Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid

Home energy consumption is increasing, and renewable energy sources are being deployed. The use of renewable energy sources as an alternative to fossil fuels and nuclear energy is a significant opportunity for reducing CO2 emissions. However, carbon emissions from electricity generation are dependent on the type of renewable energy source and the quantity of the electricity that is produced. With the emergence of the smart grid, residents are able to reduce their electricity consumption by shedding the power usage in their homes. The objective of this study is to smooth power peak demand and diminish CO2 emissions. This paper describes the power shedding benefits of reducing energy consumption and CO2 emissions. This reduction of CO2 emissions eliminates the need for auxiliary power plants in the grid in the case of peak demand points and the need to switch to renewable energy sources. In this study, we prove that emerging information and communication technology can reduce energy use and CO2 emissions. Wireless communication architecture is introduced in this study to increase grid flexibility and rapidity in the data flow among all of the components in the proposed system. Our proposed system with local renewable generation can reduce CO2 emissions nearly 72% of all CO2 emissions when all appliances are supplied with the grid. With our load shedding algorithm, the CO2 emissions decrease nearly 91%. The yearly estimation shows that our system can reduce 62% of CO2 emissions and 37% of consumed power. In this paper, we introduce an efficient architecture of a smart home energy management system and propose a shedding algorithm for home energy usage. This system is based on wireless communication among home appliances, a home management and control system, a grid management and control system and domestic renewable energy sources. Our proposed system, which employs wireless communication, domestic renewable generation and a power shedding algorithm, can operate in conjunction with future smart grids by applying real-time data, such as peak demand points. Numerous real data sets permitted the development of some of the statistics in the system results.