Model predictive control for demand flexibility of a residential building with multiple distributed energy resources

Abstract

One of the major challenges with increasing penetration of renewable energy sources (e.g., solar and wind) is to maintain grid stability. Grid-interactive efficient buildings are regarded as a promising approach to advance the role buildings can play in energy system operations and planning. This paper evaluates the demand flexibility in a residential building located in Karlsruhe, Germany, incorporating multiple distributed energy resources, including a photovoltaic and battery system, an electric vehicle, an electric water heater and a heat pump. A multi-criteria optimization problem is formulated with model predictive control (MPC) for five cases of demand flexibility. The simulation results of five winter days show that the peak power during the peak periods can be reduced by 92%, 69%, 100%, and 100% for real-time pricing, demand limiting, load shedding, and load shifting, respectively. For the power tracking scenario, the MPC tracked the reference power profile successfully for 70% of the simulation time. Achieving demand flexibility does not necessarily cause an increase in energy costs.