHEVs provide ancillary service to the grid is commonly referred to as Vehicle to Grid. The idea is to sell the capacity and energy of the parked PHEVs on the control market. Due to the fact that cars on average are parked 92% of the day, the availability of this capacity could be very high, even though it will be highly dependent on commuting patterns in peak hours. However, as each PHEV has a very small capacity from a grid perspective, it is necessary to implement an aggregating control system, managing a large number of vehicles. The present article investigates design requirements on such a system.