Smart Grid Advanced Metering Infrastructure Research Articles

Incorporating quantum key distribution and reinforcement learning for secure and efficient smart grid advanced metering infrastructure

Incorporating quantum key distribution and reinforcement learning for secure and efficient smart grid advanced metering infrastructure

Developing an Intelligent Decision Support System for large-scale smart grid communication network planning

The placement of routers and gateways in wireless communication networks for Smart Grid Advanced Metering Infrastructure (AMI) is a complex problem often addressed in the literature through heuristics, clustering, or linear programming approaches. In large-scale scenarios, the complexity is directly related to the region’s number of smart meters and poles. The terrain profile can introduce signal quality degradation between a smart meter and the routers and gateways, further complicating the problem. This study presents the AIDA-ML method, representing a preliminary step toward developing an Intelligent Decision Support System (IDSS) for an effective positioning strategy. AIDA-ML incorporates a feature engineering-based strategy and utilizes machine learning algorithms to learn from the results computed by a heuristic method called AIDA. The heuristic approach is too time-consuming, relying on external resources like numerous terrain profile API calls and a deep understanding of wireless communication loss models. To implement a machine learning-based and more straightforward approach, a feature engineering process is employed to capture the operational characteristics and results of the analytical method while requiring less processing time. Through experiments conducted with real-world data from 26 cities in the state of Paraná, Brazil, comprising 466,237 smart meters and 352,867 poles, the results obtained using machine learning algorithms suggest that AIDA-ML can ensure the connection coverage of smart meters while meeting the same minimum requirements established for the analytical method. Moreover, AIDA-ML offers the advantage of reducing the processing time by 87.60% and by 96.86% the overall search space size compared to the heuristic approach.

A Novel Evasion Attack Against Global Electricity Theft Detectors and a Countermeasure

The smart grid advanced metering infrastructure (AMI) is vulnerable to electricity theft cyber-attacks in which malicious smart meters report low readings to reduce the consumers’ bills. To avoid this problem, several machine-learningbased detectors have been proposed to detect electricity theft. Most of these detectors are global in the sense that they are trained on different consumption levels, including low and high consumptions, to be used for all consumers. In this paper, we introduce a novel type of evasion attacks against global detectors as follows. A malicious consumer who has high consumption level can send false readings for a low consumption profile (that resembles the profiles the detector is trained on) to evade the detector, i.e., steal electricity without being detected. We firstly conduct experiments to prove that the existing global detectors are vulnerable to this new kind of evasion attacks. To launch this attack, we train a Generative Adversarial Network (GAN) on a real dataset to generate fake low consumption readings that can evade the detector. The given results indicate that the success rate of the attack is between 82% and 97%. To thwart this attack, we divide the consumers into clusters of close electricity consumption levels and train one detector for each cluster. Therefore, if a malicious consumer in any cluster tries to imitate the consumption profiles of consumers in other clusters, he/she will be detected. On the other hand, it is not profitable to imitate the electricity consumption profiles of consumers in his/her cluster to evade detection. To prove the effectiveness of our countermeasure, extensive experiments are conducted and the results indicate that our countermeasure can successfully thwart the attack.

Antiquantum Privacy Protection Scheme in Advanced Metering Infrastructure of Smart Grid Based on Consortium Blockchain and RLWE

Smart meters in advanced metering infrastructure (AMI) collect fine-grained, real-time power consumption data may result in user privacy leakage. Aiming at the problem that existing privacy protection schemes cannot resist quantum attacks, an antiquantum privacy protection scheme in AMI of smart grid based on consortium blockchain and ring learning with errors (RLWE) is proposed. The scheme has the following contributions. First, the RLWE-based secure communication scheme is designed in this article, which ensures the resistance of the scheme to quantum attacks. Second, all smart meters in AMI use anonymous identity to communicate with the control center, and the fine-grained power consumption data is also encrypted and digitally signed to prevent user privacy leakage and tampering of power consumption data. And the scheme does not use bilinear pairings and exponential operations and has efficient computational performance. Third, a two-tier consortium blockchain storage system consisted of regional gateways and data aggregators are designed to eliminate the single point of failure problems of the data aggregators. The practical byzantine fault tolerance consensus mechanism is improved to reduce the communication cost in the consensus process. The scheme's security is proven theoretically, and the experimental results show that the scheme has a low computational cost.

Cost-Benefit Analysis of Advanced Metering Infrastructure of Smartgrid: A Case Study of Dhaka City

The aim of this research is to present a methodological approach to obtain the cost-benefit impact at environmental and socioeconomic level, related to the integration of advanced metering infrastructure (AMI) into utilities and their residential customers. The methodology used to quantify the costs and benefits of implementing such technology in smargrid domain at Dhaka city. The socioeconomic benefits estimated in this research were related to job creation, energy consumption decrease, reduction of non-technical losses, and the reduction of CO2 emissions. An economic analysis using the net present value is presented here for assessing the impact caused by smart metering implementation. Also, sensitivity is analyzed for energy price, AMI cost and duration of the project. The result shows that AMI system implantation has advantages over current situation.

Dynamic Modeling and Optimal Defending Strategy for Smart-Grid Advanced Metering Infrastructure Subjected to Ddos Attacks

Dynamic Modeling and Optimal Defending Strategy for Smart-Grid Advanced Metering Infrastructure Subjected to Ddos Attacks

Real-time energy data compression strategy for reducing data traffic based on smart grid AMI networks

In the future, the Internet of things (IoT) may provide huge volumes of data. Smart grids are a class of IoT electricity distribution systems that can control bidirectional energy flows between consumers and service providers (Barman et al. “IOT Based Smart Energy Meter for Efficient Energy Utilization in Smart Grid”, 978-1-5386-4769-1 1831.00, 2018 IEEE). A typical smart grid features an advanced metering infrastructure (AMI), which automatically collects meter data from widely distributed sensors. A utility company that intends to use AMI must deploy smart meters, data concentrator units, and a meter data management system (MDMS). Concentrators collect ubiquitous messages from smart meters and transmit aggregated data to their MDMS. Although ubiquitous messages may enhance the efficiency of some electricity grids, any excessive volume of messages causes data congestion. This research considers the Reference Energy Disaggregation Data Set. Our proposed algorithm can compress meter data efficiently. Our contributions are as follows: First, thus far, numerous researchers have attempted to address smart grid problems with AMI systems, and here, we provide a relatively complete review of these attempts. Second, this paper presents a strategy to analyze this problem and propose a compression algorithm to compress meter data. This proposed algorithm combines several existing compression algorithms and operates from 2 to 10% more efficiently than previously published algorithms.

End-User Privacy Protection Scheme from cyber intrusion in smart grid advanced metering infrastructure

Smart monitoring work as a backbone of a smart grid distribution network. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. The massive deployment of smart meters in the distribution side raises a concern about data loss, privacy loss and False Data Intrusion Attack (FDIA). In order to protect the commercial and non-commercial users “End-User Privacy Protection Scheme (EPPS)” is proposed, which allows smart meters to report correct reading during FDIA/intrusion. In this proposed technique statistical machine learning method based on Gaussian Mixture Model Clustering (GMMC) and Mean Square Error (MSE) using two performance indices, the Data Protection Capability (DPC) and the confidential interval for true measurement against false data injection is evaluated. For performance evaluation of the proposed methodology, a passive distributed network is studied. Using EPPS, customer pattern is reconstructed by eliminating cyber intrusion due to FDIA on a smart metering system. This research validates the proposed method through MATLAB software using the smart meter data taken from the National Renewable Energy Laboratory (NREL).

Jamming-Resilient Backup Nodes Selection for RPL-based Routing in Smart Grid AMI Networks

Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time of data transmission. The IPv6 routing protocol for low-power and lossy networks (RPL), proposed by IETF ROLL working group, is considered to be the best routing solution for the AMI communication environment. However, the performance of RPL can be seriously degraded due to jamming attack. In this paper, we analyze the performance degradation problem of RPL protocol under jamming attack. We propose a backup node selection mechanism based on the standard RPL protocol. The proposed mechanism chooses a predefined number of backup nodes that maximize the probability of successful transmission. We evaluation the proposed mechanism through MATLAB simulations, results show the proposed mechanism improves the performance of RPL under jamming attack prominently.

Anomaly-based framework for detecting power overloading cyberattacks in smart grid AMI

The Advanced Metering Infrastructure (AMI) is one of the key components of the smart grid. It provides interactive services for managing billing and electricity consumption, but it also introduces new vectors for cyberattacks. Although, the devastating and severe impact of power overloading cyberattacks on smart grid AMI, few researches in the literature have addressed them. In the present paper, we propose a two-level anomaly detection framework based on regression decision trees. The introduced detection approach leverages the regularity and predictability of energy consumption to build reference consumption patterns for the whole neighborhood and each household within it. Using a reference consumption pattern enables detecting power overloading cyberattacks regardless of the attacker’s strategy as they cause a drastic change in the consumption pattern. The continuous two-level monitoring of energy consumption load allows efficient and early detection of cyberattacks. We carried out an extensive experiment on a real-world publicly available energy consumption dataset of 500 customers in Ireland. We extracted, from the raw data, the relevant attributes for training the energy consumption patterns. The evaluation shows that our approach achieves a high detection rate, a low false alarm rate, and superior performances compared to existing solutions.

An enhanced anonymous identity‐based key agreement protocol for smart grid advanced metering infrastructure

SummarySprouting populace mass within the urban areas furnishes critical challenges of providing uninterruptible community services to fulfill the primitive needs of inhabitants in smart cities. Smart cities facilitate and uplift the living standards of inhabitants through various smart systems or infrastructures, and smart grid is one of them. Secure transmission is a key requirement in the advanced metering infrastructure (AMI) of most smart grids, and key establishment cryptographic protocols can be used to achieve such a requirement. Designing efficient and secure key establishment protocols for AMI remains challenging. For example, in this paper, we reveal several weaknesses in the identity‐based key establishment protocol of Mohammadali et al (published in IEEE Trans Smart Grid, 2017), which is based on elliptic curves. We then improve their protocol and prove its security in the random oracle model. We also demonstrate that the improved protocol achieves both anonymity and untraceability, before presenting a comparative summary of the security and computational overheads of the proposed protocol and several other existing protocols.

Enhancing the security of a cloud‐based smart grid AMI network by leveraging on the features of quantum key distribution

AbstractIn the last decade, there have been many research proposals on the potentials of incorporating cloud computing technology to the electrical power grid as it evolves rapidly to the smart grid. The advanced metering infrastructure (AMI) is one domain of the smart grid that can be greatly impacted by cloud computing technology with regards to an improved capability for data storage and computations, which will be a serious burden to the AMI in an evolved smart grid environment. In our previous study, a cloud‐based OpenFlow firewall for the mitigation of distributed denial‐of‐service attacks in a smart grid AMI (SG AMI) was proposed. In that study, it was clearly demonstrated that cloud computing technology, in addition to improving the storage and computation capability of the AMI, can also be leveraged on to enhance the security of the SG AMI in an attack scenario. In this study, we propose a cloud‐based SG AMI system model utilizing quantum key distribution (QKD) cryptography for enhancing the security of a cloud‐based SG AMI. The proposed model has been designed to complement the security features of the previously designed OpenFlow firewall to guarantee not only the availability but also the confidentiality and integrity of data traversing the AMI network and beyond. The analysis on computation and communication costs shows that the protocols utilized in the proposed system model are lightweight protocols. In addition, results from the simulation of the QKD (BB84) protocol show that QKD can be leveraged on to provide information theoretic security for the cloud‐based SG AMI.

Cloud-Centric Collaborative Security Service Placement for Advanced Metering Infrastructures

The extensive integration of smart meters to utility communication networks introduces numerous cyber security concerns. Such meters are the primary access points to a smart grid advanced metering infrastructure (AMI). They are physically unprotected devices that are geographically distributed in low-trust environments. Nonetheless, they are expected to transceive vital information regarding consumption, billing, and load management. The security of such information is an important requirement for operational continuity of a power system. Costs, system complexity, and response time are major considerations in designing security solutions for such cases. It is anticipated that future grids will be powered by the advancement of cloud computing. The security-as-a-service is a model that exploits the potential of cloud computing to provide cyber security solutions. Its key offerings include cost reduction, simplicity, and faster response. This paper proposes a cloud-centric collaborative security service architecture for the monitoring of upstream AMI traffic. It also includes a collaboration-aware service placement scheme for the proposed architecture. The placement scheme develops a quadratic assignment problem that minimizes latency. Case studies demonstrate the enhanced performance of the proposed scheme under various scenarios.

Demonstrability of Narrowband Internet of Things technology in advanced metering infrastructure

The deployment cost of advanced metering infrastructure (AMI), one of the key elements for smart grid, often arises from three sub-systems, respectively referred to as data management system, communication network, and smart devices. Among them, the costs of communication network account for a considerable proportion. In smart grid, smart devices are generally fixed in a certain position and schedule reading of interval meter data. They do not only cover smart meters, but also extend gas, water meters, and other sensors measuring light, humidity, and temperature. Many of them are often with battery supply, and this will bring utilization constraints especially in communication network. The deployment needs of reducing cost and the energy-constrained network for an extremely large-scale AMI infrastructure both require optimum communication technologies that consider energy consumption, minimize energy use, simplify network topology, and prolong network lifetime. As an emerging 3GPP radio interface technology specifically designed for low power wide area networks (LPWANs), Narrowband Internet of Things (NB-IoT) inherits from the existing LTE but does many simplifications. Its most prominent features, enhanced coverage and low power consumption, are being pursued for a large-scale AMI communication network. In this paper, we provide a survey of AMI communication, emphasize on the key technologies of NB-IoT, analyze the performance of NB-IoT network over a real-world, and demonstrate the potential of NB-IoT to support and boost AMI in smart grid.

Smart Grid Advanced Metering Infrastructure: Overview of Cloud-Based Cyber Security Solutions

The vision of making the traditional power grid smart has necessitated the incorporation of so many intelligent and smart technologies driven by ICT into virtually all domains of the smart grid. Expectedly, this has opened a lot of routes for vulnerabilities which can be maliciously exploited by today’s cyber criminals. The advanced metering infrastructure (AMI) is believed to be the foundation of the smart grid. Malicious exploitations of security vulnerabilities present in the smart grid AMI by cyber criminals would be detrimental to both energy providers and final consumers. This is in view of the huge task involved in the management and storage of data from the numerous intelligent electronic devices (IEDs) that traverse the smart grid’s AMI and beyond. A lack of capability in terms of processing, computations and storage of these data can only compound the cyber security problems facing the SG AMI. Cloud Computing is therefore a potential technology that can address the above challenging issues. Previous review works had concentrated mainly on the potentials of cloud computing for the smart grid. None of such works to the best of our knowledge had an extensive focus on the security of the SG AMI. We therefore provide in this paper firstly, a review of the cloud based solutions for the smart grid with appropriate SG domain classification in order to determine the focus points of previous related works. We then provide an elaborate overview on cloud based security solutions for the SG AMI. At the end, future research challenges were highlighted with open research ideas that could help to address the challenges also suggested.

Congestion Minimizing Scheme for Enhanced Data Aggregation in ZigBee-Based SG AMI Network

It is envisaged that the multi-directional communication of end user’s energy consumption data in a smart grid advanced metering infrastructure (AMI) network needed for billing and other grid controlled purposes will raise concerns about the potential violations of the privacy of end users’ personal data. Preserving the privacy of consumers can be achieved by designing robust and resilient AMI communication architectures with state-of-the-art cryptographic algorithms and data aggregation protocols. However, research has shown that these aggregation protocols can result in a congestive network scenario in the absence of a proper congestion management scheme. Unfortunately, there is no efficient mechanism for congestion management in the current ZigBee standard. In this paper, a well-designed Ring Triangulation Communication Architecture (RTCA) is utilized in our congestion minimizing scheme for enhancing data aggregation in a ZigBee based SG AMI network. The RTCA is implemented with a well-constructed ZigBee data and its aggregation algorithms. The average throughput and latency results from our scheme indicates a significant reduction in transmission congestion as well as traffic overheads incurred during message aggregation relative to the congestion control schemes (ZCCF and ZTCC) proposed in the literature.

A realistic performance evaluation of privacy-preserving protocols for smart grid AMI networks

The proliferation of ubiquitous communication with the Internet of Things has led to advancement in wireless communication technologies. Today, they have become an indispensable component of smart city applications thanks to the lower cost and easiness of the installation and the maintainability. For example, they are a promising alternative of the wired solutions used in Smart Grid Advanced Metering Infrastructure (AMI) networks. However, wireless communication networks are more vulnerable to cyber-attacks and easier to be eavesdropped, so researchers have proposed a number of secure protocols. In addition to being vulnerable to cyber-attacks, AMI also exposes consumer power data which poses privacy issues. While there has been a lot of research to address these issues, the validation efforts mostly utilized simulators and actual overhead due to these approaches have not been captured in a realistic setup. Therefore, in this paper, we chose two open-source wireless mesh networking standards, IEEE 802.11s and ZigBee, and built an AMI testbed at FIU Engineering Center that will be able to collect power readings from smart meters. Then, we used the testbed to assess and compare the performance of the two standards under fully homomorphic encryption and secure multiparty computation-based privacy-preserving protocols that can provide computation on the encrypted data. Extensive experiments under a variety of conditions indicate that IEEE 802.11s-based wireless mesh networks are more suitable for the AMI networks when used with a reliable transport layer protocol at the expense of data collection completion time while ZigBee can be a viable option for protocols that generate relatively far smaller data packets.

An improved routing algorithm for advanced metering infrastructure in smart grid

The advanced metering infrastructure (AMI) is a key component of information transmission networks in smart grid because it connects distribution stations to grid users. However, the ever-increasing amount of information transmitted through smart meters inevitably results in heavy path loads and route congestion in AMI, which can lead to transmission delay and data packet loss. Therefore, in this paper, we proposed a routing algorithm based on load balancing that enabled data packets to select the optimal route between smart meter and data aggregator unit. We defined the routing optimization objective function for estimating the path load by using load factor,and employed an ant colony algorithm combined with simulated annealing (ACA-SA) to solve the optimization problem. Furthermore, with considering the quality of service (QoS) requirements of latency and reliability for AMI applications, two routing methods were proposed: the method with variable initial pheromone for delay-sensitive applications, whereas the method with variable visibility for applications with low packet-loss rate requirements. Analytical and simulation results demonstrated that the proposed algorithm can offer real-time and reliable communication for AMI in smart grid.

Data Aggregation in Smart Grid AMI Network for Secure Transfer of Energy User-Consumption Data

In a secured smart grid AMI environment, congestion management during data aggregation with security encryption for privacy preservation is a challenging issue. By introducing data communication network schemes into the Advanced Metering Infrastructure (AMI), network traffic congestion and service rates can be improved while preserving user’s privacy from the grid operator’s end. In this paper, a resilient architecture called Ring Triangulation Communication Architecture (RTCA) for data aggregation and user privacy protection is proposed. To preserve privacy as well as reducing traffic congestion in the architecture, DMF homomorphic encryption algorithms were formulated for local concentrators while using a global concentrator to check for anomalies in the AMI server clusters. With TCP/IP protocol and IEEE 802.11 MAC/PHY on the network, TCP message flooding was contextualized for congestion scenario. Stochastic TCP congestion management schemes with wired equivalent privacy (WEP) and the Data Minimizing Function (DMF) scheme were compared. Our proposed architecture significantly reduced transmission congestion and cryptographic overheads incurred during message aggregation. The results of the performance of the DMF Homomorphic encryption scheme incorporated into our proposed architecture for the SG AMI were discussed. These include service rate and other QoS metrics which are negatively affected by a congestive network condition.Keywords: Advanced Metering Infrastructure (AMI), Data Minimizing Function (DMF), Ring Triangulation Communication Architecture (RTCA), Data Aggregation, Smart Grid (SG), Smart Meter (SM).

Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

In an advanced metering infrastructure (AMI), data acquisition points (DAPs) are responsible for collecting traffic from several smart meters and automated devices and transmitting them to the utility control center. Although the problem of optimized data collector placement has already been addressed for wireless broadband and sensor networks, DAP placement is quite a new research area for AMIs. In this paper, we investigate the minimum required number of DAPs and their optimized locations on top of the existing utility poles in a distribution grid such that smart grid quality of service requirements can best be provided. In order to solve the problem for large-scale AMIs, we devise a novel heuristic algorithm using a greedy approach for identifying potential pole locations for DAP placement and the Dijkstra shortest path algorithm for constructing reliable routes. We employ the characteristics of medium access schemes from the IEEE 802.15.4g smart utility network (SUN) standard, and consider mission-critical and non-critical smart grid traffic. The performance and time-complexity of our algorithm are compared with those obtained by the IBM CPLEX software for small scenarios. Finally, we apply our devised DAP placement algorithm to examples of realistic smart grid AMI topologies.