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.

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.

In this paper we present two MATLAB applications that generates synthetic electricity load profiles for office buildings and detached houses down to 1-minute resolution. The applications have been developed using App Designer — a MATLAB environment for application development. The applications are based on consumer load models for office buildings and detached houses published in previous research work. The aim of this paper is to present an overview of the application functionalities, code design, assumptions and limitations, and examples of their potential use in power system education and research. To the author’s knowledge these are the first applications which allow generating synthetic load profiles for office buildings and houses in practical and intuitive manner where building attributes can be easily configured.

In this paper we propose and evaluate two cases of a model predictive scheduling approach to anticipate overrides of schedulable electric space heating systems in detached houses. We assume a demand-response set-up where the space heating systems of a population of heterogeneous detached houses are scheduled over a finite horizon with the objective of having their aggregated space heating load follow a desired load profile. We envision that the desired load profile provides hourly to sub-hourly ancillary services to electricity market actors and define schedule overrides as the interruption of demand response following a violation of the indoor temperature comfort in a house. We use a model to represent the indoor temperature change in detached houses on minute resolution which considers, among other variables, weather- and individual behavioral-related heat gains and losses in the building. The model predictive scheduling approach is evaluated on a use-case consisting of a balance responsible player looking to minimize its daily expected power imbalances on the intraday market. The scheduling is performed on 100 detached houses participating in demand-response for two predictive cases and a non-predictive case for comparison. The predictive cases differ in the level of information known about thebuilding attributes of the population. Simulations are performed for 90 consecutive days corresponding to a Swedish winter period where results indicate power imbalance reductions of up to 30% and notable differences between predictive cases.