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

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.

Advanced Metering Infrastructure (AMI), which is being considered as the first step towards smart grid, has been well acknowledged by distribution utilities for automating the management, operation and control of low voltage distribution systems with multifold installations happening across various countries. AMI offers smart meters, variety of protocols, sensors, network and software infrastructure providing two-way communications between utility & consumer. With the emergence of new system level concept in the smart distribution arena, Distribution Automation (DA) and AMI systems convergence, for developing next generation advanced distribution automation applications it is required to change the way in which AMI systems are currently being operated and provide additional capabilities or enhancements for integrated operation with DA system. In this paper we analyze the requirements for such AMI system based on state-of-the art technology advancements towards effective DA-AMI convergence operation along with a case study.

This The project, Development of MDMS (Meter Data Management System) and SUN (Smart energy Utility Network), as one of the core technology development projects for electric power industry which is funded by Ministry of Knowledge Economy, stands for the establishment of AMI system. AMI system is a core infra system for the implementation of Smart Grid system, and is a main mechanism for the realization of Demand Response based on the bi-directional communication between demand and supply. AMI system is also an intelligent infra system having a high priority for operation of future Smart Grid such as information interaction among various distributed power systems and intelligent DMS. The establishment of AMI system suffers from problems due to a huge number of outage events and real time response for those events. These problems necessitate the efficient outage management system (OMS). OMS is one of the best methodologies to show the influence of AMI system efficiently and visually. This implies the reduction of the recovering time caused from outage by the reduction of outage recognition time and the improvement of outage data quality, and also means efficient provision of information created from the introduction of AMI system. For the future, OMS based on AMI system is necessary and this study makes a contribution to solution and technology of the system.

As an important branch of the Internet of Things, the smart grid has become a crucial field of modern information technology. It realizes the two-way information flow and power flow by integrating the advanced metering infrastructure (AMI) and distributed energy resources, which greatly improves users’ participation. However, owing to smart meters, the most critical components of AMI, are deployed in an open network environment, AMI is a potential target for data integrity attacks. Among various attack types, the scaling attack is the most typical one, because it can be used as a general expression for most of other ones. By launching a scaling attack, adversaries can randomly reduce hourly reported values in smart meters, thereby causing economic losses. A number of research efforts have been devoted to detecting data integrity attacks. Nonetheless, most of the existing investigations focus on all attack types, and little attention has been paid to a detection strategy specially designed for scaling attacks. Our contribution addresses this issue in this paper and hence, developing a detection model of scaling attacks considering consumption pattern diversity (SA2CPD), to ensure that scaling attacks can be effectively detected when users have multiple consumption patterns. To be specific, we leverage Kmeans to distinguish different consumption patterns, and then the consumption intervals can be extracted to binarize the data. We divide time periods in every day into two categories based on the binarization values, and use one of them with the greatest information gain to construct a decision tree for judgment. Both theoretical and simulation results based on the GEFCom2012 dataset show that our SA2CPD model has a higher F1 score than the decision tree model without considering consumption pattern diversity, the KNN model and the Naive Bayes model.

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.

Non-technical losses including electricity theft and anomalies in meter readings are estimated to cost the utility providers tremendous losses of approximately $96 billion per annum. The adoption of smart meter has encouraged utility providers to use analytics to identify theft. To curb nontechnical losses, they are increasingly leveraging on real-time smart metering and analytics to identify energy theft and irregularities in meter readings. We have previously put forward linear regression-based and linear programming-based anomaly detection frameworks to study consumers' energy consumption behavior for detecting the localities of metering defects as well as energy thefts. In this work, we design and construct an advanced metering infrastructure test rig in the laboratory to perform comparison studies on our previously proposed anomaly detection frameworks in smart grid environment. Results from both test rig and simulations show that linear regression-based anomaly detection framework is able to identify the positions of energy thieves and faulty smart meters without requiring large volume of data samples. However, linear programming-based framework is more robust as compared to linear regression-based because the former is capable of detecting more sophisticated types of energy theft/meter irregularities accurately even in the presence of technical losses/calibration errors.

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)

Public-key cryptography (PKC) is widely used in smart grid (SG) communications to reduce the overhead of key management. However, PKC comes with its own problems in terms of certificate management. Specifically, certificate revocation lists (CRLs) need to be maintained and distributed to the smart meters (SMs) in order to ensure security of the communications. The size of CRLs may grow over time and eventually may introduce additional delay, bandwidth, and storage overhead when various applications are run on SG. In this paper, we propose novel algorithms for creating customized CRLs with reduced size for IEEE 802.11s-based advanced metering infrastructure (AMI) networks. Rather than maintaining a huge-size single CRL that introduces unnecessary search time and storage, the idea is to cluster/group SMs within the AMI network and create CRLs based on these groups. The grouping is mainly done in such a way that they bring together the SMs that will be very likely to communicate so that the CRLs will be kept local to that group. To this end, we propose two novel grouping algorithms. The first algorithm is a bottom-up approach, which is based on the existing routes from the SMs to the gateway. Since the SMs will be sending their data to the gateway through the nodes on the route, this forms a natural grouping. The second approach is a top-down recursive approach, which considers the minimum spanning tree of the network and then divides it into smaller subtrees. Via grouping, the length of the CRL for each SM and the corresponding distribution overhead can be reduced significantly. Simulation results have shown that our approach can maintain a balance between the size of the CRL and the number of signatures generated by CAs while guaranteeing security of the communications.

Energy losses, due to theft, within the South African electricity distribution networks are adding up to millions of kilowatt hours unaccounted for yearly. With many municipal consumers still having electricity meters within the boundary of their property and the energy theft methods becoming more sophisticated, e.g. partially bypassing electricity meters or ghost vending, the conventional Two Point Measuring Hardware Based non-technical loss detection (NTLD) method can still be implemented as a cost-effective solution within the South African municipal space, but with some amendments. The focus area of this research is on how to amend the conventional Two Point Measuring Hardware Based NTLD method by leveraging on advanced metering infrastructure (AMI) solutions, in conjunction with existing billing and vending systems. This paper presents the hypothesis that if South African municipalities augment their current distribution networks with a strategically and temporarily placed AMI solution that is integrated with existing billing and vending systems, it is possible to detect the location of the origin of complex non-technical losses instances. The key findings of the research suggest that by strategically and temporarily utilizing an AMI solution integrated with existing billing and vending systems, the conventional two point measurement hardware based NTLD methods can be successfully amended to achieve the desired outcome, despite the existing challenges.

The delivery of energy and water meter data, management and control information on separate networks is expensive and defeats the gains of the Advanced Metering Infrastructure (AMI) Smart Grid (SG). In most cases, energy, gas and water services are offered by the same organizational entity, hence the use of different infrastructure for data, service delivery, control and management is expensive and highly illogical. There is a need for a combined energy and water infrastructure to reap the benefits of the AMI SG. Furthermore, combined metering will result in accurate billing, potential cost savings, and improved resource management. This work therefore develops and investigates a combined energy and water AMI smart metering framework. This is possible through a thorough understanding of the AMI technological standards. The implementation of such a system is not trivial, as it depends on many factors: environmental, geographical, technological, economical, regulatory and the existing legacy infrastructure. Optimal technological implementation choices are developed towards an integrated AMI infrastructure. An experimental test bed is developed for delivering energy and water metering data to the utility. The optimal placement results favor the system of separating energy and water actuators at the home area network of the SG while using an integrated communication system. Such a system is feasible, given the different evolution of electricity and water meters and their placement at the home area network, and enables water metering to benefit from the more advanced electrical metering infrastructure.

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.

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.

Reducing the power supply-demand gap and increasing reliability of power supply are the challenges of current energy management. Implementation of smart grid, smart meters and smart metering can be a possible solution for power demand reduction, efficient power supply management, and optimization of management resource usages. Smart meters include sophisticated measurement and calculation hardware, software, calibration and communication capabilities. For interoperability within a smart grid infrastructure, smart meters are designed to perform functions, and store and communicate data according to certain standards. In this work we discuss smart meter and various elements of smart metering, current state of the technologies related to smart grid, smart meter, advanced metering infrastructure (AMI), and meter data flow in smart grid. We also discuss standards related to smart meter, meter data format and data transmission, functions of smart meter, and functionalities of smart meters, currently deployed by utilities around the world.

The most advanced power grid design, known as a ‘smart power grid’, integrates information and communication technology (ICT) with a conventional grid system to enable remote management of electricity distribution. The intelligent cyber‐physical architecture enables bidirectional, real‐time data sharing between electricity suppliers and consumers through smart meters and advanced metering infrastructure (AMI). Data protection issues, such as data tampering, firmware exploitation, and the leakage of sensitive information arise due to the smart power grid's substantial reliance on ICT. To maintain reliable and efficient power distribution, these issues must be identified and resolved quickly. Intrusion detection is essential for providing secure services and alerting system administrators in the case of adversary attacks. This paper proposes an intrusion classification scheme that identifies several types of cyber attacks on modern smart power grids. Grey‐Wolf metaheuristic optimization‐based feature selection is used to learn non‐linear, overlapping, and complex electrical grid properties. An extended deep‐stacked ensemble technique is advanced by putting predictions from weak learners (CNNs) into a meta‐learner (MLP). The outcomes of this approach are explained and confirmed using explainable AI (XAI). The publicly available dataset from Mississippi State University and Oak Ridge National Laboratory (MSU‐ORNL) is used to conduct experiments. The experimental results show that the proposed method achieved a peak accuracy of 96.6% while scrutinizing the original MSU‐ORNL data feature set and a maximum accuracy of 99% when analysing the selected feature set. Therefore, the proposed intrusion classification scheme may protect smart power grid systems against cyber security attacks.

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.