Mubarak A. Khan, Md Mofijul Islam, Istiaq Ahmed, Md Masud Karim Rabbi · 10 authors
Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks the technological prowess of blockchain and artificial intelligence (AI) in a novel way to solve long-standing challenges in the distributed energy market, specifically those of security, fraudulent behavior detection, and market reliability. The dataset for this research is comprised of more than 1.2 million anonymized energy transaction records from a simulated peer-to-peer (P2P) energy exchange network emulating real-life blockchain-based American microgrids, including those tested by LO3 Energy and Grid+ Labs. Each record contains detailed fields of transaction identifier, timestamp, energy volume (kWh), transaction type (buy/sell), unit price, prosumer/consumer identifier (hashed for privacy), smart meter readings, geolocation regions, and settlement confirmation status. The dataset also includes system-calculated behavior metrics of transaction rate, variability of energy production, and historical pricing patterns. The system architecture proposed involves the integration of two layers, namely a blockchain layer and artificial intelligence (AI) layer, each playing a unique but complementary function in energy transaction securing and market intelligence improvement. The machine learning models used in this research were specifically chosen for their established high performance in classification tasks, specifically in the identification of energy transaction fraud in decentralized markets. To guarantee the reliability and accuracy of the used machine learning models, an extensive battery of evaluation metrics was utilized. The plot demonstrates clearly that XG-Boost obtained the highest accuracy out of the three models, Random Forest was slightly lower, and conversely, Logistic Regression was the lowest of the three models. Integrating blockchain technology with AI can increase the transparency, security, and efficiency of the energy sector in the U.S. Blockchain's decentralized and immutable ledger can make energy transactions traceable and resistant to tampering, and it becomes extremely hard for malicious actors to manipulate prices or fake records. In the future, the integration of deep learning methodologies and real-time integration of data from the Internet of Things (IoT) holds promising implications for future improvements. Deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can detect strongly nonlinear patterns of fraud, which conventional models may not identify, particularly for the usage of multivariate time-series data from smart meters, sensors, and distributed energy resources.
The traditional centralized management mode has the risks of data tampering and privacy disclosure, and the sharing efficiency is limited. In this paper, an algorithm for safe sharing and intelligent contract optimization of power metering data based on blockchain is proposed to improve the security and efficiency of data sharing. The sharing model includes data generation layer, blockchain network layer, smart contract layer and data access layer, which ensures the data's tamper resistance and privacy protection through distributed account books and encryption technology. The smart contract optimization algorithm adopts a modular design, comprising data validation, access control, and transaction optimization modules. It integrates the Dynamic Proof of Stake (DPoS) consensus mechanism and Hash TimeLocked Contracts (HTLC) for cross-chain protocols, significantly enhancing the execution efficiency and security of smart contracts. Simulation experiments conducted on the Hyperledger Fabric platform show that the proposed algorithm increases transaction throughput (TPS) by 256 % under high load, reduces Gas consumption by 38.6 %, and markedly improves the security of cross-chain transactions. Specifically, the success rate of double-spending attacks is reduced 24-fold, the success rate of data tampering attacks drops to 0.01 %, and the risk of privacy leaks is decreased 75fold compared to traditional schemes.
The exponential growth in power consumption demands a robust method to address and identify irregularities in distribution systems. This paper presents a novel approach integrating advanced machine learning with blockchain technology to enhance microgrid energy systems' anomaly detection and response times. The Isolation Forest algorithm is employed to identify outliers in power consumption. Custom statistical methods, such as Sudden Change Detection and Z-score, detect abrupt changes in power consumption patterns and statistical anomalies. To ensure prompt and automatic responses to identified irregularities, smart contracts are deployed on the Ethereum platform, enabling the instantaneous implementation of corrective measures. The system's real-time capabilities are enabled by the Web3 library, which establishes a direct connection between anomaly detection algorithms and smart contract execution, making the solution viable for practical deployment. The proposed model is demonstrated using a microgrid power consumption dataset, highlighting how smart contracts enable real-time detection and notification of anomalies. Upon identifying irregular power consumption, the smart contract recommends corrective actions, such as initiating load shedding and ensuring timely and transparent intervention. This integration of blockchain technology enhances the accuracy and efficiency of anomaly detection and provides a decentralized and autonomous solution for alerting system operators, reinforcing the security and reliability of microgrid energy systems.
The application of smart contracts in electric power systems is widespread. However, vulnerabilities in smart contracts can cause significant economic losses and require careful attention. Smart contracts in electric power systems have domain-specific characteristics that differ from traditional public blockchain applications. As a result, existing vulnerability detection tools cannot be directly applied to these systems. To address this challenge, we design a vulnerability detection tool called E-Guard specifically for smart contracts in electric power systems. E-Guard uses a tailored intermediate representation (IR) known as EIR, which provides control flow and data flow information more suited to the business logic of electric power systems than traditional static analysis tools. We identify and summarize three types of vulnerabilities unique to electric power systems based on expert knowledge. Experimental results show that E-Guard significantly outperforms traditional static analysis tools in detecting these three types of vulnerabilities. Additionally, the extra overhead generated by using EIR is minimal and negligible. This demonstrates that E-Guard is an effective and efficient tool for enhancing the security of smart contracts in electric power systems.
Smart grid technology is an amicable improvement of the conventional power grid characterized by improved communication, control, and computing technologies that enhance improved energy distribution. A smart grid is an improvement on the existing electric grid system that allow for more intelligent controlling of electricity from the generation point right down to the consumer. Consequently, the ICS (Industrial Control Systems) of smart grids have become more exposed to cyber risks resulting from enhanced network integration and digitalization. The complexity of smart grids with many DERs (Distributed Energy Resources), sensors, and systems make the security problem challenging. Reasons why decentralised smart grids have to be secure and more resilient mean that new approaches that can enable detection, prevention, and mitigations of cyber-attacks are desirable. Deep learning and smart grid cybersecurity based on decentralization has a bright outlook as it enables improving the detection of anomaly cases and potential threats and increasing the general level of resilience of the grid.Smart grids are a major evolution of conventional power grids that employ ICT (Information and Communication Technology) to optimize the delivery of electrical energy. Elements of smart grid include smart meters for consumers, automated distribution network, and communication network. Smart grids can be decentralised as it includes multiple DERs like solar power, wind mills or energy storage systems, that are usually integrated at the outskirts of the smart grid. Such decentralization adds more challenges to the grid's physical structure and also adds more vectors by which a cyber-threat can penetrate the network [1]. As such, cybersecurity emerged as a focal topic to protect the safe and reliable functioning of smart grids. It has also shown a commanding success in several contexts, which is due to the deep learning's inclusion capabilities of key features from accesses data [2]. A major advantage of deep learning is that models are able to detect the abnormal flow of traffic since they hold knowledge of normal traffic flow patterns [3]. Pattern recognition is another important factor; deep learning networks can recognize even complex pattern in a given data. However, deep learning models include scalability hence making them capable of analyzing a large quantity of data produced by, for instance, smart grids [4].The decentralized smart grids are the most vulnerable because of the localized architecture and large connections with IoT gadgets. The different systems that are used in smart girds are not homogenous and have different protocols and hence the weakness are provided [5]. Lack of computational capacity of many IoT devices due to resource constraints precludes such approaches and traditional security techniques cannot be implemented [6].Despite the potential of deep learning in smart grid cybersecurity, there are a number of issues before it. Concerning a few key points, it is important to mention data confidentiality as the training data can be considered sensitive. Another, there is an interpretability problem since, unlike traditional machine learning techniques, deep learning's models are considered 'black box' [7]. Another limitation of deep learning is that it demands massive computation, which may well not be readily feasible in low-power devices of smart grid [8]. There is also a big issue related to integration with legacy systems because such systems might be incompatible with deep learning solutions [9].Future research directions encompass the development of Smart grids which are decentralized and also experience a higher level of risk with relation to cybersecurity because of the deployment of several IoT devices. Table 1 summarizes robust deep learning approaches for smart grid cybersecurity, highlighting their advantages, challenges, and future directions. While these methods show high accuracy in detecting cyber threats (ranging from 92% to 99.5%), they face issues like high computational demands, vulnerability to adversarial attacks, and scalability concerns. Future research focuses on improving real-time integration, enhancing model interpretability, and developing more robust AI-driven cybersecurity frameworks.The most suitable solution for smart grid cybersecurity protection combines Federated Learning with Blockchain and Adversarial Deep Learning. With FL the grid nodes can participate in decentralized training processes without exchanging actual data which protects their information security and privacy. The combination of Blockchain technology and adversarial training creates an unalterable security framework which secures communication while building resistance against complex cyber threats. The implementation of this combined method becomes necessary because smart grids function through decentralized systems that connect many vulnerable IoT-enabled energy production networks to cyber security threats. Maximum security models become ineffective because they suffer from dimensional problems alongside privacy weaknesses and developing electronic strike threats. Through an integration of FL and Blockchain technology organizations achieve real-time threat detection with adaptive capabilities and lower IT overhead costs. Industrial security in the energy sector needs sophisticated AI-enabled solutions which must scale effectively to defend against infrastructure attacks and disruption of power supply. By utilizing this model organizations maintain autonomous cybersecurity operations which produce efficient proactive threat protection suitable for advanced smart grid systems against developing cyber attacks. Deep learning in the decentralised smart grid cybersecurity is a revolutionary way of handling the huge and dynamic risks. Based on the real-time data processing characteristic and the ability to recognize patterns of deep learning models, it is possible to improve the density of anomaly detection, threats' prediction, and systems' robustness. However, there are challenges that the use of deep learning in this area holds among them the fact that it calls for usage of a lot of computational power, data security issues, and the issues related with initiation and incorporation of integration of such complex technologies in the existing systems. To overcome these challenges new approaches, need to be created more efficiently, focus on to build effective privacy preservations, and integrate with other existing systems. For future work, the focus should be made on introducing new sophisticated and flexible frameworks of deep learning for the smart grid that will function in the given distributed environment. In this way, the industry can progress and advance towards building a smarter grid, that can address novel cyber threats and protect and enhance the reliability of the energy distribution systems.
Smart contracts on blockchain networks autonomously execute applications based on predefined conditions, making their security-critical due to the potential for significant financial losses from vulnerabilities. Current vulnerability detection algorithms commonly rely on expert-defined rules, which are prone to errors and insufficient for identifying complex vulnerability patterns . Given the immutability of smart contracts post-deployment, ensuring security before deployment is essential. This research presents Block-wise Abstract Syntax Tree based Federated Graph Neural Networks (BAST-FeGNN), a novel approach combining block-wise abstract syntax tree and Federated Graph Neural Networks (FeGNN) to detect code clones and multiclass vulnerabilities in Ethereum smart contracts. The BAST-FeGNN method operates in three stages: it first extracts security-related patterns from the base code using an abstract syntax tree; then, it constructs and normalizes a contract graph using FeGNN to capture critical nodes, analyze data and control flows. This integration of graph-based feature extraction with pattern matching allows precise detection of vulnerabilities like access control issues, reentrancy, and unchecked calls, as well as identifying code clones. Finally, the method pools these features for comprehensive vulnerability detection . BAST-FeGNN significantly enhances vulnerability detection accuracy and scalability, outperforming existing models with an accuracy of 95.35%, recall of 95.58%, F1-score of 95.80%, and precision of 96.10%, making it a robust solution for securing blockchain applications.
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
Cryptocurrency has transformed finance and investment, with platforms like Uniswap facilitating billions of dollars in trades. However, malicious smart contracts and scam tokens have led to significant financial losses for decentralized finance (DeFi) users. Code analysis alone cannot detect rug pulls using social engineering tactics. To address this issue, machine learning algorithms can leverage the vast amount of transactional data stored on the blockchain, particularly time series data, to identify scam tokens. This study aims to determine the optimal timeframe for detecting rug pulls and highlights the importance of token volume and transaction count features. The findings suggest that shorter timeframes are sufficient for detecting rug pull tokens since most incidents occur soon after token creation. This research offers new insights into scam token classification and prevention and contributes to a broader understanding of this field. • Rug pull detection in Uniswap V3 is researched via on-chain indicators over time. • Many rug pulls occur during the first day after token creation. • Time windows close to rug pull events significantly influence the model's predictions.
The incorporation of blockchain technology into smart grid operations has attracted considerable attention owing to its decentralized, immutable, and transparent characteristics. This study examines the use of blockchain in the IEEE 14 bus system, illustrating its capacity to improve security, transparency, and resistance against cyber threats. The suggested system utilizes blockchain to guarantee safe data flow among nodes, including power generators, loads, and control centers. Every transaction related to power flow data, control signals, or market operations is recorded on an immutable ledger. The decentralized architecture of blockchain allows nodes to independently validate transactions through consensus processes such as Proof of Work (PoW) or Proof of Stake (PoS). This decentralization removes a singular point of failure, hence diminishing susceptibility to cyber-attacks aimed against centralized systems. Data integrity is safeguarded using cryptographic hash functions, which promptly identify any attempts to modify the stored information. The blockchain consensus system guarantees the recording of only valid transactions, hence enhancing security. Mathematical models for power flow analysis are incorporated into the blockchain framework, guaranteeing precise computations of voltage magnitudes, phase angles, and power transfers among buses. Simulations of the IEEE 14 bus system demonstrate enhanced system security and resilience, particularly against False Data Injection Attacks (FDIAs). The findings underscore how blockchain enhances overall reliability and mitigates the danger of unwanted data alteration. Blockchain enhances transparency and decentralization, providing a promising approach for guaranteeing the future of smart grid operations.
Yunusa Ishaq, Ajuru Success Prince, Gbah Gonto Jean Claude, Togola Molobaly Di Bebe
Abstract: The transition to smart grids has revolutionized energy distribution, enabling more efficient and flexible power management through advanced communication and control systems. However, this interconnected structure makes smart grids vulnerable to cyberattacks, such as False Data Injection Attacks (FDIA), Distributed Denial of Service (DDoS) attacks, and data manipulation. These threats undermine the stability and reliability of the grid, and existing centralized security frameworks are often ill-equipped to address them due to their susceptibility to single points of failure and limited scalability. To overcome these challenges, this paper introduces a decentralized security framework that combines blockchain technology with machine learning (ML). The framework leverages blockchain to provide a transparent, immutable, and decentralized ledger, employing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA) to ensure secure data validation. Alongside this, ML models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), are used to detect anomalies in time-series data, such as FDIA, with high precision. Smart contracts embedded in the blockchain enable automated, real-time responses to threats, such as isolating compromised nodes or rerouting energy flows to maintain grid stability. Through simulations replicating real-world cyberattacks, the proposed framework demonstrated over 95% detection accuracy, a 30% reduction in response times, and enhanced computational efficiency with lower energy consumption. These results affirm the effectiveness of the framework as a scalable and resilient solution for modern smart grid security.
Oghenerume Augoye, Adekunle Adewoyin, Olugbenga Adediwin, Audu Joseph Audu
Artificial Intelligence (AI) is transforming energy financing by enhancing decision-making, optimizing investment portfolios, and improving risk assessment in sustainable infrastructure projects. This review explores the role of AI in energy financing, focusing on its applications in risk evaluation, credit scoring, investment optimization, and the development of climate-aligned financial strategies. AI-driven predictive analytics enable investors to assess the financial viability of renewable energy projects, identify high-impact opportunities, and optimize asset allocation. Additionally, AI-powered models enhance credit scoring for energy developers, facilitating access to funding for clean energy initiatives. The integration of AI with blockchain and smart contracts is also revolutionizing energy financing by ensuring transparency, reducing fraud, and automating financial transactions in sustainable projects. Furthermore, AI plays a crucial role in the management and monitoring of green bonds, improving impact assessment and ensuring accountability in climate finance. However, several challenges hinder AI-driven energy financing, including data limitations, regulatory gaps, cybersecurity risks, and potential biases in AI decision-making models. Ensuring data quality, developing ethical AI frameworks, and addressing cybersecurity concerns are essential for AI’s successful adoption in energy investment strategies. Future opportunities lie in AI-driven predictive analytics for emerging markets, enabling better financing mechanisms for off-grid and decentralized energy solutions. AI can also enhance public-private partnerships by optimizing investment structures and improving government funding allocation for renewable energy projects. As AI continues to evolve, it holds the potential to reshape energy financing, drive sustainable investments, and accelerate the transition to a low-carbon economy. This review underscores the need for collaborative efforts among policymakers, financial institutions, and technology providers to maximize AI’s potential in sustainable energy infrastructure financing while addressing its inherent challenges.
Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie · 6 authors
With the rapid development of blockchain technology, fraudulent activities have significantly increased, posing a major threat to the personal assets of blockchain users. The blockchain transaction network formed during user transactions can be represented as a graph consisting of nodes and edges, making it suitable for a graph data structure. Fraudulent nodes in the transaction network are referred to as anomalous nodes. In recent years, the mainstream method for detecting anomalous nodes in graphs has been the use of graph data mining techniques. However, anomalous nodes typically constitute only a small portion of the transaction network, known as the minority class, while the majority of nodes are normal nodes, referred to as the majority class. This discrepancy in sample sizes results in class imbalance data, where models tend to overfit the features of the majority class and neglect those of the minority class. This issue presents significant challenges for traditional graph data mining techniques. In this paper, we propose a novel graph neural network method to overcome class imbalance issues by improving the Graph Attention Network (GAT) and incorporating ensemble learning concepts. Our method combines GAT with a subtree attention mechanism and two ensemble learning methods: Bootstrap Aggregating (Bagging) and Categorical Boosting (CAT), called SGAT-BC. We conducted experiments on four real-world blockchain transaction datasets, and the results demonstrate that SGAT-BC outperforms existing baseline models.
Maintaining system reliability is one of the foremost concerns of any transmission sector utility. Certain constraints in the transmission system elements e.g. lack of N-1 redundancy in power system elements, constraints in loading capabilities of transmission lines, transformers, etc. may lead to undesired system conditions where the stability of a wide area of the transmission system network may be at stake. The modern IEC 61850 compliant IEDs (Intelligent Electronic Devices) can perform fast, accurate and extensive logical decisions with respect to prevailing system conditions. The capability of the IEDs can be extensively used to design wide area SPS (System Protection Schemes) to protect and preserve the reliability, security, and integrity of the grid during abnormal system conditions. The following paper describes the designing, testing and implementation of various SPS in the Assam power system network which have proved to be successful in preserving the stability of the grid. The SPS has been designed utilizing the present protection and communication system of AEGCL (Assam Electricity Grid Corporation Limited) with a primary focus on reduction in additional hard wirings, reduction in additional cost involvement, efficient use of IEC 61850 features of the IEDs and designing a fail proof scheme with efficient control and monitoring features
The integration of blockchain technology into decen-tralized energy trading presents a promising solution to current challenges in energy markets. By leveraging Layer 2 blockchain solutions, such as sidechains and rollups, the scalability, cost-effectiveness and transaction throughput of energy trading plat-forms can be significantly enhanced. This paper focuses on the implementation of smart contracts for energy trading, comparing transaction costs and scalability between Layer 1 and Layer 2 networks. Layer 2 network employs smart contracts to match orders of buyers to that of sellers. The results demonstrate that Layer 2 solutions drastically reduce costs and improve efficiency, making them more feasible for real-world applications. Moreover, this study highlights the potential of using advanced smart contracts and algorithms to further enhance decision-making in energy trades. Future work will focus on optimizing these Layer 2 solutions to handle complex trading scenarios and integrating them into smart grids to create a more sustainable and efficient energy market.
• The article highlights the significance of smart grids in managing energy demand, addressing environmental issues, and ensuring energy security, emphasizing the need for blockchain technology and AI. • The study discusses data collection from smart city power consumption in a smart grid, utilizing techniques like Z-Score normalization and Spatial Temporal Correlation, and suggests blockchain technology for secure data transmission and storage. • The paper proposes a LSTM-RNN-ISSA for improved load forecasting accuracy and discusses the use of Blockchain-Based Smart Energy trading for effective communication in the smart grid. • Task-Oriented Communication facilitates real-time demand response, balancing electrical load and supply, outperforming existing approaches in smart city energy management. A smart grid (SG) is the financial benefit of a complicated and smart power system that can keep up with rising demand. It has to do with saving energy and being environmentally friendly. Growing populations and new technologies have caused a big rise in energy use, causing big problems for the environment and energy security. It is essential and significant to use blockchain technology and artificial intelligence (AI) to solve problems with power control. Data can be collected using a smart city in a power-consumed smart grid data and pre-process using a Z-Score normalization technique. It can extract features using a Spatial-Temporal Correlation (STC) to assess smart grid power usage within the context of a smart city using large-scale, high-dimensional data. Ensuring data integrity, privacy, and trust among grid applicants, transmit the data securely and reliably to a centralized or distributed cloud platform utilizing blockchain technology—a secure transmission and storage using Distributed Authentication and Authorization (DAA) protocol. To achieve precise load forecasting, a short-term recurrent neural network with an improved sparrow search algorithm (LSTM-RNN-ISSA) is incorporated. The smart grid may then record the projected results. Communication can be done on a smart grid with the users; the Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization (BSET-AVVO) algorithm can be used for effective communication—a quick balancing electrical load and supply via a task-oriented communication mechanism in real-time demand response. Finally, our proposed method performs successfully better than the existing approaches.
Zalina Fatima Azzahra, Nur Ichsan Utama, Sinung Suakanto, Toni Dwi Setiawan
In distributing crude oil from production wells to oil processing units, the pipeline network is considered the primary means of transporting petroleum products. However, in the process of distributing oil using pipes, several problems are currently still found, namely the discrepancy in the amount of oil sent from gas stations with the amount of oil arriving at oil processing refineries, the falsification of data and the occurrence of oil theft through pipes due to the absence of monitoring along the pipeline route. Pipeline monitoring is complex because pipelines are often located in remote areas and are difficult to reach. Therefore, to overcome this problem, this research proposes a solution by creating a prototype monitoring system to monitor oil distribution through pipes using blockchain technology and supported by sensor devices installed on pipes. This research was developed using Ethereum as a blockchain and the solidity programming language and a private blockchain network to maintain the privacy and security of oil distribution data through pipelines by data records from sensors along the pipe. Based on the results of designing a private blockchain-based oil distribution monitoring system through pipelines that have been proposed in this research, it is hoped that this system can help overcome problems that occur in the oil distribution process through pipelines and can help make it easier for parties to record data and monitor oil distribution in Indonesia.
Gary Hahn, Emilio C. Piesciorovsky, Raymond Borges Hink, Aaron Werth
• Faulted phases detection using backup method that is external to protective relay. • Validates integrity of input data using distributed ledger technology. • Tested using simulated faults in a realistic electrical substation test bed. Modern electrical grids have intelligent electronic devices (IEDs) such as protective relays that use internal logic to detect the types of electrical faults. The increasing integration of distributed energy sources and the resulting complexity of electrical grid communication architectures necessitates enhanced robustness of IEDs’ monitoring while maintaining security against potential cyber threats. In this study, a backup electrical faulted phase detection method with a distributed ledger technology (DLT) platform was implemented. Cyber Grid Guard software was developed to collect phase currents and voltages transmitted through IEC 61850 GOOSE messages, detect faulted phases from the IEDs using the GOOSE data, and validate the data by hashing them and storing them in the distributed ledger. In this way, the hashed data were run into an electrical faulted phase algorithm based on using a current threshold for detecting the faulted phases in the medium-voltage main feeder of an electrical substation. The detection of the electrical faulted phases was assessed in a real-time simulator with protective relays, meters, the software framework, and DLT in the loop. The proposed method provides secure and reliable backup detection external to the IEDs, and DLT validation enhances system security and trust.
Information and Communication Technology (ICT) provides customers with utilities and smart grid solutions, enabling enhanced monitoring and control of energy management systems. This technology is poised to elevate the reliability, sustainability, and efficiency of future electric grids through the implementation of advanced metering infrastructure (AMI). However, current Supervisory Control and Data Acquisition (SCADA) systems lack trusted machine authentication in smart grid communications, leaving the electric grid vulnerable to cyberattacks via sophisticated network technologies such as wireless access points, sensors, routers, and gateways. Therefore, ensuring proper management of data integrity from field sensors is crucial to enhance the reliability of SCADA systems. In this context, the utilization of quantum key distribution (QKD) key pairs is proposed to uphold integrity in smart grid communications. This paper presents a fibre optic blockchain network designed to manage and utilize cryptographic keys, facilitating the authentication of peer-to-peer (P2P) communications in SCADA systems. This demonstration underscores the feasibility of employing QKD and blockchain to further strengthen the integrity and authentication of smart grid communications. Additionally, this paper delves into discussing the performance metrics and overhead expenses of the proposed scheme in comparison with existing state-of-the-art proposals. Simulation results highlight the significant impact of blockchain size on the system setup’s throughput and latency.
Uttam Ghosh, Laurent Njilla, Danda B. Rawat, Charles Kamhoua
The demand for electricity continues to increase significantly as the industrial age progresses. The concept of a smart grid was introduced to guarantee effective electricity distribution, supply security, minimum losses, and high levels of quality in the power system. The effective integration of green and renewable energy technology has been proposed as a new vision for the conventional power system to reduce its carbon footprint, environmentally friendly energy mix, and foster sustainable economic development. To achieve these, the smart grid combines advanced technologies, data analytics, smart devices, and communication systems. However, the traditional centralized grid system faces critical challenges to effectively manage the smart devices, maintain security, and stability of the system. As a result, the smart grid is leading to a shift from a centralized topology to a more decentralized and distributed model. On the other hand, blockchain is a decentralized and distributed ledger technology that holds significant potential as an intriguing application within the smart grid paradigm. In this chapter, we aim to provide an overview of blockchain technology, and a number of recent research works presented in different literatures on blockchain integration into smart grid systems for energy management, energy trading, security and privacy, microgrid management, and electric vehicle management. Finally, we present the limitations and future direction of applying blockchain in smart grids. Index Terms -Blockchain, smart grids, cyber-physical system, electric vehicles, and security and privacy
The integration of smart contracts within water distribution networks presents a transformative approach to addressing challenges in water management. In this context, we propose a pioneering tool aimed at streamlining the design and implementation of smart contracts tailored specifically to smart water distribution networks. This tool allows stakeholders to input essential parameters such as water sources, distribution points, consumption patterns, and contractual stipulations. Through the utilization of predefined templates and adaptable contract logic, the tool automates critical processes including water allocation, usage monitoring, and penalty imposition based on predefined criteria. Furthermore, seamless integration with blockchain technology ensures the security and integrity of contract execution. By addressing scalability, compliance, and regulatory considerations, this tool represents a significant advancement in empowering stakeholders to optimize water management practices through the deployment of efficient and transparent smart contracts.
In the backdrop of advancing communication technology and the adoption of decarbonization initiatives, peer-to-peer (P2P) electricity trading has evolved into a consequential avenue for the reliable utilization of clean energy resources. Most efforts have focused on the design of P2P distributed mechanisms to ensure that the security constraints of the grid can be adhered to. However, ensuring the assurance of the correct operation of the distributed mechanisms is also essential but has received less attention. A common assumption is that all participants in the P2P market are honest and make reasonable bids at market prices. Such an assumption could be risky because the P2P market clearing process relies on a coordination process of market participants and the clearing outcome of the P2P market is susceptible to manipulation by dishonest participants. In this work, we propose a new architecture for the P2P market by adding a verification layer based on zero-knowledge proof technology to identify dishonest bidding information of market participants without collecting their private cost information. In addition, we introduce an asynchronous market mechanism, which can greatly guide the P2P market clearing results in a dishonest environment to be close to the theoretical optimal results. Case studies demonstrate the advantages of our approach in resisting dishonesty, preserving privacy, and enhancing market robustness, which can help build a more credible and resilient P2P market environment.