Towards an Efficient Energy Management to Reduce CO2 Emissions and Billing Cost in Smart Buildings

Greenhouse gas emissions are an emerging issue that poses a serious environmental problem and threat the entire world. Electricity production process is considered among the principal CO2 emitters since it relies heavily on non-renewable sources like coal and natural gas. However, switching to green electricity generation as hydro or wind is not yet evident because they represent intermittent sources that are highly weather-dependent and can meet now at maximum 14% of electricity generation needs. Moreover, green electricity is often more expensive than non-green ones. As governments and utilities worldwide seek to reduce carbon footprints and conserve energy, they are interested to use further wireless Internet of Things (IoT) technologies to transform traditional energy infrastructure into interconnected smart grid. Smart meters are an essential element and usually the first milestone in smart grid implementations. In this context, we propose an optimization model embedded in these IoT devices (i.e. smart meters) to intelligently schedule energy consumption from renewable & non-renewable electricity providers and an energy storage system (battery) to meet smart buildings electricity requirements. The proposed optimization model takes into consideration several constraints, namely the availability as well as the electricity price of each source. The goal of this model is to find the best proportion of using each source in order to reduce CO2 emissions but also taking into account the minimization of consumer's billing cost. Simulation results prove the efficiency of our model either with or without using carbon emissions tax to penalize using non-green energies.