Uncertainty-Cognizant Model Predictive Control for Energy Management of Residential Buildings with PVT and Thermal Energy Storage
This work addresses energy management for residential buildings to reduce costs and integrate renewable resources, representing an incremental improvement in control methods for specific building systems.
The paper tackles the problem of minimizing operating costs for residential buildings by developing a stochastic model predictive control strategy to manage a system with heat pumps, thermal energy storage, and photovoltaic thermal collectors, accounting for uncertainties in energy generation to shift electric demand and enable energy arbitrage.
The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic thermal (PVT) collectors to supply both electric and thermal energy demands of the building with minimum operating cost. To this end, the paper develops a stochastic model predictive control (MPC) strategy to optimally determine the set-point of the whole building energy system while accounting for the uncertainties associated with the PVT energy generation. This system enables the building to 1-shift its electric demand from high-peak to off-peak hours and 2- sell electricity to the grid to make energy arbitrage.