In the present paper, we introduce the Smart Energy System Control Laboratory (SESCL) as a fully-automated and user-oriented research infrastructure for controlling and operating smart energy systems in the context of a microgrid-under-test setting. SESCL’s high level of automation and capacity to fully function in a grid-decoupled way allow for the study and evaluation of yet-to-be-developed tools and algorithms for energy technologies and grid control strategies on the edge of system stability, but in a safe environment. In the context of various European Smart Grid Laboratories, the new concept and specifications of SESCL are outlined in depth. The key advantages of SESCL are highlighted as (i) the provisioning of a fully-automated busbar matrix to provide a very flexible and adjustable microgrid topology; (ii) the capability of load shedding or integration of grid participants, as well as changing the microgrid topology on demand; (iii) and the possibility to control and modify setpoints and operating parameters of grid participants during runtime. Inspired by real-world events in island grids, the islanding of a microgrid is utilized as a use case to illustrate the capabilities of the SESCL research infrastructure.

The present paper develops an Economic Model Predictive Control (EMPC) framework to provide Demand-Response (DR) for supporting the power grid stability while also maintaining Occupants' Thermal Satisfaction (OTS) in buildings. Our controller combines economic and occupant-oriented aspects by simultaneously optimizing two conflicting control goals, namely grid stability and OTS in buildings. We represent grid stability with Grid Costs (GC) based on a real-world dynamic electricity price and OTS with a reference indoor temperature, respectively. In the literature, there exists no study about occupant-oriented DR where the Model Predictive Control (MPC) is based on Resistor-Capacitor (RC) models identified from real measurements that also includes an attendance schedule for DR. For this, the EMPC uses a grey-box thermal building model that is designed, identified, and validated with real-world measurement data. For evaluation, we compare the EMPC with a well-tuned conventional Proportional-Integral (PI) controller. The results show that the EMPC significantly outperforms the PI controller in terms of GC, while it respects OTS. CCS CONCEPTS• Hardware → Power and energy.

No abstract

No abstract