Wireless Sensor Networks and Advanced Metering Infrastructure Deployment in Smart Grid

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.

In smart grid, Advanced Metering Infrastructure (AMI) systems feed the metering data to Energy Supplier (ES) every 15 minutes. Using this information, ES can offer incentives to reduce the total power consumption during the peak hours through the real-time pricing mechanism. However, it also brings privacy concerns. Since the metering data is collected by the ES nearly in real-time, the customer's behavior and habits can be disclosed via the statistical patterns. The privacy problem will hamper the development and deployment of future smart grid. In this paper, we propose a novel metering data transmission scheme in wireless networks. In each Smart Meter (SM), the metering data is split into two parts randomly: Part 1 and Part 2, where the sum of two parts is equal to the original metering data. Part 1 is sent to the ES via cellular link, and Part 2 is sent to the next SM via Wireless Sensor Network (WSN) link. Then the next SM merges Part 2 of the previous SM into its original metering data, and creates the updated metering data. Using the proposed scheme, the metering data received by the ES is randomized, and the customer's privacy can be effectively protected even if some SM cooperates with the ES.

This chapter introduces the background of the smart grid, including its motivations, communication architecture, applications, and requirements. In order to achieve the objectives of the smart grid, several important applications must be added to or upgraded in the current power grid. The most important applications and requirements include demand response, advanced metering infrastructure, wide-area situational awareness and wide-area monitoring systems, and communication networks and cybersecurity. The chapter explores some of the major topics in smart grid communication infrastructures and provides solutions and suggestions. It proposes a complete information and communication technologies (ICT) framework for the smart grid. The chapter introduces big data analytics and cloud computing into the smart grid communications to enhance grid operations and control. It also proposes security schemes for communications in the advanced metering infrastructure, and ID-based security schemes for transmission over the Internet in the smart grid.

KAHRAMAA initiated AMI pilot project in 2012 for 17,000 customers including both, residential and commercial categories in three different zones in Doha, Qatar. AMI Pilot project aims to evaluate technology suitability and feasibility to overcome the regional challenges for roll out. As I am sure everyone is aware of the complexity involved in AMI deployment, it is very important to identify all the technology and process gaps to have a successful roll out. As Smart Metering technologies are still emerging; it is very important to explore various technologies available in the Market. We would like to share the challenges we have faced during the implementation phase to help other utilities to consider these painful points before moving ahead with their AMI implementation. We would also like to recommend some best practices to ensure smooth and successful implementation. AMI System helps Utility to measure customers consumption and power quality parameters remotely and it supports On-Demand Reads, Connect/Disconnect commands that saves operational cost for Utility. Smart Meters enable utility to provide customers with detailed information about their energy usage, which in turn enables customers to manage their energy use more proactively. Smart Meters are the first step toward creating a Smart Grid. As with the Smart Grid; digital technologies are applied to every aspect of the industry, from generation, to transmission, to distribution, to the customer interface. This will help the grid sense what is happening to the energy flow, keep it in balance and improve reliability. Through the power quality parameters, utility can plan and operate their network more efficiently.

Emerging Smart Grids have given rise to deployment of advanced metering infrastructure (AMI) which produces voluminous load data with respect to time. The information extracted from this data can potentially be used to design Demand Side Management (DSM) policies. The major impediment in the deployment of DSM is the lack of knowledge of individual consumers' load demand to determine target consumers for the incorporation in DSM. This paper proposes an innovative probabilistic approach to generate typical load profiles of consumers using smart meter data. The method incorporates clustering and then re-clustering the individual clusters using the k-means clustering algorithm until reliable load profiles showing true load-time characteristics of consumers are extracted. Next, the multi layered re-clustering methodology is applied to generate typical load profiles for consumers on an alternative scale by segregating them into different classes of load levels in such a way that they represent typical smart meter consumers of the population. The approach enables the utility to determine the consumers whom can be applied the DSM actions for an effective operation of a smart distribution network and to reduce the load peak and rebound effects.

Indian grid now totally synchronized and working as National Grid is poised for multi-prong development. It has already implemented and has ambitious plans for Smart Grid applications in both transmission and distribution segments. With the progress in Information and Communication Technology, full advantage of it is being taken for evolving the Smart Grid. A number of measures like implementation of wide area monitoring system using phasor measurement technology, continuance of network embedding with HVDC system, etc. are the means at transmission level to achieve the goal in this respect. While integration of renewable energy sources to grid with energy storage and transmitting surplus to far-flung areas of the country through interconnection is being made possible, on distribution front due to deployment of advanced metering infrastructure, demand response enabled peak load management is being attempted. With the proliferation of electric vehicle and other modes of transportation based on electricity, further step is contemplated to take care of the same, both as load and storage with increase in number.

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.

This survey paper is an excerpt of a more comprehensive study on Smart Grid (SG) and the role of Advanced Metering Infrastructure (AMI) in SG. The survey was carried out as part of a feasibility study for the creation of a Net-Zero community in a city in Ontario, Canada. SG is not a single technology; rather it is a combination of different areas of engineering, communication and management. This paper intends to focus on AMI, which is responsible for collecting all the data and information from loads and consumers, as the foundation for SG. AMI is also responsible for implementing control signals and commands to perform necessary control actions, including Demand Side Management (DSM). In this paper we introduce SG and its features, establish the relation between SG and AMI, explain three main subsystems of AMI and discuss related security issues.

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

Today, smart grid has been widely discussed in worldwide. As advanced metering infrastructure is one of main technologies of smart grid, its structure and functions of each component is represented. In terms of basic features of smart grid, some key capabilities that advanced metering infrastructure should possess are analyzed, and its impacts on power grid networks as well. Combining current research situation of power utility information acquisition system in china, this paper provides reference for building smart grid of user side on the basis of these preliminary analyses.

One of the requirements of a smart grid (SG) is making the electrical network and its subsystems aware of their condition. The deployment of various sensing devices plays an essential part in achieving this goal. Nevertheless, data generated by this deployment needs to be well managed so that it can be leveraged for operational improvement. Data aggregation is perceived as an important technique for managing data in the SG in general, and in its Advanced Metering Infrastructure (AMI) in particular. Indeed, data aggregation techniques have been used in order to reduce communication overhead in SG networks. However, in order to fully take advantage of the aggregation process, some level of intelligence should be introduced at concentrator nodes to make the network more responsive to local conditions. Moreover, by using a more meaningful aggregation technique, entities can be accurately informed of any disturbance. This paper contributes an agent-based approach for data and energy management in an SG. It also proposes CoDA, a correlation-based data aggregation technique designed for the AMI. CoDA employs fuzzy logic to evaluate the correlation between several messages received from Smart Meters (SMs). Analysis and simulation results show the benefits of the proposed approach w.r.t. both packet concatenation and no aggregation approaches.

The Advanced Metering Infrastructure (AMI) is a vital element in the current development of the smart grid. AMI technologies provide electric utilities with an effective way of continuous monitoring and remote control of smart grid components. However, owing to its increasing scale and cyber-physical nature, the AMI has been faced with security threats in both cyber and physical domains. This paper provides a comprehensive review of the crucial cyber-physical attacks and counter defense mechanisms in the AMI. First, two attack surfaces are surveyed in the AMI including the communication network and smart meters. The potential cyber-physical attacks are then reviewed for each attack surface. Next, the attack models and their cyber and physical impacts on the smart grid are studied for comparison. Counter defense mechanisms that help mitigate these security threats are discussed. Finally, several mathematical tools which may help in analysis and implementation of security solutions are summarized.

In this paper, we present a routing protocol design and implementation for the Advanced Metering Infrastructure (AMI) in Smart Grid. The proposed protocol implementation is based on the framework of the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), which is proposed by IETF and currently still in its design phase. RPL is based on the idea of maintaining a directed acyclic graph (DAG) structure for the network. We provide a practical implementation of RPL with a number of proper modifications so as to fit into the AMI structure and meet stringent requirements enforced by the AMI. In particular, we propose a novel DAG rank computation method and a reverse path recording mechanism, which enables real-time automated meter reading and real-time remote utility management in the AMI. Our proposed routing protocol design for AMI networks is validated through extensive simulations.

Internet of Things connects anythings at any time in any places. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most important components of smart grid which aggregates data from smart meters and sends the collected data to the utility center to be analyzed and stored. In this paper, we have proposed an aggregation-based indirect architecture for advanced metering infrastructure architecture in smart grid Internet of Things and focused on finding the optimum number of regional aggregators in its neighborhood area network (NAN) based on three metrics: regional aggregator (RA) power consumption, cost of RAs and wide-area aggregator, and NAN lifetime. We have developed a power consumption model for each RA and define NAN lifetime based on that. We also defined a new performance metric, the product of power consumption and cost (PC), so that by minimizing it, we can obtain the optimum number of RAs. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between NAN lifetime and RAs' cost.

A review of non-technical loss attack models and detection methods in the smart grid