Smart Grid Advanced Metering 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.

GITM: A GINI Index-Based Trust Mechanism to Mitigate and Isolate Sybil Attack in RPL-Enabled Smart Grid Advanced Metering Infrastructures

GITM: A GINI Index-Based Trust Mechanism to Mitigate and Isolate Sybil Attack in RPL-Enabled Smart Grid Advanced Metering Infrastructures

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

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).

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.

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.

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.

Genetic algorithm-based redundancy optimization method for smart grid communication network

This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure (AMI) to realize economy and reliability targets. AMI is a crucial part of the smart grid to measure, collect, and analyze data about energy usage and power quality from customer premises. From the communication perspective, the AMI consists of smart meters, Home Area Network (HAN) gateways and data concentrators; in particular, the redundancy optimization problem focus on deciding which data concentrator needs redundancy. In order to solve the problem, we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures. Then, we establish a complete redundancy optimization model, which comprehensively consider the factors of reliability and economy. Finally, an advanced redundancy deployment method based on genetic algorithm (GA) is developed to solve the proposed problem. The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.

Impact of Distributed Denial-of-Service Attack on Advanced Metering Infrastructure

The age of Internet of Things has brought in new challenges specifically in areas such as security. The evolution of classic power grids to smart grids is a prime example of how everything is now being connected to the Internet. With the power grid becoming smart, the information and communication systems supporting it is subject to both classical and emerging cyber-attacks. The article investigates the vulnerabilities caused by a distributed denial-of-service (DDoS) attack on the smart grid advanced metering infrastructure. Attack simulations have been conducted on a realistic electrical grid topology. The simulated network consisted of smart meters, power plant and utility server. Finally, the impact of large scale DDoS attacks on the distribution system’s reliability is discussed.

On preserving user privacy in Smart Grid advanced metering infrastructure applications

Advanced metering infrastructure AMI enables real-time collection of power consumption data through the Smart Grid communication network. With the current deployment of smart meters SMs, one of the concerns that started to be raised by the customers is on the privacy of their power consumption data. The exposure of these data can lead to several privacy problems that need to be addressed before the customers can be convinced for the use of SMs. This paper has two contributions. First, it identifies the threats regarding user and data privacy in AMI applications and comprehensively surveys the existing solutions to address these threats. We categorize the existing approaches on privacy and discuss pros and cons of these approaches with respect to some criteria. Second, we pick one of the existing solutions on privacy, namely the homomorphic encryption, and evaluate its feasibility and impact on performance when used in data aggregation for real-time AMI applications. We investigate and compare the performance of homomorphic encryption in terms of data size and end-to-end delay with that of hop-by-hop secure data aggregation and data concatenation within a network of SMs via extensive simulations. We finally conclude the paper with some future privacy issues that are subject to further research. Copyright © 2013 John Wiley & Sons, Ltd.

Traffic Scheduling Technique for Smart Grid Advanced Metering Applications

In this paper, we present a multi-gate mesh network architecture that has been developed to ensure high performance and reliability under emergency conditions when a system expects to receive power outage notifications and exchanges. In order to handle the metering traffic, under time varying outage conditions we introduce a multi-gate and single-class back-pressure based scheduling algorithm, which takes into account both the hop-count, as well as the queue length of each mesh node. An important requirement of this algorithm is that all the meter nodes should always maintain a separate path to each gateway. We first quantify the stability region of the network when our novel algorithm is applied to schedule the packets. We then present a numeric analysis to prove that the overall network delay is reduced as a result of employing the proposed scheduling algorithm. Moreover, we also theoretically prove that the network is always able to remain stable as long as the arrival rate vector lies inside the stability region provided by our scheduling algorithm. Finally, we derive a distributed objective function that is adopted by the practical implementation of the packet-scheduling scheme. The simulation results indicate that under the context of the multi-gate network, our packet-scheduling scheme can indeed significantly improve the network's reliability and delay performance, which are important factors under outage conditions.

Evaluating the effectiveness of the security quality requirements engineering (SQUARE) method: a case study using smart grid advanced metering infrastructure

This paper presents an evaluation of the security quality requirements engineering (SQUARE) method. The evaluation of SQUARE was conducted by its application on the advanced metering infrastructure of smart grid as a case study. We evaluated the effectiveness of SQUARE with respect to its ability to elicit a set of artifacts, threats, and vulnerabilities; to perform likelihood, impact analysis, and risk level determination; and to elicit, categorize, and prioritize the security requirements. The main contribution of this work is the evaluation of the effectiveness of SQUARE using qualitative security requirements engineering method evaluation criteria.