Connections: An Introduction to the Economics of Networks by Sanjeev Goyal .

The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer’s instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.

A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks

Development in the field of electronics and communication set up new paradigm. The need of making the grid smart is increasing due to environmental constrain and effective use of the available electricity. The use of digital information and controls technology to improve reliability, security, and efficiency of the electric grid.

Smart grid

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid also known as, smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication (SGC) system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SGC system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. By separating the control plane from the data plane, SDN helps the network operators to manage the network flexibly. Since SG heavily relies on communication networks, therefore, SDN has also paved its way into the SG. By applying SDN in SG systems, efficiency and resiliency can potentially be improved. SDN, with its programmability, protocol independence, and granularity features, can help the SG to integrate different SG standards and protocols, to cope with diverse communication systems, and to help SG to perform traffic flow orchestration and to meet specific SG quality of service requirements. This paper serves as a comprehensive survey on SDN-based SGC. In this paper, we first discuss taxonomy of advantages of SDN-based SGC. We then discuss SDN-based SGC architectures, along with case studies. This paper provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.

Software Defined Networks-Based Smart Grid Communication: A Comprehensive Survey

Generative Adversarial Networks (GANs) have been successfully used in a large number of domains. This paper proposes the use of GANs for generating network traffic in order to mimic other types of traffic. In particular, our method modifies the network behavior of a real malware in order to mimic the traffic of a legitimate application, and therefore avoid detection. By modifying the source code of a malware to receive parameters from a GAN, it was possible to adapt the behavior of its Command and Control (C2) channel to mimic the behavior of Facebook chat network traffic. In this way, it was possible to avoid the detection of new-generation Intrusion Prevention Systems that use machine learning and behavioral characteristics. A real-life scenario was successfully implemented using the Stratosphere behavioral IPS in a router, while the malware and the GAN were deployed in the local network of our laboratory, and the C2 server was deployed in the cloud. Results show that a GAN can successfully modify the traffic of a malware to make it undetectable. The modified malware also tested if it was being blocked and used this information as a feedback to the GAN. This work envisions the possibility of self-adapting malware and self-adapting IPS.

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Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection

The addition of synchrophasors such as phasor measurement units (PMUs) to the existing power grid will enhance real-time monitoring and analysis of the grid. The PMU collects bus voltage, line current, and frequency measurements and uses the communication network to send the measurements to the respective substation(s)/control center(s). Since this approach relies on network infrastructure, possible cyber security vulnerabilities have to be addressed to ensure that is stable, secure, and reliable. In this paper, security vulnerabilities associated with a synchrophasor network in a benchmark IEEE 68 bus (New England/New York) power system model are examined. Currently known feasible attacks are demonstrated. Recommended testing and verification methods are also presented.

Cyber security evaluation of synchrophasors in a power system

Malware is constantly evolving. Although existing countermeasures have success in malware detection, corresponding counter-countermeasures are always emerging. In this study, a counter-countermeasure that avoids network-based detection approaches by camouflaging malicious traffic as an innocuous protocol is presented. The approach includes two steps: Traffic format transformation and side-channel massage (SCM). Formattransforming encryption (FTE) translates protocol syntax to mimic another innocuous protocol while SCM obscures traffic side-channels. The proposed approach is illustrated by transforming Zeus botnet (Zbot) Command and Control (C&C) traffic into smart grid Phasor Measurement Unit (PMU) data. The experimental results show that the transformed traffic is identified by Wireshark as synchrophasor protocol, and the transformed protocol fools current side-channel attacks. Moreover, it is shown that a real smart grid Phasor Data Concentrator (PDC) accepts the false PMU data.

Stealthy malware traffic - Not as innocent as it looks

The purpose of this study is to summarize the status of current smart meter deployment initiatives in the United States. This paper presents the findings from a survey that was sent to US utility companies listed on the Department of Energy's smart grid website under the Recovery Act Smart Grid program. The survey provides information such as: the duration of the project, regions covered, budget, number of meters deployed (for residential, commercial, and industrial use), benefits (financial- and performance-based for customer and power companies), and obstacles. Emphasis is given to the successes and setbacks the companies faced during implementation. This study is aimed at bringing awareness to the electricity community of the overall progress across the U.S. towards development and deployment of Advanced Metering Infrastructure (AMI) systems and their impacts.

Smart meter deployment: US initiatives

This paper presents a review of artificial-intelligence methodologies for contingency analysis for power system security. In the planning process, offline artificial intelligence methods can assist the operator with anticipatory information useful to preserve the steady state security of the system. In online operation stages, artificial intelligence techniques have been proposed to speed up and improve the accuracy of voltage-stability analysis and for contingency selection, without requiring a traditional mathematical model of the power grid. Furthermore, when the power grid is stressed, artificial intelligence approaches to dynamic contingency analysis are capable of empowering the operator to control the power grid in a more reliable and rapid fashion.

On Artificial Intelligence Approaches for Contingency Analysis in Power System Security Assessment

The development and integration of technologies such as the Internet of Things (IoT) or edge computing devices is contributing to the formation of an increasingly digital, intelligent and connected...

G. Kumar Venayagamoorthy

Papers

Cyber security evaluation of synchrophasors in a power system

Stealthy malware traffic - Not as innocent as it looks

Smart meter deployment: US initiatives

On Artificial Intelligence Approaches for Contingency Analysis in Power System Security Assessment

Edge Computing and Adaptive Fault-Tolerant Tracking Control Algorithm for Smart Buildings: A Case Study