Electric power systems are undergoing a paradigm shift where an increasing number of variable renewable energy resources such as wind and solar power are being introduced to all levels of existing power grids. At the same time consumers are gaining a more active role where self energy production and home automation solutions are no longer uncommon. This challenges traditional power systems which were designed to serve as a centralized top-down solution for providing electricity to consumers. Demand response has risen as a promising solution to cope with some of the challenges that this shift is creating. In this thesis, control and scheduling studies using demand response, and consumer load models adapted to environments similar to Sweden are proposed and evaluated. The studies use model predictive control approaches for the purpose of providing ancillary and financial services to electricity market actors using thermal flexibility from detached houses. The approaches are evaluated on use-cases using data from Sweden for the purpose of reducing power imbalances of a balance responsible player and congestion management for a system operator. Simulations show promising results for reducing power imbalances by up to 30% and managing daily congestion of 5-19 MW using demand response. Moreover, a consumer load model of an office building is proposed using a gray-box modeling approach combining physical understanding of buildings with empirical data. Furthermore, the proposed consumer load model along with a similar model for detached houses are packaged and made freely available as MATLAB applications for other researchers and stakeholders working with demand response. The applications allow the user to generate synthetic electricity load profiles for heterogeneous populations of detached houses and office buildings down to 1-min resolution. The aim of this thesis has been to summarize and discuss the main highlights of the included articles. The interested reader is encouraged to investigate further details in the second part of the thesis as they provide a more comprehensive account of the studies and models proposed. / <p>QC 20171011</p>