Modellingand analysis of cost effective smart meter with decentralized CIS framework towards optimal network traffic

Abstract

The smart meter has become necessary technology due to the high demand for energy-relevant real-time data to monitor and control the smart grids. According to UN-SDG 7. b, smart meter plays a vital role in infrastructure expansion and technological upgrades to ensure affordable and sustainable energy services in developing and underdeveloped countries. Although the smart metering approach yields many benefits, it involves complete infrastructural changes on utility providers and consumer premises. Meanwhile, the research society widely concentrates on replacing existing metering infrastructure with the new smart meter. But this changeover results in uneconomical large-scale smart meter deployments, including communications infrastructures and maintenance costs. Unfortunately, very few researches were conducted on utilizing the existing metering infrastructure to achieve the benefits of smart metering. And also, the viability of such an implementable model is less common. Aside from this, as advanced metering infrastructure emerges, smart grid communication is experiencing an increase in data traffic due to the collection of huge volumes of data. Consequently, this paper proposes a Network Enabled Smart Energy Meter that can network existing multifunctional digital energy meters without replacing them for smart meters, while reducing network traffic through a decentralized framework. The proposed design is simulated and tested in Proteus software to validate basic and decentralized data processing operations. Obtained results show that without changing metering infrastructure at consumer premises, smart metering can be incorporated using Network Enabled Smart Energy Meter with lower deployment cost for a group of houses compared with existing metering technologies. Moreover, decentralized data processing frameworks are used to reduce data traffic in smart grid communication networks, and their results are discussed in the comparative data volume analysis section.

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