Building automation system for grid-connected home to optimize energy consumption and electricity bill

This paper proposes an algorithm for coordinated control of the distributed generators integrated to a DC microgrid (DCMG), in islanded and grid connected modes of operation. The proposed DCMG connects photovoltaic (PV) panels, energy storage (i.e. battery bank and hybrid vehicle) and public utility access to supply the load of a house. The integration of distributed sources with DCMG depends on their feed in tariff (i.e low feed in tariff high integration priority). Moreover the microgrid electricity price is the function of feed in tariff of a power source, and their power feed in the DCMG. Therefore, DCMG prices are dynamic. The non energy storable appliance (NESA) and energy storable appliance (ESA) control algorithm has been proposed for load controlling. The electricity price is one of the parameter for ESA control algorithm. The ESA control algorithm shifts the operating time of energy storable appliances without affecting the user comfort to optimize the electricity bill with the same amount of energy consumption. The control action of NESA control algorithm depends on the occupant presence and other parameters such as illumination, temperature in the house. So the NESA control algorithm reduces the energy consumption in the house as well as cost of the energy. The SCADA system is used to interface with the smart sensors of the house and to execute the NESA and ESA control algorithms to switch the appliances. The results from a case study of a home in Alinagar (India) is presented in this paper in order to evaluate the energy efficiency of the load control system and to analyses the luminous, and temperature conditions and occupancy obtained through the application of this technology. The environmental and energy performances, together with the degree of users satisfaction and acceptance of this control system, were analysed for a day to verify the potentiality and operation of this load control system. The obtained results regarding the potential in savings of energy (31%) and electricity bill (35%) were evaluated taking into account both the monitored annual electric energy consumption (for operation) and the parasitic energy consumption due to the installed devices (sensors and controllers), and were compared with the estimated energy consumption with manual load control.