Market and behavior driven predictive energy management for residential buildings

With the advancement of smart home and grid, a more connected and efficient operation of the grid is achievable. Involving buildings as the largest consumer of electricity in such a smart operation is a critical step in achieving an interactive grid system. In this paper, a building energy management system is introduced considering electricity price and people behavior, controlling major consumers of electricity in a single family residential building. An air conditioner, water heater, electric vehicle, and battery storages are controlled in a photovoltaic (PV) equipped building. A model predictive control is designed to minimize the operation cost considering system model, electricity price and people behavior patterns in each device control. Centralized and stand-alone configuration of MPC for building energy management is formulated and were put in contrast for time of use pricing (TOU), hourly pricing and five minutes pricing. Simulation results show that in real time five minutes pricing these methods can achieve 20%-30% cost savings in different appliances, and 42% savings in overall electricity cost adding battery optimal control compared to traditional rule based control. Cost savings and peak shaving results demonstrate the capabilities of introduced price and behavior based control.