Smart Grid Communication Network Research Articles

Survivability-aware routing restoration mechanism for smart grid communication network in large-scale failures

Natural disasters such as earthquakes have consecutive impacts on the smart grid because of aftershock activities. To guarantee service requirements and smart grid stable operations, it is a challenge to design a fast and survivable rerouting mechanism. There are few studies that consider concurrent rerouting aiming at multiple services in smart grid communication network, however. Firstly, we formulate the node survivability, link survivability, and path survivability model in terms of the distance from the epicenter to the node and the link of the network. Meanwhile, we introduce the indicator of site difference level which is unique in the smart grid to further restrict the service path. Secondly, to improve the algorithm efficiency and reduce rerouting time, the deep first search algorithm is utilized to obtain the available rerouting set, and then the I-DQN based on the framework of reinforcement learning is proposed to achieve concurrent rerouting for multiple services. The experimental results show that our approach has a better convergence performance and higher survivability as well as the approximate latency in comparison with other approaches.

An Energy Efficient Network Architecture and Spectrum Sharing Technique for Cognitive Radio Based Smart Grid Communications

With the introduction of an automated electric power system known as smart grid, issues relating to power quality, shortages and inefficiency have been solved. Two vital foundations necessary for the sustainability and the stability of Smart Grids (SG) are a resourceful and reliable communication between users in a smart grid network and an energy efficient Smart Grid Communication Network (SGCN) architecture. On the other hand, Cognitive radios (CR) are equipped with the capability of exploiting unused licensed spectrum resources for effective spectrum utilization. The objective of this paper is to integrate cognitive radio into smart grid communication network in order to make the network a lot smarter and provide spectrum sharing solutions in SGCN. In this paper, an energy efficient SGCN architecture that is layered into the Home Area Network (HAN), Neighbourhood Area Network (HAN) and the Wide Area Network (WAN) has been proposed. Furthermore, in order to achieve a fair spectrum sharing amongst users in the network, CR users in the HANs estimate the need of its neighbouring users to transmit data. Then it uses this information to predict the vacant channels that will be utilized by any node in the network using a Markov chain process. In the NANs, a model is also proposed where every user sensor node access the spectrum based on the buffer occupancy estimates of its neighboring nodes. Simulation results show that there is a higher throughput in the network and also an increase in the quantity of data packets successfully transmitted using the proposed model.

PASS: A Privacy-Aware Approach for Secure Smart Metering in Advanced Metering Infrastructure Networks

Huge amounts of sensitive data are generated and transmitted on a smart grid infrastructure. These data are used for acquisition of various parameters of interest from smart meters in the grid and control of the grid. However, wireless communication processes across the smart grid network are vulnerable to different attacks like any other wireless communication infrastructure, which has unavoidably given rise to concerns about how to efficiently secure services and preserve privacy in smart grid network especially in metering. In this article, an efficient approach and framework was introduced for secure metering and preservation of consumers’ privacy in different neighborhood area networks of different service providers of the smart grid communication network. Our approach effectively introduced a communication framework for efficient and secure metering, privacy preservation, load balancing, and billing using perturbation and data aggregation techniques. Computational and security analysis were performed to ascertain its efficiency in terms of computational cost, energy consumption, and security. The results showed that the approach not only prevented gleaning of the metering but did so at a relatively low computational overhead compared with other approaches.

Water Cycle Algorithm Tuned Fuzzy Expert System for Trusted Routing in Smart Grid Communication Network

Finding an optimal route for reliable data delivery in the smart grid communication network (SGCN) is a challenging task due to its dynamic nature. Even though the rule set (RS) and membership function (MF) framed intuitively in our previous fuzzy logic (FL) approach performs the trusted routing satisfactorily, it consumes computational memory and reduces the energy efficiency of the node. To address this issue, in this article, we proposed a novel trust evaluation framework that employs water cycle algorithm (WCA) for automatic tuning of the rule set and membership function for the decision variable to route the packet in an adaptable manner. Variables like distance, link stability, and node honesty are considered for evaluation using WCA in three iterative processes, namely, exploitation, evaporation, and raining to find the near optimal if–then rules and points for the membership functions. An experimental setup is created using Network Simulator-2 (NS2) to evaluate the performance of the proposed trusted routing algorithm in SGCN. Extensive experiments are conducted for three cases, namely, 1) evaluating RS with fixed MF; 2) evaluating MF with fixed RS; and 3) combined evaluation of MF and RS to evaluate the performance of the proposed model. From the simulation results, it is clear that the RS and MF generated by the proposed model is small and compact enough to provide reliable routing in SGCN.

Lightweight Security and Privacy Safeguard strategies for Smart Grid management in Client-side Networks

In order to improve the reliability and efficiency of the power grid, the smart grid uses different communication technologies. Smart grid allows bidirectional flow of electricity and information, about the state of the network and the preconditions of the clients, between the different parts of the network. Therefore, it reduces energy losses and generates and distributes electricity efficiently. Although smart grid improves the quality of network services, due to the nature of the power grid communication networks are exposed to cybersecurity threats along with the other threats. For example, electricity consumption messages sent by consumers to the utility through the wireless network can be captured, modified or reproduced by adversaries. As a consequence, the important challenges in smart grid seems to be security and privacy concerns. The smart grid update creates three main communication architectures: the first is communication between the utility companies and customers through diverse networks; that is, Local Area Networks (HAN), Construction Area Networks (BAN) and Neighboring Area Networks (NAN), we refer to these networks as client-side networks in our thesis. The second architecture is the communication through the vehicle-to-network (V2G) connection between the Electric Vehicles and the network to charge or discharge their batteries. The hindmost network is connection of the network with measurement units that extend throughout the network in order to monitor the status and send reports periodically to the main CC to estimate the status and detect erroneous data. The proposed schemes are promising solutions for the security and privacy problems of the three main communication networks in smart grid. The novelty of these proposed schemes is not only because they are robust and efficient security solutions, but also due to their lightweight communication and computing overhead, which qualifies them to be applicable in devices with limited capacity in the network. Therefore, this work is considered an important progress towards a more reliable and authentic intelligent network.

An Energy-efficient Cognitive Radio Based Smart Grid Communication Network Architecture

With the introduction of an automated electric power system known as smart grid, issues relating to power quality, shortages and inefficiency has been solved, One vital foundation necessary for the sustainability and stability of smart grid (SG) is a secured, reliable, stable and efficient communication network infrastructure. The aim of this paper is to examine smart grid communication networks (SGCN) and propose a SGCN architecture that is energy efficient with the integration of cognitive radio technology. The SGCN architecture is layered into the Home Area Network (HAN), Neighbourhood Area Network (HAN) and the Wide Area Network (WAN) and each layer is analyzed. The paper also examine the motivation behind the employment of CR in SGCN and the challenges involved in designing an effective and reliable SGCN Infrastructure.

A Cross-Layer Trust Evaluation Protocol for Secured Routing in Communication Network of Smart Grid

Cybersecurity is one of the major issues to be considered while designing a communication network for Smart Grid (SGCN). However, due to the openness and unpredictable nature of the wireless network, it is often prone to different adversarial attacks that exploit vulnerabilities to launch cross-layer attacks during data transmission. To address this issue, we proposed a novel trust-based routing framework with Bayesian Inference to calculate direct trust and Dempster-Shafer (D-S) theory is used to compute the indirect trust by combining the evidence collected from reliable neighbours. The analytical hierarchy process (AHP) calculates the node’s creditability trust by taking cross-layer metrics like transmission rate, buffering capacity and received signal strength. Further, the fuzzy theory is combined with BDS-AHP to incorporate fairness in utilizing cross-layer metrics for calculating the link trust to enable reliable routing. Extensive experiments are carried to evaluate the performance of the proposed fuzzy-based trusted routing algorithm (FBDS-AHP) with the presence of malicious nodes launching packet-dropping, bad-mouthing, on-off and Sybil attacks. Simulation result proves that the proposed trust evaluation algorithm achieves promising results in defending against these attacks thereby providing improved reliability and ensures secured routing in SGCN.

Auditing on Smart-Grid With Dynamic Traffic Flows: An Algorithmic Approach

The introduction of Smart Grid systems has raised many new security concerns. If an attacker can compromise components of the Smart Grid communication network, they can fabricate malicious messages to interfere with the grid and ultimately cause outages. One method to address this concern is to conduct network audits by logging network traffic into dedicated servers in order to detect malicious messages. This may be done by using switches’ or routers’ ability to duplicate packets they receive with minimal overhead. The question of how many and which switches/routers to select for this task naturally follows. This paper considers the problem of finding minimal set of routers/switches in a Smart Grid communication network to use for auditing traffic. Accordingly, we devise three algorithms: the first one is highly effective with an approximation ratio of $(2+\theta)(\ln |V| + 1)$ . The second method is a highly scalable algorithm with a constant performance ratio. And the last one is a dynamic algorithm which can efficiently update its solution in response to changes to critical traffic. We experimentally evaluate our solutions and compare them to an optimal Integer Programming formulation, finding that they perform near-optimally and significantly outperform a simple heuristic in all cases.

Smart Grid Network With D2D Communication and Coherent PLC: Error Analysis

In this paper, we consider a hybrid smart grid communication network consisting of power line communication (PLC) at the transmitter side and the device-to-device (D2D) cellular communication on the receiver side which provides last mile connectivity. The PLC channel is assumed to experience Rayleigh fading and is corrupted by impulsive noise, and the D2D communication being a short range communication system, is assumed to experience Nakagami- $m$ fading with additive Gaussian noise. We propose a maximal-ratio combining (MRC) receiver for the considered hybrid smart grid communication system employing binary phase shift keying (BPSK) modulation at the transmitter and derive a closed-form symbol error probability (SEP) expression for the proposed receiver. The interference between the D2D links is assumed to be negligible for the tractability of the error probability analysis. Numerical results demonstrate the optimality of operating the D2D links at lower signal-to-noise ratio (SNR) values to overcome the effect of the impulsive PLC channel by utilizing diversity branches.

QoS-aware traffic scheduling framework in cognitive radio based smart grids using multi-objective optimization of latency and throughput

To meet the diverse QoS requirements of future Smart Grid (SG) communication network, efficient traffic scheduling and optimization techniques to prioritize data from various SG applications are required. Recently, focus has only been on the latency and criticality based priority-aware policies for same channel bandwidth. Traffic scheduling and subsequent channel allocation are required to support both the differential throughput and latency requirements simultaneously for channels having different bandwidths and SNRs. In this paper, a QoS-aware framework for data traffic scheduling in cognitive radio based SG communication network is proposed. The channels available to smart grid Communication Nodes (SCNs) are categorized as low and high bandwidths. For each bandwidth, all SG applications are categorized into several priority-classes comprised of latency and throughput sub-classes. For both channel bandwidths, the scheduler maintains and updates two sets of priority queues based on weights associated with each class and its data type. The complete scheduling framework is formulated as a multi-objective optimization problem. The overall objective function is the weighted sum of individual utility functions of latency and throughput. A novel usage of Adam optimizer is proposed to minimize the latency and maximize the throughput by obtaining optimal system cost, resulting in optimal decision policy. Simulation results show that the proposed algorithm achieves desired QoS requirements in the presence of heavy PU traffic, whereas in case of no priority assignment, QoS requirements of lower priority applications are met by compromising the QoS of higher priority data.

Optimal placement of data concentrators for expansion of the smart grid communications network

Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high-performing, reliable, and resilient power system. Three network design formulations based on mixed-integer linear and non-linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much-improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality-of-service in the event of an outage. Analysis of multi-period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.

DYNAMIC SMART GRID COMMUNICATION PARAMETERS BASED COGNITIVE RADIO NETWORK

The demand for more spectrums in a smart grid communication network is a significant challenge in originally scarce spectrum resources. Cognitive radio (CR) is a powerful technique for solving the spectrum scarcity problem by adapting the transmission parameters according to predefined objectives in an active wireless communication network. This paper presents a cognitive radio decision engine that dynamically selects optimal radio transmission parameters for wireless home area networks (HAN) of smart grid applications via the multi-objective differential evolution (MODE) optimization method. The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. Simulation results highlight the superiority of the proposed system in terms of accuracy and convergence as compared with other evolution algorithms (genetic optimization, particle swarm optimization, and ant colony optimization) for different communication modes (power saving mode, high throughput mode, emergency communication mode, and balanced mode).

Reliability evaluation of a stochastic-flow network in terms of minimal paths with budget constraint

In a stochastic-flow network with budget constraint, the network reliability for level (d, b), i.e., , where d is a given demand value and b is a budget limit, is the probability of transmitting at least d units of flow from a source node to a sink node within the budget of b. The problem of evaluating in terms of Minimal Paths (MPs), which is called the (d, b)-MP problem, has been of considerable interest in the recent decades. Here, presenting some new results, an improved algorithm is proposed for this problem. Some numerical comparisons between our MATLAB implementation of the algorithm proposed in this article and a recently proposed one are made. This way, computational comparative results on some benchmarks and thousands of random test problems are provided in the sense of performance profile introduced by Dolan and Moré. Moreover, complexity results are provided. The complexity and numerical results show the efficiency of our algorithm in comparison with the others. Furthermore, we state how to use the output of the algorithm in order to assess the system reliability. Ultimately, based on the main proposed algorithm, a simple algorithm is stated to evaluate the reliability of some smart grid communication networks.

Joint power and channel allocation scheme for IEEE 802.11af based smart grid communication network

To reap the full potentials of Smart grid (SG), a reliable, resourceful, efficient and cost-effective communication network is inevitable. The crucial need to transmit a significant amount of smart grid application data in an efficient spectral manner makes cognitive radio (CR) technology most suitable for SG environment. Identically, TV white space (TVWS) is the most expectant candidate for CR based smart grid communication network (CRSGCN). In this paper, we are investigating the problem of joint power and channel allocation (JPCA), which is among the most important and widely explored area in cognitive radio domain. We first model a typical scenario in a neighborhood area network (NAN) communication using IEEE 802.11af via open loop regulatory framework for TVWS. A mathematical model is formulated to jointly address power and channel allocation considering practical constraints for two real-world scenarios of fairness-based and priority-based allocation. Next, an efficient power allocation (PA) scheme is presented, meeting quality-of-service (QoS) requirements, followed by channel allocation (CA) scheme based on cuckoo search algorithm (CSA). The performance of the proposed solutions is analyzed using exhaustive simulations to optimize power consumption, fairness and user rewards. The presented results in the form of graph and numerical comparisons indicate the effectiveness of our allocation algorithm to achieve the desired objectives.

Channel Access and Power Control for Energy-Efficient Delay-Aware Heterogeneous Cellular Networks for Smart Grid Communications Using Deep Reinforcement Learning

Cellular technology with long-term evolution (LTE)-based standards is a preferable choice for smart grid neighborhood area networks due to its high availability and scalability. However, the integration of cellular networks and smart grid communications puts forth a significant challenge due to the simultaneous transmission of real-time smart grid data which could cause radio access network (RAN) congestions. Heterogeneous cellular networks (HetNets) have been proposed to improve the performance of LTE because HetNets can alleviate RAN congestions by off-loading access attempts from a macrocell to small cells. In this paper, we study energy efficiency and delay problems in HetNets for transmitting smart grid data with different delay requirements. We propose a distributed channel access and power control scheme, and develop a learning-based approach for the phasor measurement units (PMUs) to transmit data successfully by considering interference and signal-to-interference-plus-noise ratio (SINR) constraints. In particular, we exploit a deep reinforcement learning(DRL)-based method to train the PMUs to learn an optimal policy that maximizes the earned reward of successful transmissions without having knowledge on the system dynamics. Results show that the DRL approach obtains good performance without knowing the system dynamic beforehand and outperforms the Gittin index policy in different normal ratios, minimum SINR requirements and number of users in the cell.

A Reliability Perspective of Distribution Systems in Smart Grid Communication Networks

A Reliability Perspective of Distribution Systems in Smart Grid Communication Networks

Cost Efficient Data Aggregation Point Placement With Interdependent Communication and Power Networks in Smart Grid

In a smart grid, data aggregation points (DAPs) are communication hubs between sensors, actuators, and control center. Each DAP has a limited communication capacity and requires an installation cost. We want optimal placement for DAPs to minimize cost without compromising communication quality-of-service (QoS) requirements. DAP placement problem must not be dealt with as a communication issue in isolation because of interdependency between communication network and power network. Inter-network cascading failures may occur when a failure in DAP leads to a failure in power network, which leads to further failures in other DAPs. To avoid such failures, we have identified three requirements: power independence , communication robustness , and power robustness . We have formulated a constrained optimization problem, called cost minimization DAP placement (CMDP) to minimize DAP installation cost while satisfying communication QoS requirements, and avoiding inter-network cascading failures. CMDP is proven NP-hard, and we have proposed a heuristic algorithm to produce sub-optimal solution in reasonable time. Evaluation results show that for small systems, compared to exhaustive search, the heuristic algorithm produces identical solutions at computation time which is 375 000 times shorter. Results also indicate that communication robustness and power robustness requirements can increase linearly total DAP installation cost.

Delay-Aware Energy-Efficient Joint Power Control and Mode Selection in Device-to-Device Communications for FREEDM Systems in Smart Grids

Cellular technology with long-term evolution (LTE)-based standards is a promising technology for smart grid communication networks. However, the integration of cellular technology and smart grid communications is a significant challenge due to the transmission of simultaneous and delay-sensitive smart grid data. The Device-to-device (D2D) communications are proposed as critical solutions to enhance the performance of the LTE. In this paper, we study energy efficiency and delay problems in D2D underlaying cellular communications for the future renewable electric energy delivery and management (FREEDM) system in smart grid with different message sizes and varying channel conditions. We adopt a D2D-assisted relaying framework to assist links that meet poor channel conditions in order to increase the data rate of intelligent energy management devices with large size report. We propose a joint power control and mode selection scheme to deal with the problem and develop a brute-force-based algorithm to find the solutions. We conduct simulations to show the effectiveness of the proposed scheme in balancing the trade-off between energy efficiency and end-to-end delay and show significant energy efficiency improvements when exploiting the proposed scheme compared to direct and relaying schemes in a variety of different conditions.

QoS-based dynamic channel selection algorithm for cognitive radio based smart grid communication network

Smart Grid (SG) operation requires multi-way communication among power generation, transmission, distribution, and consumer facilities. The complete SG architecture involves multiple layers of networks stretching from energy control center to consumer facilities and multiple applications with diverse Quality-of-Service (QoS) requirements. To meet these requirements, Cognitive Radio (CR) which offers efficient spectrum utilization and sharing, has been considered as a favorable solution. Due to diverse requirements of latency, data rate and reliability, it is a great challenge to support various SG applications under spectrum sensing uncertainties, Signal-to-Noise Ratio (SNR) variations and availability of desired channel bandwidth. In this paper, we propose a dynamic channel selection algorithm based on Fuzzy Inference System (FIS) capable of selecting the most appropriate available idle channel with desired bandwidth, minimum required SNR and probability of miss detection Pmd. The proposed algorithm utilizes various modes of IEEE Standard 802.22, Wireless Regional Area Network (WRAN), at the physical layer. In case of unavailability of idle channels with required SNR and/or Pmd, we propose a novel approach to select the channel with closest possible parameters aiming to reduce the re-transmission probability and thus achieve the desired throughput, latency and reliability requirements. The performance is compared with existing algorithms to demonstrate the effectiveness and superiority of the proposed algorithm.

A new efficient error control algorithm for wireless sensor networks in smart grid

Error detection and correction is an important issue in the design and maintenance of a smart grid communication network to provide reliable communication between sender and receiver. Various error-control coding techniques are employed to reduce bit error rates (BER) in wireless sensor networks (WSNs). The performance of these techniques is also compared and evaluated to find the most suitable technique for WSNs. This is the first study to compare the most efficient coding techniques in the smart grid environment, and it suggests a new error correction algorithm based on this comparison result. Therefore, this article first examines and compares two forward error control (FEC) coding techniques such as Bose-Chaudhuri-Hochquenghem code (BCH) and Reed Solomon code (RS) with various modulation methods including frequency shift keying (FSK), offset quadrature phase-shift keying (OQPSK), and differential phase shift keying (DPSK) in a 500 kV line-of-sight (LoS) substation smart grid environment. Second, as a result of this comparison, a new adaptive error control (AEC) algorithm is proposed. Adaptive error control adaptively changes error correction code (ECC) based on the channel behavior that is observed through the packet error rate (PER) in the recent previous transmissions. The link-quality-aware capacitated minimum hop spanning tree (LQ-CMST) algorithm and the multi-channel scheduling algorithm are used for data transmission over the log-normal channel. Therefore, the performance of compared coding techniques and AEC are also evaluated when multiple channels are used during transmission. Further, AEC is compared with static RS and without-FEC methods based on performance metrics such as the throughput, BER, and delay in different smart grid environments, e.g., 500 kV Substation (LoS), underground network transformer vaults (UTV) (LoS), and main power control room (MPR) (LoS). Our simulation results indicate that the proposed AEC algorithm achieves better performances than all those techniques.