The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

As one of the key components of the smart grid, advanced metering infrastructure brings many potential advantages such as load management and demand response. However, computerizing the metering system also introduces numerous new vectors for energy theft. In this paper, we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers’ normal and malicious consumption patterns. Using distribution transformer meters, areas with a high probability of energy theft are short listed, and by monitoring abnormalities in consumption patterns, suspicious customers are identified. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors, make the algorithm robust against nonmalicious changes in usage pattern, and provide a high and adjustable performance with a low-sampling rate. Therefore, the proposed method does not invade customers’ privacy. Extensive experiments on a real dataset of 5000 customers show a high performance for the proposed method.

Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

A blockchain future for internet of things security: a position paper

A smart grid (SG) consists of many subsystems and networks, all working together as a system of systems, many of which are vulnerable and can be attacked remotely. Therefore, security has been identified as one of the most challenging topics in SG development, and designing a mutual authentication scheme and a key management protocol is the first important step. This paper proposes an efficient scheme that mutually authenticates a smart meter of a home area network and an authentication server in SG by utilizing an initial password, by decreasing the number of steps in the secure remote password protocol from five to three and the number of exchanged packets from four to three. Furthermore, we propose an efficient key management protocol based on our enhanced identity-based cryptography for secure SG communications using the public key infrastructure. Our proposed mechanisms are capable of preventing various attacks while reducing the management overhead. The improved efficiency for key management is realized by periodically refreshing all public/private key pairs as well as any multicast keys in all the nodes using only one newly generated function broadcasted by the key generator entity. Security and performance analyses are presented to demonstrate these desirable attributes.

Efficient Authentication and Key Management Mechanisms for Smart Grid Communications

Achievement of the goals of smart grid such as resilience, high power quality, and consumer participation strongly depends on the security of this system. Along with the security measures that should be built into the smart grid from the beginning, appropriate Intrusion Detection Systems (IDSs) should also be designed. Home area network (HA#) is one of the most vulnerable subsystems within the smart grid, mostly because of its physically insecure environment. In this paper, we present a layered specification-based IDS for HA#. Considering that ZigBee is the dominant technology in future HA#, our IDS is designed to target ZigBee technology; specifically we address the physical and medium access control (MAC) layers. In our IDS the normal behavior of the network is defined through selected specifications that we extract from the IEEE 802.15.4 standard. Deviations from the defined normal behavior can be a sign of some malicious activities. We further investigate the physical and MAC layer attacks in ZigBee networks and evaluate the performance of our proposed IDS against them. Our IDS provides a good detection capability against known attacks, and since this is an IDS based on anomalous event detection, we expect the same for unknown attacks.

Specification-based Intrusion Detection for home area networks in smart grids

A key feature of the smart grid is the introduction of two-way data communications into the power grid. This brings many security challenges, because of the large-scale, difficult-to-secure environment, complexity of smart grid systems, and resource limitations of the smart grid deployments. In this paper, we focus on security and privacy concerns in the context of the smart grid. Existing security mechanisms developed for traditional information technology systems can be used as a basis for designing security measures for the smart grid. However, new methods that meet the special requirements and characteristics of the smart grid are also required. In spite of the obstacles against developing detailed security solutions for the future smart grid, such as uncertainty of the architecture and lack of practical experiences with security attacks, some research has been performed in this area over the last few years. We survey the existing literature on different security aspects of the smart grid and provide directions for further research. Copyright © 2012 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.559

A survey on security issues in smart grids

One of the most important and challenging area in the smart grid context is security and privacy section. Smart grid is a vulnerable system and can be attacked even from aboard, attacks that may cause different level of issues and harms on the devices and society. So, research community has paid attention to this topic and the reasons of required security and privacy for the smart grid. The first step of designing and implementing security for any system such as a smart grid is an authentication scheme followed by a key management protocol. Other security aspects like integrity, authorization and confidentiality can be implemented as long as a strong key management protocol has already been designed and addressed.

Paria Jokar

Papers

Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

Efficient Authentication and Key Management Mechanisms for Smart Grid Communications

Specification-based Intrusion Detection for home area networks in smart grids

A survey on security issues in smart grids

Smart grid authentication and key management for unicast and multicast communications