Smart Grid Network Research Articles

Impact of Distributed Energy Resources in Smart Homes and Community-Based Electricity Market

The transformation of passive to energy-active consumers in smart homes has been enabled by the proliferation of distributed energy resources (DERs) and demand-side management technologies. Building a smart community-based electricity market (SCEM) centred around a local energy community has the potential to expedite this transformation by tapping the flexibility associated with peer-to-peer energy transactions inside the community. The article presents a systematic approach to quantifying the benefits of smart homes, starting from the energy-passive to energy-active homes under SCEM with intermediate stages identifying smart homes with DERs. The investigation also includes the impact of seasonal variations with contrasting characteristics. Smart homes with solar PV and energy storage under SCEM achieve maximum savings of 50% and 36.6% for the summer and winter months, respectively, and SCEM boosts consumption of localized green energy by a further 31% in the summer month. ES leverages the smart homes gain significantly through self-consumption and energy arbitrage. However, the operation of ES under SCEM in the winter month reduces the network's voltage stability. The article is conducted based on real-life measurements from an energy community in Ireland. Recommendations are made further to boost the transition of smart homes toward the decarburization of smart grid networks.

ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks

This research explores the potential of Machine Learning (ML) to enhance wireless communication networks, specifically in the context of Wireless Smart Grid Networks (WSGNs). We integrated ML into the well-established Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced version called ML-RPL. This novel protocol utilizes CatBoost, a Gradient Boosted Decision Trees (GBDT) algorithm, to optimize routing decisions. The ML model, trained on a dataset of routing metrics, predicts the probability of successfully reaching a destination node. Each node in the network uses the model to choose the route with the highest probability of effectively delivering packets. Our performance evaluation, carried out in a realistic scenario and under various traffic loads, reveals that ML-RPL significantly improves the packet delivery ratio and minimizes end-to-end delay, making it a promising solution for more efficient and responsive WSGNs.

Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection

The growing cyber space with the developments in cyber network technologies in smart grid (SG) systems has necessitated questioning the reliability of networks and taking precautions against possible cyber threats. For this reason, defensive strategies and approaches against cyber attacks must be improved to sustain secure information flow of the network connections used in electricity generation, transmission, distribution, and consumption. This paper proposes a multi-agent multi environment deep reinforcement learning (MM-DRL) based defender response against cyber epidemics consisting coordinated cyber-attacks (multi-CAs) in the same time frame scheme to sustain security for SG networks. In this regard, the PMU-connected 123-bus system is integrated as a Markov game. MM-DRL approach is implemented for subenvironments of a typical SG system. Multi-CAs game aims to coordinate PMU signals across intersections to improve the network efficiency of a SG. DRL has been applied to data control recently and demonstrated promising performance where each data signal is regarded as an agent. Conversely, multi-CAs are self-renewing emerging causative agent of electricity theft, network disturbances, and data manipulation in SG systems characterized with wide characteristic diversity and rapid evolution. The game results show that the presented request response algorithm is able to minimize system attack damage and maintain protection duties when compared to a benchmark without request response. In addition, the performance of the MM-DRL approach compared to other developed methods is examined.

A Survey on IoT-Enabled Smart Grids: Technologies, Architectures, Applications, and Challenges

The state of the power system has changed over the last decades. Recently, the power system has faced several challenges and issues. On the one side, demands for electrical energy are increasing day-by-day, with power losses, grid failure, and lack of smart technology; on the other side, security threats are also increasing. The current power grid cannot deal with these issues. The Internet of things (IoT) has grown quickly in a very short time because of its main features. By using IoT in the power grid, we can enhance the conventional grid’s efficiency, capacity, reliability, sustainability, scalability, and stability. Using the IoT in smart grids resolves the numerous problems faced by current smart grids. According to the latest research on IoT-enabled smart grid (SG) systems, security issues have been identified as one of the key problems. This paper reviews the different security aspects and applications while integrating the IoT with smart grids and discusses various frameworks. Apart from this, we also focus on various IoT and non-IoT technologies used in the smart grid network, such as sensing, communication, and computing technologies, as well as their standards.

Enhancement of Power Quality with Increased Levels of Multi-level Inverters in Smart Grid Applications

Research focuses on renewable energy-based smart grids to meet appropriate energy demands in a world where climate change and power management are becoming more significant. The smart grid is a modernized autonomous power network that can efficiently transmit electricity, save money and resources, and improve the stability of the local grid. As a result, a multi-level inverter (MLI) that is connected to the smart grid is provided in this work. To meet the requirements of the smart grid network, the inverter is controlled using a model predictive control algorithm with increased levels with the main objectives of controlling the injected power generated by the renewable source, improving the quality of the current waveform, lowering THD, and eliminating the shift phase between the current and the grid voltage.

Deep Learning-Based Authentication Framework for Secure Terrestrial Communications in Next Generation Heterogeneous Networks

Smart Grid (SG) network is growing rapidly to improve the efficiency and reliability of the power supply. It will connect billions of devices to the internet in the near future. This increase in the number of internet-connected devices has led to an exponential increase in the market size of smart meters. These smart meters and devices will generate lakhs of gigabytes of data in upcoming years. To handle this massive amount of data flow, the Information and Communication Technology (ICT) of SG will experience huge advancements in terms of 5G and 6G wireless networks. These networks will provide higher data rates by utilizing the huge bandwidth spectrum of heterogeneous networks (HetNet). As the massive amount of data will be transmitted to various components of the smart grid using the internet, an open channel. Therefore, in addition to the need for high bandwidth and low latency, HetNet security has also become a prime concern. This paper presents an efficient approach to authenticate the smart devices in SG environment by using the channel state information (CSI) of the wireless links. Firstly, Low-Density Parity-Check (LDPC) codes are used to maintain the confidentiality and reliability of data transmission in the Advanced Metering Infrastructure (AMI) of SG. Then, a Convolution Neural Network (CNN)-based authentication framework is proposed to enhance the physical layer security (PLS) of terrestrial communications in next generation SG Hetnets. Further, the proposed framework is evaluated using received Channel State Information (CSI) values from smart meters placed at different locations, while also taking the scenario of active eavesdroppers into account. Experimental analysis reveals that the proposed CNN based authentication achieves 30.76 percent better authentication rate than existing DNN based authentication scheme. Further, the LDPC coded data transmission scheme reduces the bit error rate (BER) by 20 percent as compared to uncoded data transmission.

VNF Orchestration and Power-Disjoint Traffic Flow Routing for Optimal Communication Robustness in Smart Grid With Cyber-Physical Interdependence

We explore the use of software-defined networking (SDN) technology in building a communication network for smart grid. With cyber-physical interdependence, such communication network may suffer from cross-network cascading failures. To prevent the failures, we perform virtual network function (VNF) orchestration jointly with power-disjoint routing. Our work is novel in proposing an efficient scheme to find power-disjoint routes at the same time of performing VNF orchestration. We formulate an optimization to maximize the ratio of power-disjoint route count to VNF orchestration cost. The optimization has a non-linear non-convex objective function. We propose a two-level hierarchical solution approach. At higher level, the scheme converts the problem into a fractional maximum flow circulation, which can be solved using simplex method to find the maximum number of power-disjoint routes. Given a higher level solution, the lower level aims to minimize the VNF orchestration cost while satisfying VNF chaining and placement requirements. This lower level hierarchy uses the Dijkstra’s algorithm in building a sequence of minimum spanning trees, each roots at the current VNF hosting node in a VNF chain. Extensive simulation results confirm that the proposed scheme can find the maximum number of power-disjoint routes and minimize the cost within a second, for a system with 120 communication nodes. The results show that the number of power-disjoint routes can be increased by increasing either the number of nodes or node degree, but only the node degree can keep the cost flat. Therefore, one should build a robust software-defined smart grid communication network by enhancing node connectivity.

Cyber-Attack and Reliability Monitoring of The Synchrophasor Smart Grid Network

The recent advancement of synchrophasor measurements technology in the conventional power grid can monitor and control the state variables of the network very accurately at a high sampling rate in real-time. The complete observability of system states can be achieved through the Phasor Measurement Unit (PMU). The inclusion of a zero injection bus (ZIB) optimized the total number of PMU requirements for complete observation of the synchrophasor network. The communication channels between measurement devices and control centers are highly vulnerable to cyber threats. Thus, an anomaly that occurs with PMU devices during a cyber-attack can affect the system’s reliability. Therefore, monitoring the reliability of the synchrophasor network has become essential for healthy power operation. Synchrophasor measurement technology can enhance wide-area surveillance and security functionality. However, the dependability of such technologies in the context of information network accessibility has yet to be investigated in a coherent model. Growing electric grid defence levels to mitigate the impact of cyber-attacks is essential. The cumulative effect of synchrophasor network observability and reliability is discussed in this paper by optimizing the number of PMUs deployed and the interruption load that occurs during an anomaly with PMU while taking ZIB into account.The backup PMU deployment modeling is also presented to secure the reliability and observability of the grid network during an anomaly occurs with PMU. The indices, Interrupted Load Probability Index (ILPI) and Expected Demand Not Supplied (EDNS), are used to evaluate the reliability of synchrophasor grid networks by integrating the state probability of PMU unavailability due to cyber intrusion.

Smart Grid and wind generators: an overview of cyber threats and vulnerabilities of power supply networks

The subject of this study is the cyber vulnerability of wind generators, as part of the cyberphysical system of intelligent power supply networks, Smart Grid. Wind generators produce electricity for further distribution in the network between «smart» electricity consumers, which often include autonomous power systems in medical institutions, autonomous power supply of homes, charging stations for cars, etc. Wind generators operate in two aspects: in the physical and information space. Thus, a violation of the security of the information flow of a wind generator can affect the physical performance of electricity generation, and disable equipment. The study aims to identify types of cyber threats in the wind generator network based on the analysis of known attack incidents, analysis of the Smart Grid network structure, network devices, protocols, and control mechanisms of a wind generator. The tasks of the work are: review and analyze known cyberattack incidents; review the classification of cyber threats to wind farms; consider the most common methods of attacks on the cyberphysical system of wind farms; consider ways of intrusions into the information flow of the cyberphysical system wind generator; consider resilience mechanisms of wind generators in case of a cyberattack, consider the directions of further research. The methods are a systematic approach that provides a comprehensive study of the problem, quantitative and qualitative analysis of incidents of cyber attacks on wind generators, and methods of attacks. The following results were obtained: 11 large-scale known incidents of cyber attacks on the cyberphysical systems of the energy sector and smart power supply networks were analyzed, and information flow features and structure of the wind generators were considered. Main communication interfaces of the Smart Grid network were reviewed, control mechanisms for the physical parts of the wind generator system such as automatic voltage regulator, and automatic generation control were observed, vulnerable data transmission protocols, DNP3 in particular, were analyzed, possible consequences in the case of a cyber-intrusion into the network were considered. Conclusions: wind farms, as part of the Smart Grid system, are a convenient target for cyberattacks, as the number of potential ways to interfere with the information flow of the cyberphysical system is growing due to an increase in the number of sensors, communication channels in the network. This is especially important for the further development of wind farm security systems, which at the time, are not able to provide high accuracy of intrusion detection into the information flow.

Coverage hole optimization with a mobile sensor in wireless sensor networks for smart grid

The Smart Grid (SG) enables electricity and energy exchange between two ends of the grid. Wireless Sensor Networks (WSNs) are considered as an enabling communication technology for effective SG management. How well the target region is covered by a WSN indicates the service quality of the relevant implementation. Since effective monitoring is key to exploiting the benefits of the SG, maintaining the desired coverage level is critical in WSN for SG applications. However, some uncovered regions (coverage holes) might emerge due to several reasons such as improper localization or sensor malfunctioning. In this paper, we propose using a mobile sensor to cover such blind regions in a timely and hence energy-efficient way. First, we present a novel 0–1 mixed integer programming model without restrictive assumptions about the mobility pattern to find the shortest trajectory for the mobile sensor. Then we show that the problem is NP-hard with a polynomial reduction to the Traveling Salesman Problem. Consequently, we present an optimization-based heuristic, two heuristic-based algorithms, and a hybrid algorithm. We show that our heuristics are efficient as they find high quality solutions in a few seconds. We also observe that assumptions about the starting point of the tour and the potential set of stops significantly affect the final tour’s length.

Research on source-load coordinated dispatching of flexible DC distribution network based on big data

In this new round of power network development, the concept of coordinated and optimized operation of power network interconnection and smart grid “source network load” has gradually attracted attention. Based on the analysis of the impact of the flexible DC distribution network on the complex energy system with multiple data sources and large data volume under the big data platform, the coordinated dispatching of the source and load of the big data flexible DC distribution network is studied. Therefore, a source-load matching index that can evaluate the impact of different types of loads on the stability of the flexible DC grid is constructed and incorporated into the load recovery optimization model. First, use the scene reduction method to process all generated scenes; next, take the reduction technology and the scene generation method into account of the uncertainty caused by the prediction error and incorporate them into the multi-objective function optimization model; finally, the membership function of fuzzy numbers is used to model uncertainty. So as to construct a load recovery model that can coordinate the load recovery amount, importance and system dynamic response. The actual meaning of the matching index is explained through model solving and actual case analysis.

An Adaptive Network Design for Advanced Metering Infrastructure in a Smart Grid.

A smart grid is a next-generation intelligent power grid that can maximize energy efficiency by monitoring power information in real time and by controlling the flow of power by introducing IT communication technology to the existing power grid. In order to apply a wireless communication network to a smart grid, it is necessary to be able to efficiently process large amounts of power-related data while enabling a high level of reliability and quality of service (QoS) support. In addition, international standards-based design is essential considering compatibility and scalability. The IEEE 802.15.4 standard is considered to be the most powerful communication method for processing data through the smart grid AMI. To reduce the energy consumption, as the duty cycle of the superframe increases, the probability of the congestion increases. However, this binary exponential algorithm in IEEE 802.15.4 standard does not account for the application of traffic characteristics that essentially negatively affect the smart grid network performances in terms of packet delivery ratio and time delay. Therefore, in this paper, we propose a new transmission scheme to reduce performance degradation by excessive collisions in the content access period (CAP), when data transmission is performed in IEEE 802.15.4 applied to smart grids. In addition, we investigated the main research topics required when applying wireless networking technology to smart grids and suggested improvement measures. Simulation results showed that the proposed scheme increased the data delivery rate and reduced the latency, and it was confirmed that reliability was improved.

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper.

Assessment of Smart Grid Systems for Electricity Using Power Maclaurin Symmetric Mean Operators Based on T-Spherical Fuzzy Information

Traditional electricity networks are replaced by smart grids to increase efficiency at a low cost. Several energy projects in Pakistan have been developed, while others are currently in the planning stages. To assess the performance of the smart grids in Pakistan, this article employs a multi-attribute group decision-making (MAGDM) strategy based on power Maclaurin symmetric mean (PMSM) operators. We proposed a T-spherical fuzzy (TSF) power MSM (TSFPMSM), and a weighted TSFPMSM (WTSFPMSM) operator. The proposed work aims to analyze the problem involving smart grids in an uncertain environment by covering four aspects of uncertain information. The idempotency, boundedness, and monotonicity features of the proposed TSFPMSM are investigated. In order to assess Pakistan’s smart grid networks based on the suggested TSFPMSM operators, a MAGDM algorithm has been developed. The sensitivity analysis of the proposed numerical example is analyzed based on observing the reaction of the variation of the sensitive parameters, followed by a comprehensive comparative study. The comparison results show the superiority of the proposed approach.

Multilevel Inverter with Predictive Control for Renewable Energy Smart Grid Applications

In a world where climate changes and power management are becoming increasingly important, research work focuses on renewable energy based smart grid to meet adequate demands of energy. The smart grid is a modernized autonomous power network that can transmit electricity effectively, conserve resources and costs, and increase the local grid's stability. As a result, a smart grid connected multilevel inverter is presented in this work. The inverter is controlled using a model predictive control algorithm with increased levels with the primary goal of controlling the injected power generated by the renewable source, improving the quality of the current waveform, lowering THD, and eliminating the shift phase among the injected current and the grid voltage in effort to match the smart grid network's requirements. Therefore, this paper aims to show the performance analysis of multilevel inverters with predictive control for renewable energy smart grids application. The major concern was observed with most of the multi-level inverters are that with increased level total harmonic distortion (THD) is increased if switching control is not designed properly. Therefore, in this case predictive control is implemented on renewable energy based smart grid inverters to increase level with minimum voltage THD and current THD. The case analyzed voltage and current THD in the different levels of multilevel inverter. The result analysis was performed on 7, 11, 17, 21, 27, 31, 37 and 41 respectively. From result analysis it was observed that minimum THD was observed with level 31, i.e., 0.35% and at level 41 it was increased up to 0.55% which is still not high as compared to other existing MLI architectures. The result findings of proposed approach decreased THD with varying levels and outperforms better as compared to other works.

Performance Evaluation of Integrated Antennas on Photovoltaic Solar Cells

The performance of antennas is critical to ensuring reliable wireless communication and robust data transmission. Unfortunately, antennas’ performance gets degraded when loaded with lossy materials. This paper presents the numerical and experimental evaluation of low-profile antennas’ performance when integrated with photovoltaic (PV) solar cells for potential use in smart grid and green power networks. Such integrated antennas can serve as a communication unit and sensors to monitor PV solar cells. For convenience, a microstrip patch antenna was used in this assessment study, where the antenna was designed, numerically simulated, and experimentally tested. After which, it was installed on top of a PV solar cell at different orientations. The antenna is designed to operate within the 2.45 GHz ISM band. Based on the results, the antenna performed well when placed at the middle of the PV solar cell with a peak gain of 2.58 dBi compared to other placements within the PV solar cell. Moreover, creating a small air gap between the antenna and the PV solar cell results in better performance. Based on the findings of this study, the antenna has satisfactory performance when integrated with PV cells, which is promising to deploy in many applications, including smart grid networks.

A cluster-based approach against wormhole attacks in MANETs among smart grid

Mobile ad hoc networks in smart grid become more and more popular and significant. However, the deployment scenarios, the functionality requirements and the limited capabilities make them vulnerable to a large group of attacks, e.g., wormhole attacks. In this paper, a novel cluster-based scheme is proposed for the purpose of preventing wormhole attacks. Firstly, a clustering algorithm is proposed that employs a powerful analytical hierarchy process methodology to elect clusterheads. Afterwards, the elected clusterheads are required to implement the wormhole attacks prevention scheme which includes two phases, i.e., detection phase and location phase. By detection phase, the existence of wormhole attacks can be detected. By location phase, the wormhole nodes are able to be detected. Simulation results indicate the scheme in our paper can be used to prevent wormhole attacks in ad hoc networks efficiently.

Smart Grid based Wireless Communication in 5G Network for Monitoring and Control Systems in Renewable Energy Management

Wireless networks are becoming ubiquitous and as the cost of equipment decreases and performance increases, it becomes both economically and technologically feasible to deploy wireless networks in power systems and industrial environments for a wide range of applications. They have advantage of providing diverse controlling features through a unified communication platform. Application of such networks in the smart grid/industrial environments is under active research and expected to become an integral part of the power system. This research propose novel technique smart grid communication in wireless 5G networks for monitoring and controlling management. Here the smart grid designing has been done based on wireless communication networks. The smart grid network for renewable energy has been controlled using Stackelberg equilibrium based SCADA (supervisory control and data acquisition) method. The control method based collected data has been monitored for detection of malicious activities in the network using supervised radial basis fuzzy systems. The experimental analysis has been carried out based on control system and network malicious activities. Here the control system based parameters analysed are Scalability of 65%, QoS of 71%, Power consumption of 41%, Network Efficiency of 92%. Then machine learning based malicious activities detection in terms of accuarcy of 96%, network security of 88%, throughput of 94%, Network delay of 41%. Proposed method supports interoperability of multiple types of inverters, is scalable and flexible, and transmits data over a secure communication channel.

Automatic fault identification in WSN‐based smart grid environment

SummaryWireless sensor network (WSN) plays a vital role in the smart grid (SG) environment. Due to the fault tolerance characteristics, cost reduction, and large‐scale convergence, SG introduces many unique challenges caused by system and functional devices. To solve this problem, a WSN‐based SG network is used to identify faults. During data transmission, faulty nodes occur in the transmission line. The node failures, calibration, network failures, low battery, dead nodes, environmental changes, software failures, and so on lead to the interruption in data delivery and spoil the entire WSN‐based SG network. In order to tackle these problems, the new WSN model is designed to detect the faults in the transmission line based on the SG environment. This paper uses the Adaptive Zigbee‐Aquila communication protocol (AZACP) to find the optimal shortest path for transferring data. AZACP finds the shortest optimal path for transmitting the sensed data to the base station with low cost and less time consumption. Fault detection automatically identifies the fault in the transmission line and isolates the faulty nodes to ensure efficient data transmission in WSN. Here, enhanced recurrent equilibrium neural network (ERENN) is introduced to identify the fault in data transmission. The proposed approach provides better performance in terms of evaluating performance metrics like throughput, delay, reliability, average residual energy, number of total transmissions, network lifetime, efficiency, and bit error rate (BER).

Interference Cancellation and Efficient Channel Allocation for Primary and Secondary Users Using Hybrid Cognitive (M2M) Mac Routing Protocol

It is projected that communication among cognitive machine-to-machine (M2M) in smart grid networks is necessary and vital in forthcoming years. The objective of this paper is to bring out the significance of cognitive medium access control (MAC) protocol focusing on the distinctive characteristics of the devices in M2M and requirements of the smart grid communication. Therefore, developing a cognitive radio system which supports dynamic access to the spectrum available that is recently considered as a novel solution for extending wireless systems. In this paper, MAC layer sensing approaches in cognitive radio networks are investigated by considering both proactive and reactive sensing. To evaluate the performance, analytical modeling and simulation studies are carried out by comparing with state of art methods. As a result, the proposed HCM2M-MACachieves 687.3 kbps of blocking probability, 368.6kbps of dropping probability, 96.4% bandwidth utilization, 67.3% end to end delay and 89.3% of throughput.