MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages

The mismatch between the energy demand and energy supply is one of the major problems in low or zero energy buildings with distributed power generations. The policy of time-sensitive electricity pricing provides a possibility to improve the energy efficiency of the building energy systems. A model predictive control (MPC)-based strategy using nonlinear programming (NLP) algorithm is proposed to optimize the scheduling of the energy systems under day-ahead electricity pricing. Evaluations are conducted using a reference building based on the Hong Kong Zero Carbon Building. A stratified chilled water storage tank is introduced as the thermal energy storage, which makes possible to optimize the scheduling of the building energy systems. The distributed power generation in the building consists of a combined cooling and power system and a photovoltaic (PV) system. Two types of grid-connections (i.e., selling electricity to grid is allowed/forbidden) are considered. Results show that the proposed optimal scheduling strategy can achieve significant reductions in carbon dioxide emission, primary energy consumptions and operation cost. Sensitivity analysis shows uncertainties in the inputs do not affect the performance of the proposed method significantly.