Energy management of space-heating systems and grid-connected batteries in smart homes

Several approaches of energy management systems reduce power consumption of heating demand and electricity storage based on static or dynamic tariffs. However, such methodologies impose uncertainties due to forecasting errors of energy consumption and generation, while evaluating electricity prices. Alternatively, this paper proposes a novel methodology of residential energy management to decrease electricity consumption of space-heating units and grid-connected batteries without incorporating price signals, while maintaining their characteristic operation. The proposed algorithm of energy management develops seasonal calculations of heating load and storage power to achieve energy savings in smart homes based on mixed-integer linear programming, considering photovoltaic electric generation. Power consumption of heating systems is estimated considering heat losses of conduction and ventilation through buildings in addition to other important parameters such as outdoor and indoor temperatures. Charging and discharging patterns of grid-connected batteries are modelled consistent with residential loads. Simulation results show that the proposed algorithm of energy management is able to reduce energy consumption of space-heating loads by 15%, mitigating their environmental impact while keeping their functioning usage. Moreover, the algorithm decreases charging demand of grid-connected batteries by 13%, maintaining their state-of-charge levels between 10 and 90%.