Channel characteristics analysis in semi-basement scenarios for smart meter communication systems

Smart meters for enhanced water supply network modelling and infrastructure planning

The urge to manage daily energy consumption has now become a worldwide priority. The Internet of Things (IoT)-based smart meter has been widely used to monitor energy consumption in many households. LoRaWAN connectivity is confirmed to be one of the most reliable connectivity to be used as a network system for IoT use cases. It can fulfill the needs of low power, long-range, efficient, and affordable IoT technology in utility energy monitoring. LoRaWAN-based smart electric meters are designed in rural areas for monitoring and managing electric energy consumption. The smart electric meter system consists of smart kWh meters, LoRa gateway, network system, and dashboard for energy usage monitoring. The system design process consists of feature design, architecture design, data flow design, LoRa gateway site planning, and platform setup. The implementation can be concluded that LoRa can be used for smart electric meters in rural areas. A gateway can reach a maximum distance of 1.58 km, with an RSSI of -99.40 dBm ±4.56 dBm. LoRa gateway availability can reach 100% by implementing gateway protection systems. The LoRaWAN-based smart electricity meter has also proven to be costoptimized, efficiently reducing monthly costs to 92.12%.

The reliability of a power distribution system reflects its ability to supply uninterrupted electricity. The installation of smart meters improves reliability by minimizing outages and expediting restoration. Smart meters enhance efficient energy use, balance supply and demand, and strengthen system reliability. This study applies a simultaneous equations model and a modified Hyperlink Induced Topic Search algorithm to analyze the relationship between federal funding, smart meter adoption by electric utilities, and reliability, aiming to rank electric utility companies in the United States. Results show positive associations between federal funding and smart meter adoption rates, as well as between smart meter adoption rates and system reliability. The proposed network-based methodology ranks electric utility companies by ownership type. The research findings underscore the significance of smart meter adoption and provide valuable insights for policymakers and electric utility companies seeking to enhance power system reliability.

Electricity theft and inefficient monitoring of energy have been the major drawbacks that power distribution companies have been experiencing. Such drawbacks contribute to huge revenue losses and make energy management ineffective. Traditional energy meters do not have advanced monitoring functionality and features for detecting tampering. This makes the system more exposed. All these challenges could only be overcome with a less costly and efficient system by improving energy responsibility and enabling the identification of unauthorized use. The study developed and implemented a smart prepaid energy metering system that could be used in real-time monitoring and theft detection. The Arduino microcontroller used a current sensor (ACS712), a voltage sensor (ZMPT101B), and a relay (SRD-05VDC-SL-C) to calculate power consumption every second. Codes could be used to recharge the meter, while a SIM800 module allowed for tamper detection by sending an SMS should the cover be opened. The smart meter is supposed to show in real-time the reading of consumption, the usage, and the units available. The meter is supposed to work continuously, whether in the presence of an AC supply or there being a DC battery backup. The testing involved energy calculation, detection of tampering, and also the recharging action functionalities. The smart prepaid energy meter worked well, as it calculated the consumption of energy accurately. When the units ended, they disconnected the supply power. It also has tamper detection through which it was able to send SMS notifications reading “Possible Energy Theft” in case of unauthorized interference. Further, it attains 20 hours of operation due to DC battery support when there is no power. Hence, it gives reliable measures. This system offers a practical solution for real-time energy monitoring, enhancing theft detection and promoting efficient energy management.

© 2014 IEEE. Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A countermeasure technique which uses support vector regression and impact difference for detecting anomaly pricing is then proposed. These pricing cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyber-Attacks in the smart home context. Our simulation results demonstrate that the pricing cyberattack can reduce the attacker's bill by 34.3% at the cost of the increase of others' bill by 7.9% on average. In addition, the pricing cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results show that the proposed countermeasure technique can effectively detect the electricity pricing manipulation.

The Smart Grid concept is conceived as a means to optimize the operation and to improve the reliability of the electric delivery system. Many applications operating at the distribution level can be considered as Smart Grid functions because the results of the control applications ripple all the way into the transmission system and the generation plants. Unless these applications are properly integrated, the possibilities that the results create undesirable situations will occur. Accurate assessment of the state of energy system can be obtained from data collected from Smart Meters and other remote monitoring devices. A solid two-way communication system to handle transfer of massive amounts of data to a data repository computer is required. The communication system also serves as a control link between command center and remotely located control devices. This paper describes the requirements that link the architectural building blocks, the Smart Meters, Communication System and Data Repository and Management System, to form a superstructure that supports many functions and applications for Smart Grid applications.

Advanced meter infrastructures (AMIs) are now widespread and their importance within smart grid systems continues to increase with the advent of new applications. Performance analysis of the infrastructure is key to assess the limits of application deployment. However, due to the large-scale nature of AMI networks that are often composed of tens of thousands of nodes per collector, performance analysis is often carried out in contained experimental trials. To our knowledge, no thorough mathematical performance analysis of real-sized systems has been carried out so far. In this work, we present a model to analyze the performance of a large-scale RF-AMI system and show its application to large-scale real-case scenarios.

The upgrading of residential smart meters become the focus of power sector if the tiered pricing for electricity is implemented. This paper presents the design of distributed residential smart meter system based on C / S mode. The hardware modules such as monitoring host, power measurement, and power line carrier communication are briefly description. The development programs and the flow charts of the host computer software are given specifically. Engineering test proves that the residential smart meter system works stably and achieves the purpose of calculating the tiered pricing for electricity and monitoring electricity energy consumption of the users.

Deep space exploration is one of the three major aerospace activities of mankind in the new century, and deep space exploration is inseparable from the research on technologies of deep space communication. In the future, the goal of human space exploration will be extended to more and farther stars, analyzing the basic characteristics of the deep space communication link channel, and studying the specific communication problems of the nearer stars will be the necessary basis for the research of deep space communication. Focus on the characteristics of the communication channel between Earth–Moon and Earth–Mars. According to the influential parameters of different communication link, the impact of the communication link of Ka frequency band and below is simulated and analyzed to clarify each range of loss and the effect of each parameter, and the relevant channel characteristics of deep space communication link are obtained.

The Smart meters are used in the areas of generation, transmission, distribution and consumption. The capabilities of smart meter systems and grid networks, such as distributed intelligence and broadband capabilities can greatly enhance efficiency and reliability, but they may also create much new vulnerability if not deployed with the appropriate security controls. Much of the technology currently in use by the meters are outdated and in many cases unreliable. A system architecture implementing should recognize security threats and capture events that result not from external threats but from internal mistakes, with human error being a more common occurrence. An effective security approach enhances reliability because some security failures might be people failures, while others might be equipment failures, might be due to natural causes or might be deliberate. A simple perimeter defense is not sufficient; monitoring, both for events and physical actions, is required to bring the benefits of smart meters with minimal risk to this vital part of the infrastructure of modern life.

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.

This paper establishes a smart meter reading solution with Long Rang (Lo Ra) as the regional dedicated communication network to provide a communication guarantee for automatic meter reading in multiple application scenarios. First, the paper analyzes the key performance indicators of LoRa technology. Based on the analysis of system performance and function, combined with the requirements of the power industry and application scenarios, the architecture design, and hardware and software design scheme of smart meter reading system with LoRa are presented. On this basis, an optimization method of LoRa key performance indicators is proposed. Finally, after comparing the communication distance, communication energy consumption, and meter reading success rate, the superiority of the smart meter reading solution based on LoRa was confirmed.

We study the feasibility of utilising the RF transceiver of a mobile device to establish some kind of situation awareness. Our results show that the analysis of channel characteristics can provide additional and sufficiently accurate context information. This situation awareness is cheap in the sense that it can be obtained from already ongoing communication in a network of nodes. In this paper we present results from a case study with several USRP software radios. We show how the presence, position and activity of persons in a room can be obtained exclusively from channel measurements.

Technology users and standardization: Game changing strategies in the field of smart meter technology

The demand for energy and growing concerns for the environment have led to the advancement of sustainable practices such as solar energy generation. Improvements in solar cell technology have led to an increase in photovoltaic (PV) generation, especially in remote regions. In order for PV grid systems to operate successfully, there must be a way to monitor and manage the data that is produced. This paper proposes a smart, remote monitoring system that will allow for an operator to monitor, store, and manage grid data in real-time. The smart monitoring system includes the use of an eGauge Pro smart meter at its heart, sensors, and interconnections with other grid electronics. The eGauge is a smart meter, data logger, and has a built-in web server, that allows it to communicate with the systems sensors, that connect grid devices such as the solar inverters, battery storage system, and solar arrays. An operator can connect the meter into a live grid system and begin capturing sensor data. Once the meter has all of the physical connections necessary, the operator is able to configure the meter through the web server. From the server, all of the sensors are registered, labeled, and available for display numerically and graphically, both locally and remotely. These capabilities provide the ability for grid data to be monitored remotely, anywhere there is network connectivity and customized for in-house or commercial use, packaged in a low-cost system. Thus, unique alerts and notifications are designed to better facilitate grid maintenance and protection.