Enabled Smart Grid Research Articles

IOT Based Smart Grid to Remotely Monitor and Control Renewable Energy Sources using Cloud Computing

Abstract: The growing demand for clean and sustainable energy sources necessitates advancements in grid structure. This project explores the potential of an IOT-based smart grid to remotely monitor and control renewable energy sources. In this, we use solar energy as a renewable energy source, and with the help of cloud computing, we monitor and control different types of loads. The paper highlights the key components, functionalities, and benefits of such a system.The increasing demand for sustainable energy solutions fuels the development of smart grid technologies. This project proposes an IOT (Internet of Things)-enabled smart grid architecture for remote monitoring and control of renewable energy sources. The core concept involves integrating sensors with renewable energy sources like solar or wind turbines. These sensors collect real-time data on energy generation, environmental conditions, and system health. This data is then transmitted over a network to a central control system or a cloud platform for further analysis. By implementing remote control capabilities, grid operators can: regulate energy production based on demand; integrate storage solutions for surplus energy; and improve overall grid stability and reliability. The project concludes by outlining the benefits of this approach, including a reduced carbon footprint, cost savings, and a more sustainable energy future.

Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity

Internet of Things (IoT)-enabled Smart Grid (SG) network is envisioned as the next-generation network for intelligent and efficient electric power transmission. In SG environment, the Smart Meters (SMs) mostly exchange services and data from Service Providers (SPs) via insecure public channel. This makes the entire SG ecosystem vulnerable to various security threats. Motivated from the aforementioned challenges, we incorporate Digital Twin (DT) technology, Software-Defined Networking (SDN), Deep Learning (DL) and blockchain into the design of a novel SG network. Specifically, a secure communication channel is first designed using an authentication method based on blockchain technology that has the ability to withstand a number of well-known assaults. Second, a new DL architecture that includes a self-attention mechanism, a Bidirectional-Gated Recurrent Unit (Bi-GRU) model, fully connected layers, and a softmax classifier is designed to enhance the attack detection process in SG environments. To deliver low latency and real-time services, the SDN is next employed as the network’s backbone to send requests from SMs to a global SDN controller. DT technology is finally integrated into the SDN control plane, which stores the operating states and behavior models of SMs and communicates with SMs. The efficiency of the proposed framework is demonstrated by the blockchain implementation used in the SG network to assess computing time for the various numbers of transactions per block. Finally, the numerical results based on the N-BaIoT dataset shows better intrusion detection.

English

English

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring.

Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

Due to the drastic increase in global population as well as economy, electricity demand becomes considerably high. The recently developed smart grid (SG) technology has the ability to minimize power loss at the time of power distribution. Machine learning (ML) and deep learning (DL) models can be effectually developed for the design of SG stability techniques. This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability (SSODLSA-SGS) prediction model. Primarily, class imbalance data handling process is performed using Synthetic minority oversampling technique (SMOTE) technique. The SSODLSA-SGS model involves two stages of pre-processing namely data normalization and transformation. Besides, the SSODLSA-SGS model derives a deep belief-back propagation neural network (DBN-BN) model for the prediction of SG stability. Finally, social spider optimization (SSO) algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model. The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model. A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model. The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

Efficient adaptive deep neural network model for securing demand side management in IoT enabled smart grid

Electricity distribution reliability is critical for global safety, healthcare and the economy. The smart grid is defined as the combination of sensors and modern technology into the power system, which innovates the electricity generation, distribution, surveillance, and control model. Several issues must be addressed in order for the smart grid to be useful. The smart grid’s security is both a difficult task and a critical concern. An Adaptive Deep Neural Network (ADNN) will be developed in this study to secure demand-side management in an IoT-enabled smart grid. The proposed ADNN combines Deep Neural Networks (DNN) and the Squirrel Search Algorithm (SSA). The SSA is used in the DNN to improve its performance by selecting optimal weighting factors. The proposed methodology will be used to ensure that energy is used efficiently based on its priority level. It will also be used to prevent intrusions into the smart grid. A proposed classifier will classify the intrusion or dishonest entities. To optimize energy utilization and empower security, this proposed multi-agent energy management system (MEMS) and autonomous interface system (AIS) are being developed. Performance metrics such as energy consumption, load demand power, and generated power will be used to evaluate the proposed methodology, which will be implemented in MATLAB. Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) will be used to compare the proposed methodology to existing methods.

A Robust Access Control Protocol for the Smart Grid Systems

Lightweight cryptography (LWC)-based authenticated encryption with associative data (AEAD) cryptographic primitives require fewer computational and energy resources than conventional cryptographic primitives as a single operation of an AEAD scheme provides confidentiality, integrity, and authenticity of data. This feature of AEAD schemes helps design an access control (AC) protocol to be leveraged for enhancing the security of the resource-constrained Internet of Things (IoT)-enabled smart grid (SG) system with low computational overhead and fewer cryptographic operations. This article presents a novel and robust AC protocol, called RACP-SG, which aims to enhance the security of resource-constrained IoT-enabled SG systems. RACP-SG employs an LWC-based AEAD scheme, ASCON and the hash function, ASCON-hash, along with elliptic curve cryptography to accomplish the AC phase. Besides, RACP-SG enables a smart meter (SM) and a service provider (SEP) to mutually authenticate each other and establish a session key (SK) while communicating across the public communication channel. By using the SK, the SM can securely transfer the gathered data to the SEP. We verify the security of the SK using the widely accepted random oracle model. Moreover, we conduct Scyther-based and informal security analyses to demonstrate that RACP-SG is protected against various covert security risks, such as replay, impersonation, and desynchronization attacks. Besides, we present a comparative study to illustrate that RACP-SG renders superior security features while reducing energy, storage, communication, and computational overheads compared to the state of the art.

Gradient Ascent Algorithm for Enhancing Secrecy Rate in Wireless Communications for Smart Grid

The emerging Internet of Things (IoT) and bidirectional communications in smart grid are expected to improve smart grid capabilities and electricity management. Because of massive number of IoT devices in smart grid, size of the data to be transmitted increases, that demands a high data rate to meet the real-time smart grid communication requirement. Sub-6 GHz, millimeter-wave (mmWave) technologies, and massive multiple-input multiple-output (MIMO) technologies can meet high data rate demands. However, IoT enabled smart grid is still subject to various security challenges such as eavesdropping, where attackers attempt to overhear the transmitted signals and the jamming attack, where the attacker perturbs the received signals at the receiver. In this paper, our goal is to investigate jamming and eavesdropping attacks while improving secrecy capacity for smart grid communications. Specifically, we propose to employ a hybrid beamforming design for wireless communications in smart energy grid. In previous works, the secrecy capacity is increased by randomly augmenting the source power or setting the system combiners. Unlike state-of-the-art, we design and evaluate the Gradient Ascent algorithm to search for the best combiners/waveform that maximizes the secrecy capacity in smart grid communications. We also study two different optimization scenarios by considering both fixed and variable transmit power. Numerical results are used for performance evaluation and supporting our formal analysis.

Reachability-Based False Data Injection Attacks and Defence Mechanisms for Cyberpower System

With the push for higher efficiency and reliability, an increasing number of intelligent electronic devices (IEDs) and associated information and communication technology (ICT) are integrated into the Internet of Things (IoT)-enabled smart grid. These advanced technologies and IEDs also bring potential vulnerabilities to the intelligent cyber–physical smart grid. State estimation, as a primary step of system monitoring and situational awareness, is a potential target for attackers. A number of other smart grid applications, such as voltage stability assessment and contingency screening, utilize state estimation results as input data. False data injection (FDI) is a specific way to attack state estimation by manipulating input data. Existing research mainly focuses on the mathematical analysis of FDI attacks; however, in these methods, discussions of reachability requirements to compromise measurements considering cyberinfrastructure are limited. Reachability is defined as a measure that estimates the number of hosts to compromise for the possible FDI. Most of the existing FDI attack methods require the simultaneous manipulation on multiple measurement devices in different substations, in order to bypass the bad data detection, which may be impractical. In this paper, a new type of reachability-based FDI attack considering the cybernetwork with a practical attack is proposed and validated on two IEEE test systems. The corresponding defence mechanisms are (a) decentralized state estimation (DSE), (b) DSE with additional backup computational nodes, (c) communication network rerouting, and (d) intrusion detection system, and they were developed and presented with validation for two IEEE test systems with superior performance for an IoT-enabled intelligent smart grid system.

Authentication model and trust management for secured internet of things enabled smart grid using blockchain framework

Flexible power trading is an important capability brought by smart grid to traditional electric distribution networks. Flexible power trading necessitates trust among smart grid entities, security and integrity for the transaction among entities for reliable power trading. In this work, a novel blockchain framework over internet of things enabled smart grid is proposed to facilitate secure and reliable power trading. Towards this aim, a cooperative trust-based reputation score model is proposed to provide reputation score to the entities. The communications between entities involved in power trading are authenticated using a three-party mutual authentication model. The transactions between the entities are integrity verified and secured in a blockchain framework. Use of blockchain provides an immutable storage for transactions and ensures consensus across all the entities involved in power trading, so that no entity can deny the participation in trading.

Authentication model and trust management for secured internet of things enabled smart grid using blockchain framework

Authentication model and trust management for secured internet of things enabled smart grid using blockchain framework

An integrated conceptual model to secure digitally enabled smart grid towards sustainable energy

An integrated conceptual model to secure digitally enabled smart grid towards sustainable energy

Implementation and Verification of 5G Network Slicing for Smart Grids

In this paper, 5G slicing networks are proposed to be deployed to address the issues associated with current communication networks in power grid systems and to enable smart grid applications. 5G slice architecture and the corresponding schemes including radio resource reservation, QoS scheduling strategy, VLAN, VPN and 5QI are applied for the access network, the transport network and the core network in the system, based on which a real 5G slicing network is implemented for smart grid applications. It is shown by field trial that the performance requirement regarding service isolation, bandwidth, delay and reliability required by various smart grid applications can be satisfied using the proposed schemes in the real system.

Improving smart grid security through 5G enabled IoT and edge computing

AbstractThis article investigates and analyzes the security aspects of 5G specifications from the perspective of IoT‐based smart grids. As the smart grid requires high‐speed and reliable communication to enable real‐time grid monitoring via Internet of Things (IoT) devices, 5G can be considered a catalyst to transform the current power grid infrastructure into a smart grid. Thus, an understanding of what 5G can bring in terms of cyber security in IoT‐based smart grids is important for design decisions and future risk analysis efforts. In this article, we explore a smart grid use case on automatic voltage control—a use case utilizing 5G as a wireless communication infrastructure with edge support. We identify the benefits 5G brings to several security aspects, and show how 5G security techniques are applicable to the smart grid, thus providing a foundation for future security analysis of 5G enabled smart grid systems. Future research should extend this work to additional smart grid use cases.

MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid

Electrical power being the key driver for any technology driven development, an intelligent technology enabled smart grid which ensures reliable, environment-friendly and power quality certainly provides the necessary fillip to the urban intelligence. This study introduces a novel differential approach of microgrid fault detection and classification as a smart grid enabler. The proposed microgrid protection scheme (MPS) involves an initial phase of pre-processing through anti-aliasing and filtering out of noise of the retrieved system parameters. This is followed by feature extraction process using Maximal Overlap Discrete Wavelet Transform (MODWT) with an abstract wavelet family of mother wavelet ‘FejerKorovkin’ and three level of decomposition. The differential energy calculated for both three-phase current and its zero-sequence current component at each of the decomposition level of MODWT finally serves as input to an Extreme Gradient Boost (XGBoost) based machine learning model to achieve incipient fault detection and classification. The combination of MODWT and XGBoost as an intelligent MPS working upon a pre-processed de-noised system signals, hitherto untried as per the knowledge of the authors, is tested using standard IEC microgrid test model under varied topological configurations, operational modes, fault conditions, etc. The simulation results, so extensively obtained, prove the effectiveness and robustness of the proposed approach of MPS. The MPS is additionally verified on an IEEE 13 bus microgrid model to reinforce the clam of efficiency.

A polymorphic heterogeneous security architecture for edge-enabled smart grids

The emergence of edge computing alleviates the network congestion, delay, and packet loss of cloud computing enabled smart grid architecture in big data scenarios, greatly improving the operation efficiency and communication quality of the smart grid. However, edge computing architecture also brings great security risks to the smart grid. This paper proposes a Polymorphic Heterogeneous Security Architecture (PHSA) for edge enabled smart grid, to assure the key services in the smart grid. Based on the dynamic heterogeneous redundancy architecture, the proposed architecture introduces intelligent scheduling of executors, intelligent arbitration of multi-state service response, cleaning and reconstruction of executors, and attack surface management. We establish a closed-loop core control process of decision and processing, adjustment and scheduling, cleaning reconstruction, migration, and recovery. Furthermore, a dynamic scheduling algorithm based on executor credit and heterogeneity is proposed. The simulation results show that, compared with the traditional smart grid security architecture, the PHSA has better defense ability and system reliability, significantly improving the security of edge enabled smart grid system. Besides, compared with the random scheduling strategy, the proposed dynamic executor scheduling algorithm can further improve the security of the system.

An integrated conceptual model to secure digitally enabled smart grid towards sustainable energy

An integrated conceptual model to secure digitally enabled smart grid towards sustainable energy

New Approach using Artificial Intelligence-Machine Learning in Demand Side Management of Renewable Energy integrated Smart Grid for Smart City

With the emergence of smart sustainable cities, renewable energy enabled smart grid with active consumer participation is in quest. But to entrap the potential of renewable energy integrated smart grid is strenuous. It has two major correlative power concerns: 1) To synchronize demand-supply and generation scarcity-excess situation with least curtailment 2) Administering variability-uncertainty issues due to renewable energy generation to proportionate adequacy of power supply. Demand-side management (DSM) can be the key solution. An Artificial Intelligence/ Machine learning-based DSM will offer fast responses on a real-time basis to trace consumer consumption patterns, predicting energy forecast, energy market participation, demand response strategies, manage power outages, predicting system loopholes along with sustaining low-cost per unit charges. This paper draws a conclusive summary in tabular form of the thorough review carried out on the latest demand-side management approaches using artificial intelligence/ machine learning to unhide gaps and predict its path for future expansion.

SCAN-CogRSG: Secure Channel Allocation by Dynamic Cluster Switching for Cognitive Radio Enabled Smart Grid Communications

ABSTRACTSmart grid (SG) communication requires multilevel communication among multiple layers. To handle this paramount spectrum requirement, cognitive radio network (CRN) is one of the promising solutions. However, the integration of CRN for SG communications introduces new challenges in spectrum allocation. Still, achieving high spectrum utilization is restricted by security vulnerability and poor algorithm design. This paper resolves all these issues to achieve the objective of spectrum utilization efficiency. A novel dynamic cluster switching approach is designed for secure channel allocation for cognitive radio-based smart grid (SCAN-CogRSG) communications. Strong authentication is enabled for secondary users (SUs) by Binate physically unclonable function (Bi-PUF) authentication mechanism. The key idea behind Bi-PUF mechanism is to prevent spectrum resources from unauthorized access. The formed neighborhood area network (NAN) clusters are dynamically switched to distribute uniform spectrum availability. For dynamic switching, a novel Topology aware fuzzy-reinforcement learning (TA-FuReil) algorithm is presented. For an effectual channel allocation, feature-based K-means (FK-means) algorithm and tri-objective cuttlefish optimization (TriO-CFO) algorithm are proposed. The formulated objectives are interference minimization, power minimization, and data rate maximization. In the case of priority situations, the one-to-many matching-based TriO-CFO (OMTriO-CFO) algorithm is utilized. The proposed SCAN-CogSG is modeled in NS-3 and validated for performance evaluation. The observed results show betterment in throughput, retransmission probability, latency, and authentication time.

Cyber-Physical Power System (CPPS): A Review on Modeling, Simulation, and Analysis With Cyber Security Applications

Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.