Performance of Advanced Metering Infrastructure using cellular communication based on uplink CDMA

The demand for enhanced energy management systems is being driven by the desire for sustainable development and the rapid evolution of energy consumption patterns. This study examines the potential for advanced metering infrastructure (AMI) and smart meters to enhance the efficacy of energy transmission and consumption. Smart meters enable customers to monitor their energy consumption in real time, thereby increasing their awareness of their actions and motivating them to adopt more environmentally favorable behaviors. These technologies facilitate two-way communication between utilities and end consumers when integrated with AMI. These features include demand response, automatic invoicing, dynamic pricing, and power outage monitoring. The integration of data analytics and the Internet of Things by AMI enhances the environmental sustainability, operational efficiency, and reliability of the grid. This paper investigates the design, advantages, disadvantages, and potential future improvements of smart energy systems that are powered by AMI and smart meters. We are of the opinion that these technologies will be beneficial in the creation of an intelligent and resilient energy system

This document contains sufficient information about the CDMA propagation model applied in our AMI (Advanced Metering Infrastructure), In this proposal the aim is to provide more supply, reading, cutting and re connections, prepaid services that focus on the specific characteristics of the inhabitants of rural areas and services that let you know the power consumption in real time in relation to the cost that represents it. To what smart meters to provide that information to our central information and be propagated in cell phone for the subscriber has its consumption without expecting a return is required. The use of CDMA technology in electrical measurement systems help reduce the costs of non-technical losses and that is where we can make the most of this new system as it would improve the quality of the electrical system getting real data readings and allowing improve communication between supplier and consumer.

This effort to make the power grid more intelligent is tightly coupled with the deployment of advanced metering infrastructure (AMI) as an integral part of the future vision of smart grid. The goal of AMI is to provide necessary information for the consumers and utilities to accurately monitor and manage energy consumption and pricing in real time. Immediate benefits are enhanced transparency and efficiency of energy usage and the improvement of customer services. Although the road map toward successful AMI deployment is clearly defined, many challenges and issues are to be solved regarding the design of AMI. In this paper, a multi-agent AMI based on the fog-computing approach is presented. Architecture follows structural decomposition of AMI functionalities encapsulated in a form of local and area-specific service components that reside at the different tiers of hierarchically organized AMI deployment. Fog computing concepts provide the framework to effectively solve the problems of creating refined and scalable solutions capable of meeting the requirements of the AMI as a part of future smart grid. On the other hand, agent-based design enables concurrent execution of AMI operations across the distributed system architecture, in the same time improving performance of its execution and preserving the scalability of the AMI solution. The real-time performance of the proposed AMI solution, related to the periodic and on-demand acquisition of metering data from the connected electricity meters, was successfully verified during one year of pilot project operation. The detailed analysis of the performance of AMI operation regarding data collection, communication and data availability across the deployed pilot AMI, covering several transformer station areas with diverse grid topologies, is also presented.

Smart grid is considered as next generation power system which performs automated control and monitoring operations to make it self-resilience. Advanced Metering Infrastructure is an essential part of smart grid which enables two-way communication thereby providing real-time communication link between consumer and grid operators. Emergence of Distributed Energy Resources (DERs) at end user make AMI's operation more challenging. Smart Meters (SMs) are key components in AMI that sends Power Consumption Data (PCD) of home appliances to Distributed System Operator (DSO). PCD received at DSO can be used for real time pricing applications, real time energy consumption, automatic diagnostic, daily energy metering and tasks such as billing, monitoring, planning and predicting of energy usage. In the context of selling of excess energy generated at DER's to the grid, SMs participates in Demand Response (DR) applications to perform energy trading. Revealing PCD of a home leads to privacy breach, hence it is necessary to achieve secure communication between SM and DSO. At the same time, it is essential to make energy trading transparent. To address this issue, this paper proposes a cloud based private blockchain framework to accomplish secure communication between SM and DSO without compromising privacy. Furthermore, trust between SM and DSO is achieved through smart contracts. Performance evaluation of the proposed blockchain shows its applicability to the said environment.

The protection of smart meters (SMs) from cyberattacks is of utmost importance because SMs in advanced metering infrastructure (AMI) are physically unprotected and produce a large amount of sensitive data. Due to scalability, the SMs are small-sized and low-cost devices having low computational capabilities. The algorithms that are designed to complete the security requirements of SMs should be lightweight. To address this issue, this paper proposes a lightweight security solution to address the man-in-the-middle attack, data tempering, and blockchain-based data provenance. Received signal strength indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient ( $\rho $ ) of 0.9102 is achieved when no adversarial node is present in between the connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of $\rho $ are computed when the adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain-based data provenance, all the hash values of the packet header are 100% matched with the hash functions present at the data concentrator unit (DCU), which shows no adversary’s involvement in AMI. For cases when the adversary is in the AMI, hash functions show no match with the hash values present at the DCU.

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

With the substantial increase in implementation of green technology across all sectors worldwide to overcome energy loss, attributes such as reliability, efficiency, productivity, and security become very crucial for a system. Especially with the integration of IoT and smart features into a system alongside the evolution of Internet, cybersecurity and privacy of an individual component should be taken into important consideration. A smart grid comprises wireless network components exposed to cyber-attacks, smart meters being one the most vulnerable part of an advanced metering infrastructure. An advanced metering infrastructure enables two-way communication for automatic meter readings (AMR) and transmits data through wireless network. To counter cyber-attacks afflicting the network of AMI and functioning of a smart meter, additional intrusion detection methods are implemented into the grid network. The review includes cybersecurity concerns associated with smart meters along with an in-depth insight to some major cyber-attacks and their aftermath.KeywordsCybersecurityCyber-attacks advanced metering infrastructure (AMI)Smart metersDenial-of-service (DoS)False data injection (FDI)Man-in-the-middle (MitM)

Internet of Things connects people and things at any time and in any place. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most significant components in smart grid which collects data from smart meters and sends the collected data to the utility center to be analyzed and stored. To implement two-way communication between smart grid components, machine-to-machine communications has a great potential. In this paper, we propose a machine-to-machine architecture for advanced metering infrastructure in Internet of Things-based smart grid and focus on finding the optimum number of collectors based on two metrics: collectors' cost and packet delay. We model advanced metering infrastructure as a queuing system and derive an expression for packet delay. We also propose a newperformance metric, the product of cost and delay (CD), so that by minimizing it, we can find the optimum number of collectors. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost and delay.

We are investigating a novel approach towards reliable and efficient protection of consumer privacy in the Advanced Metering Infrastructure (AMI). In the smart grid, one of the main concerns of consumers is associated with the usage of the smart meters and how utility companies handle energy consumption data, which can potentially reveal sensitive and private information about consumers. Current solutions provide privacy-preserving protocols using zero-knowledge proofs and homomorphic encryption, which work on aggregated smart meter data. There is still lack of an integrated solution that enables privacy preservation with access to fine-grained data such that opportunities of making energy consumption more efficient are not sacrificed. Such access will also enable other forms of advanced intelligent analysis like energy fraud detection. In this regard, we propose a three-tier privacy preservation model that includes secure communication among smart meters, utility company, and a Trusted Third Party (TTP) using Certificateless Public Key Encryption and AES 128. It is a flexible framework allowing protection of consumer privacy such that only consumers can securely retrieve their fine-grained readings through the TTP’s web-portal. This protocol supports dynamic rate utilization as well as data mining for advanced analysis. In addition, the proposed secure framework satisfies computational resource limitations in the Advanced Metering Infrastructure and provides a scalable solution for efficient consumer privacy-preserving billing.

Smart meters are key components of the smart grid infrastructure, connecting households to utility providers. With Internet connectivity to such devices, the smart grid Advanced Metering Infrastructure (AMI) is exposed to attacks by the adversary class. Any compromise of a smart meter may jeopardize the security of not just the household in question, but also the entire neighborhood network and possibly the utility provider's core network. One attack category that may pose a serious threat to the AMI is a meter compromise attack, wherein a smart meter is controlled by the adversary, with the intent of committing energy fraud. In this paper, we present a detection model based on a cumulative analysis of smart meter readings of clustered smart meters. The clustering of smart meters is done based on the physical deployment points of individual smart meters within a neighbourhood area network. Results obtained through simulation of the proposed scheme are promising.

The increasingly rapid development of technology accompanied by the ever-increasing consumption of fossil fuels has a negative impact on environmental sustainability and provides challenges related to innovation regarding these problems. Electric fuel vehicles are one of the relevant innovations to reduce air pollution and minimize exhaust emissions from fossil fuel vehicles on global warming. IMEI TEAM UMSIDA continues to contribute to the development of vehicles powered by electric energy by participating in energy-saving vehicle competitions such as KMHE and Shell Eco Marathon. In developing electric cars, the IMEI TEAM UMSIDA experienced problems related to obtaining information on the car's energy consumption in real time or when the car was on the track. This problem prompted researchers to create a tool that is capable of monitoring car energy consumption in real time and can be accessed remotely. The experimental method is the right method for this research, because with this method the researcher carries out many experiments to ensure that the tool system works well. The parameters produced in this research are voltage, current, power and energy values in units of time. This tool is equipped with an IoT-based communication system with an internet network as a link between the user, hardware system and software system which functions to carry out remote monitoring in real time. The test results show that this tool functions well and is able to carry out voltage measurements with an error percentage of less than 0.1% and current measurements with an error percentage of less than 0.2%.

An adaptive framework for real-time data reduction in AMI

The introduction of Smart Meter (SM) is a first stage towards Smart Grid to develop future energy system. The main difference of a SM with a traditional meter is that instead of only showing the current usage on the meter itself, it can also communicate the usage details directly to a Distribution System Operator. In order to make overall reduction in energy consumption, increasing awareness about the energy consumption is an important factor. The SM data gives that facility by visualizing the energy consumption in real time. The energy consumption can also be reduced by using low voltage Direct Current (DC) distribution in houses and offices. Several factors have increased the recent interest in DC power system. The number of devices that operate on DC continues to increase in both homes and offices. Most of the devices require Alternating Current (AC) to DC conversion between the AC supply and the DC side of the device. These need of conversion from 230V AC to low voltage DC inside the DC power consuming apparatus results in a low overall efficiency of the AC system. This work focused on feasibility of using 230V AC to 48V DC converter with SM for a house with low voltage DC distribution system.

Advanced Metering Infrastructure (AMI) enables two-way communication between smart devices and utility control centers. This involves remote monitoring and control of energy consumption as well as other parameters in the electrical power network in real time. However, increasing technologies in AMI due to huge deployment of smart meters, integration of devices and application of sensors, demand a strong architectural model with the best network topology to guarantee efficient usage of network resources with minimal latency. In this work, a resource efficient multi-hop network architecture is proposed using hybrid media access protocols. The architecture combines queuing and random-access protocol to achieve optimal network performance. Numerical results show that the probability of delay incurred by an arbitrary smart meter depends on the mean and distribution of the queue switch over a period. It is also observed that for a single queued system, the throughput performance is equal to the existing hybrid method. As the number of smart meters increases to 500, the throughput of the proposed method improves by 10% compared to the existing method. Likewise, as the number of smart meters increases to 500, the delay reduced by 15% compared to the existing method. Keywords: Advanced Metering Infrastructure; hybrid media access protocols; Smart Meter; Smart Grid; Power Network.

Nowadays, legacy electrical distribution systems are migrating to a new modern electric grid with the capability of supporting different applications such as advanced metering infrastructure (AMI), distributed energy resources, and electric vehicles. Among these applications, AMI is playing an important role in delivering data from customers to power utilities, supporting reliable real-time monitoring, and remote operation of power quality data and voltage profile. The AMI consists of smart meters, data aggregation points (DAPs), the utility control center, and communication networks. Appropriate network planning plays an essential role in facilitating the exchange of data between consumers and power utilities as well as accommodating new smart grid applications and future growth. This work proposes an optimal placement of DAPs for AMI based on machine learning clustering techniques in residential grids. Network partitioning is introduced to create sub-networks and graph algorithms generate a deployment topology given optimization constraints. A new measurement metric called coverage density is considered to indicate neighborhood area networks (NAN) zones with the appropriate coverage. Three real scenarios of NAN are considered: urban, suburban, and rural. The proposed algorithm is evaluated and compared with conventional heuristic optimization methods with respect to average and maximum distance between smart meters and DAPs, coverage density, and execution time.