Smart Grid Security: Threats, Challenges, and Solutions

The cyber-physical nature of the smart grid has rendered it vulnerable to a multitude of attacks that can occur at its communication, networking, and physical entry points. Such cyber-physical attacks can have detrimental effects on the operation of the grid as exemplified by the recent attack which caused a blackout of the Ukranian power grid. Thus, to properly secure the smart grid, it is of utmost importance to: a) understand its underlying vulnerabilities and associated threats, b) quantify their effects, and c) devise appropriate security solutions. In this paper, the key threats targeting the smart grid are first exposed while assessing their effects on the operation and stability of the grid. Then, the challenges involved in understanding these attacks and devising defense strategies against them are identified. Potential solution approaches that can help mitigate these threats are then discussed. Last, a number of mathematical tools that can help in analyzing and implementing security solutions are introduced. As such, this paper will provide the first comprehensive overview on smart grid security

Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid

The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions

A Downsizing Strategy for Combinatorial PMSG Based Wind Turbine and Micro-SMES System Applied in Standalone DC Microgrid

This paper presents a combinatorial standalone permanent magnet synchronous generator (PMSG) based variable speed wind turbine (VSWT) and small-size superconducting magnetic energy storage (SMES) system into the DC microgrid system. The principal purpose of SMES system is to preserve power balance by absorbing power during peak wind generation and to release it during low power generation. This work accomplished by describing the optimized design of the SMES solenoid coil, ensuring the desired energy storage capacity based on the simulated annealing (SA) algorithm. More importantly, the new control technique is developed for bi-directional DC-DC converter to level output power of the wind turbine depending on the demand thereby reducing the capacity of the DC-DC converter system. Detailed simulation studies implemented in PSCAD/EMTDC corroborate the superior robustness and balancing performance of the proposed micro-SMES controller with an optimal coil size under various situations including variable wind speed. This combination will result in “scaling-factors” knowledge through downsizing strategy which will lead to the most efficient system from cost cutting, energy savings, and downsizing viewpoints

LTE and Wi-Fi Coexistence in Unlicensed Spectrum with Application to Smart Grid: A Review

Long Term Evolution (LTE) is expanding its utilization in unlicensed band by deploying LTE Unlicensed (LTEU) and Licensed Assisted Access LTE (LTE-LAA) technology. Smart Grid can take the advantages of unlicensed bands for achieving two-way communication between smart meters and utility data centers by using LTE-U/LTE-LAA. However, both schemes must co-exist with the incumbent Wi-Fi system. In this paper, several co-existence schemes of Wi-Fi and LTE technology is comprehensively reviewed. The challenges of deploying LTE and Wi-Fi in the same band are clearly addressed based on the papers reviewed. Solution procedures and techniques to resolve the challenging issues are discussed in a short manner. The performance of various network architectures such as listenbefore- talk (LBT) based LTE, carrier sense multiple access with collision avoidance (CSMA/CA) based Wi-Fi is briefly compared. Finally, an attempt is made to implement these proposed LTEWi- Fi models in smart grid technology.Comment: submitted in 2018 IEEE PES T&

Optimal Reliability-based Placement of Plug-In Electric Vehicles in Smart Distribution Network

Distributed renewable resources are one of the major technological inclinations that play a pivotal role in the future green power system. In this context, high penetration of Plug-In Electric Vehicles (PIEVs) may bring many challenges for the smart grid. PIEVs’ unscheduled utilization will cause technical issues for both generation side and demand side. High penetration of PIEVs may lead to reliability deterioration of smart distribution network. In this paper, firstly a PIEV owner behavior model is introduced. The output of this model is the hourly demand of PIEVs per day. Secondly, a comprehensive reliability-based objective function is proposed to allocate these modeled PIEVs in smart distribution network. Finally, IEEE RBTS bus 2 is used for implementing allocation algorithm utilizing both genetic algorithm and particle swarm optimizatio

A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi

In advanced metering infrastructure (AMI) of smart grid, WiFi is an appropriate choice for its bidirectional communication requirement to transmit data to the billing center. But, WiFi functions in the free spectrum bands and LTE also requires to use the same free bands for its network expansion being licensed spectrum is limited and expansive. LTE and WiFi can operate simultaneously in the 3.5 GHz band (also known as citizen broadband radio service (CBRS)), which has large amount of free and clean spectrum. In this paper, we propose a smart grid metering infrastructure based on fixed duty cycled LTE and WiFi, where smart meters and its’ data collectors (known as Access Point) use WiFi and LTE, respectively, for transferring data. Under a system level simulation environment, we investigated the LTE-WiFi coexistence performance in CBRS band considering a time division duplexing (TDD)-LTE associated with FTP traffic, and IEEE 802.11n (WiFi). The simulation performance demonstrates a good neighborhood coexistence between WiFi and LTE, which makes it a potential communication solution for the AMI