A Smart Meter Infrastructure for Smart Grid IoT Applications

Abstract

Electric infrastructures have been pushed forward to handle tasks they were not originally designed to perform. To improve reliability and efficiency, state-of-the-art power grids include improved security, reduced peak loads, increased integration of renewable sources, and lower operational costs. In this framework, "smart grids" are built around bidirectional communication technologies, where "smart meters" communicate with all other entities and collect data from the power grid, offering specific features to each actor playing in the energy marketplace. In this paper, to overcome some of the challenges raised by smart grids and smart meters, we propose a distributed metering infrastructure which provides bidirectional communication, self-configuration, and auto-update capabilities. Our 3-phase smart meters follow the basics Internet-of-Things principles and have the ability to run, either on-board or distributed on the network, multiple algorithms for smart grid management. These algorithms can be freely added, updated, or removed on-the-fly thanks to the auto-update feature of the system. Moreover, to reduce costs and improve scalability, we prove that it is possible to implement our smart meters using only off-the-shelf and inexpensive hardware devices. A digital real-time simulator (i.e., Opal-RT) has been used to assess the capabilities of both the infrastructure and the meter. Our experimental analysis shows that the latency introduced by the data transmission over the Internet is compliant with the limits imposed by the IEC 61850 standard. As a consequence, our architecture does not affect the operational status of the smart grid, making it a viable solution to support the deployment of novel services.

Data Interworking Model and Analysis for Harmonization of Smart Metering Protocols in IoT-Based AMI System

“…They discuss the communication protocols, data format, data gathering procedure, and decision system based on big data. Orlando et al [ 21 ] suggested a low-cost smart meter architecture for AMI and made a prototype of smart meters that follow the basic IoT principles for smart grid management. Furthermore, they verify the performance of the smart meters using a hardware-based digital real-time simulator.…”

A Service Oriented Architecture for the Digitalization and Automation of Distribution Grids

“…This proposal provides the details about the functional interactions between power system and IT components, but possible implementation at the edge and integration of legacy systems are not discussed. In [39], microservices are instead used to deploy some distribution grid applications directly at the edge. Also in this case, no details are provided for the integration of legacy systems and a comprehensive view of the overall DMS implementation is missing.…”

Research on short-term power load forecasting of smart grid based on residual DBSCAN-CNN-LSTM model

“…; and third, hybrid methods, which can be further divided into two categories, one is to use a variety of methods to separately The first is to use multiple methods to forecast loads separately and then synthesize them to form the final prediction results, and the second is to decompose the loads first, and then sum up the final prediction results after forecasting each component separately. Most of the above load forecasting techniques take the time series curve as input and rely on mathematical and statistical methods and a priori knowledge to construct mathematical models [3], without exploring the information contained in the load curve in depth, and there is still room for improving the self-adaptation and self-learning ability of the forecasting model [4].…”

“…With the expanding demand-side resources in the power system, in-depth research on demand response and the utilization of demand-side resources to reduce carbon emissions have become a research weight. The literature [4] constructs a two-stage economic dispatch model for microgrids that takes into account the value of active/passive demand response and conditional risk to reduce the potential dispatch risk of the system. A day-ahead economic dispatch model is constructed by considering the load-side tariff-based demand response and targeting the wind power consumption rate and low-carbon operation [5].…”