Optimal Allocation of Smart Meters to Real or Virtual Data Concentrators

Abstract: The Smart Grid is the new, modernized, electrical distribution grid. It allows for many applications such as automation, reliability and efficiency of electrical distribution. A key feature of the smart grid is the AMI (Advanced Metering Infrastructure) which is the system that measures, collects and analyzes energy use.When designing an LV/MV AMI smart grid, one can use several different media types, two of those are PLC (Power Line Communication) and GPRS (General Packet Radio Service). Choosing one over another may have differences in both communication performance and overall economic cost.This thesis describes a method to optimally assign the smart meter communication devices in an AMI network to either the secondary substation via the power lines (PLC) or towards the head-end via GPRS. When assigned to the secondary substation, the data collected from the smart meters will be managed by a Data Concentrator or forwarded by a Gateway towards the head-end where a Virtual Data Concentrator would be located. As an alternative to PLC communication, GPRS can be used to wirelessly communicate between smart meters and the virtual data concentrator.The method proposed uses MatLab to read the users input data, such as smart meter and power line link locations. The read data then gets compiled into a network topology consisting of smart meter nodes and power line links between them. For easier comparisons, the network is then clustered into groups. The clustering is done using two methods, one fore PLC and another for GPRS.The cluster data will then be compared with the packet loss ratio data acquired from either simulations or other sources, stored in a repository. The comparisons, along with various constraints set by the user, will then determine if the communication is fit for use.If deemed fit for use, each type of communication have their CAPEX and OPEX costs calculated, based on the users input. This thesis will use data acquired from DSOs (Distribution System Operators) to analyze the costs of four network locations and determine the cheapest assignments for each one for various settings.