Predictive Control of buildings for Demand Response with dynamic day-ahead and real-time prices

This paper proposes a Model Predictive Control (MPC) scheme to control residential buildings with space heating/cooling loads, an Electric Water Heater (EWH), photovoltaics (PV) and battery storage in a time-varying electricity price environment. The controller uses models for the system components as well as predictions for future disturbances such as weather conditions, occupancy and electricity prices to find the building operation that minimizes electricity costs over the prediction horizon while respecting user comfort constraints. The building operation is investigated under three different price scenarios: (1) a simple day-night tariff for end-customers, (2) a day-ahead dynamic tariff reflecting the wholesale market marginal costs and (3) a real-time dynamic tariff. Residential buildings response to such price signals is investigated in a case study and their potential for Demand Response (DR) programs is evaluated.