Manuel Uche-Soria, Antonio Martínez Raya, Alberto Muñoz Cabanes, Jorge Moya Velasco
Rapid depletion of fossil fuel reserves forces the global energy sector to transition to sustainable energy sources. Specifically, distributed energy markets have emerged in the renewable energy sector in recent years, partly because blockchain technology is becoming a successful way to promote secure and transparent transactions. Using its decentralized structure, transparency, and even pseudonymity, blockchain is increasingly adopted worldwide for large-scale energy trading, peer-to-peer exchanges, project financing, supply chain management, and asset tracking. The research comprehensively analyzes blockchain applications across multiple fields related to energy, bibliographically evaluating their transformative potential. In addition, the study explores the architecture of various blockchain systems, assesses critical security and privacy challenges, and discusses how blockchain can enhance operational efficiency, transparency, and reliability in the energy sector. The paper’s findings provide a roadmap for future developments and the strategic adoption of blockchain technologies in the evolving energy landscape for an effective energy transition.
Waqas Amin, Qi Huang, Jianping Li, Abdullah Aman Khan · 6 authors
An increase in the popularity of peer-to-peer energy trading in smart grids due to the massive integration of renewable energy sources demands effective and competitive pricing and energy allocation policies to ensure fairness within the market framework. Considering the scalability issues, technical complexity, and operational costs of distributed ledger technology such as blockchain, the reputation of the participants becomes a prominent factor to ensure trustworthiness, reduce risk, and increase market efficiency. This paper proposes a novel method to determine the reputation of participants within the energy market. Based on the evaluated reputation of the participants, an effective pricing method along with an energy distribution technique is devised by considering several market dynamics that significantly affect the pricing and energy allocation method. Extensive experiments have been conducted to validate the effectiveness of the proposed model. The results demonstrate that through the proposed model, the energy bills of the buyers can be reduced by 44%. This highlights the tangible benefits and practical applicability of the proposed approach in optimizing energy costs for consumers in the P2P energy trading ecosystem.
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.
Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu
This study proposes the integration of a hybrid Proof of Work/Proof of Stake (PoW/PoS) consensus mechanism with a Long Short-Term Memory (LSTM) model for anomaly detection in blockchain-based microgrids. The hybrid PoW/PoS model is designed to address common issues in blockchain systems, such as 51% attacks, double-spending, and high energy consumption, by optimizing energy usage and enhancing security. Simulation results show that the system can process transactions with an average throughput of 37.25 transactions per second (TPS), an average latency of 26.84 milliseconds per transaction (ms/tx), and extremely efficient energy consumption per transaction (0.01 kWh/tx). The LSTM model applied for anomaly detection achieves an 89.10% detection rate, a 0.00% false positive rate, and a 0.12 s recovery time, indicating the system's reliability in facing attacks. The hybrid PoW/PoS system demonstrates advantages in both energy efficiency and resilience to attacks compared to individual PoW and PoS systems. This research contributes to the development of safer, more efficient, and scalable blockchain-based microgrids by integrating Artificial Intelligence (AI) to strengthen the system against anomalies and threats.
M. Zulfiqar, Muhammad Babar Rasheed, Daniel Rodríguez, María D. R‐Moreno
Contemporary power grid systems increasingly rely on sophisticated energy trading mechanisms to optimize resource allocation and operational performance. While prior studies have examined the coordination roles of energy intermediaries and utility operators, particularly through distributed ledger technologies that ensure data provenance and transaction verifiability in decentralized energy marketplaces, significant security vulnerabilities persist. Notably, fraudulent practices by energy suppliers characterized by payment collection without corresponding energy delivery pose substantial risks to market integrity and participant confidence. This research presents the Blockchain-based Energy Trading with Multi-Factor Trust Framework (BC-ET-MF), a novel architecture that addresses critical security deficiencies through advanced cryptographic protocols and consensus mechanisms. The framework utilizes anonymous credential systems to safeguard participant privacy while implementing time-locked commitment schemes that ensure transaction fairness and verifiability. The architecture incorporates granular access control mechanisms for secure service orchestration and establishes a consortium blockchain infrastructure among energy intermediaries to facilitate distributed transaction validation and immutable record-keeping. To mitigate computational overhead associated with conventional consensus algorithms, we introduce a Proof-of-Verifiability protocol that dynamically calibrates to real-time energy production and consumption patterns. This adaptive mechanism reduces system resource requirements while maintaining security guarantees. Experimental evaluation demonstrates that BC-ET-MF achieves substantial performance improvements: energy consumption reduction of 43.0 %, peak-to-average ratio optimization from 8.27 to 3.21 and 5.88 under 25 % and 50 % demand reduction scenarios respectively, and establishment of 92.5 % participant trust levels. The framework additionally yields 37.6 % transaction latency reduction while preserving user anonymity and enabling comprehensive audit capabilities, thus establishing a secure, efficient, and trustworthy energy trading ecosystem.
Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 61% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6x faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.
Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids.
Alper Alimoğlu, Kamil Erdayandı, Mustafa Mustafa, Ümit Cali
This paper proposes a new decentralized framework, named EDGChain-E (Encrypted-Data-Git Chain for Energy), designed to manage version-controlled, encrypted energy data using blockchain and the InterPlanetary File System. The framework incorporates a Decentralized Autonomous Organization (DAO) to orchestrate collaborative data governance across the lifecycle of energy research and operations, such as smart grid monitoring, demand forecasting, and peer-to-peer energy trading. In EDGChain-E, initial commits capture the full encrypted datasets-such as smart meter readings or grid telemetry-while subsequent updates are tracked as encrypted Git patches, ensuring integrity, traceability, and privacy. This versioning mechanism supports secure collaboration across multiple stakeholders (e.g., utilities, researchers, regulators) without compromising sensitive or regulated information. We highlight the framework's capability to maintain FAIR-compliant (Findable, Accessible, Interoperable, Reusable) provenance of encrypted data. By embedding hash-based content identifiers in Merkle trees, the system enables transparent, auditable, and immutable tracking of data changes, thereby supporting reproducibility and trust in decentralized energy applications.
The rapid advancement of smart grids, propelled by the integration of distributed energy resources (DERs) and renewable energy technologies, has exposed key challenges such as interoperability, standardization, and data security. These issues impede the efficient operation and scalability of smart grid applications, particularly in enabling decentralized, real-time energy trading and demand response management. Effective management of DERs and demand response systems relies heavily on seamless information exchange, data-driven decision-making, and robust digital communication frameworks to maintain system stability and operational efficiency. To address these challenges, this study presents the Blockchain Consortium-Based Demand Energy Trading System (BC-DETS), a blockchain-powered framework designed to enhance interoperability, security, and standardized energy trading mechanisms within smart grid ecosystems. Comprehensive simulations have validated the effectiveness of BC-DETS using Key Performance Indicators (KPIs) such as transaction latency, demand response participation rate, operational cost reductions, and overall grid efficiency under diverse scenarios. Findings reveal a 35% boost in grid efficiency through optimized energy distribution and minimized energy losses, alongside a 15% decrease in operational costs due to reduced transaction overhead and improved energy allocation. Moreover, demand response participation rates increased by 40%, facilitated by secure and transparent blockchain-enabled real-time energy transactions. These numerical findings underscore blockchain’s transformative potential in enhancing the scalability, security, and inclusivity of smart grids, establishing a foundational platform for future advancements in blockchain standardization and sustainable energy management solutions.
Data integrity in Smart Grids (SG) systems can be vulnerable with the implementation of the novel Community Blockchain-Driven Traceability Framework (CBDTF). It enhances Detection Rates (DR), maintains low End-to-End Delay (EED), and uses less energy by using distributed ledger technology and community-based validation. This model deployed a Delegated Proof of Stake (DPoS) consensus mechanism and community-driven testing, resulting in an average Detection Rate (DR) of 98.7% for Data Tampering attacks and a False Positive Rate (FPR) of 1.78%. It outperforms conventional Blockchain (BC) solutions with an EED of 120.8 ms and an average CPU utilization of 1,113 tx/kWh. When compared with conventional Proof-of-Work (PoW), CBDTF requires 60% less energy while proving 96.2% consensus resilience against distinct attacks. Applying real-world SG data collected by a distributed network of 100 nodes, the accuracy of this model was tested. The present study makes a valuable contribution to the field by signifying how BC platforms driven by the public can address SG's data security issues while maintaining the accuracy of real-time operations.
Mohammad Nasrinasrabadi, Maryam A. Hejazi, Ehsan Chaharmahali, Mousa Hussein
The integration of blockchain and the Internet of Things (IoT) within smart grids offers transformative potential for enhancing energy management, security, and operational efficiency. Smart grids rely on advanced digital technologies to enable bidirectional communication between energy producers and consumers, optimizing the integration of renewable energy sources and promoting demand-side management. Blockchain technology, with its decentralized and immutable nature, ensures secure and transparent energy transactions while fostering trust without the need for centralized authorities. Meanwhile, IoT facilitates real-time data collection and monitoring, enabling dynamic energy management and transactive energy systems. This review explores the synergies between blockchain and IoT in addressing critical challenges such as cybersecurity, data security, and network security. By leveraging mechanisms like smart contracts and consensus algorithms, these technologies enhance grid resilience and privacy, providing robust solutions to manage distributed energy resources and decentralized energy markets. The integration also supports peer-to-peer energy trading, improves scalability, and reduces reliance on intermediaries, aligning with sustainability goals by promoting renewable energy adoption. Despite significant advancements, challenges such as regulatory barriers, high computational costs, and scalability limitations persist. This paper emphasizes the need for innovative approaches to overcome these issues and highlights emerging trends such as hybrid blockchain models and AI-enabled solutions. By addressing these gaps, blockchain and IoT can redefine smart grid infrastructures, ensuring a secure, efficient, and sustainable energy future.
This study delves into the vulnerability of the smart grid to infiltration by hackers and proposes methods to safeguard it by leveraging blockchain and artificial intelligence (AI). A categorization and analysis of cyberattacks against smart grids will be conducted, focusing on those targeting their communication layers. The main goal of the work is to address the challenges in this area by implementing novel detection and defense strategies. The authors categorize attacks on smart grid networks based on the communication classes they want to compromise. They propose novel taxonomies specifically designed to detect and implement defense strategies. The study investigates artificial intelligence and blockchain techniques to identify cyber-attacks that employ deceptive data injection. The study indicates that cyberattacks against smart grids are increasing in frequency and complexity. The paper proposes innovative strategies for defense, such as enhancing cybersecurity with artificial intelligence and blockchain technology. The research further enumerates several challenges, such as counterfeit topological data, imprecise data identification, and combining big data with blockchain technology. Given the increasing risks, the study emphasizes the crucial need for robust cybersecurity safeguards in smart grids. This work contributes to the protection of smart grid infrastructures by categorizing attacks, suggesting novel defenses, and exploring solutions integrating artificial intelligence and blockchain technology. Research should prioritize enhancing technology to maximize security and counter emerging attack methods. The intended audience of our paper comprises graduate-level academics and independent researchers.
With the shifting from traditional grids to smart grids, there is an immense shift towards decentralized energy trading wherein “prosumers” can enter peer-to-peer transactions. This model decreases dependence on centralized utilities and maximizes efficient, flexible, and resilient energy distribution. It enhances transparency and trust by automating and securing trades via smart contracts. No intermediaries are required; hence transaction costs are low. However, an attack that would breach the security guarantees of blockchain systems-by tremendous quantum computers-might break a few of the older cryptographic methods or even reveal very significant portions of their keys. This paper introduces a blockchain-based decentralized framework for energy trading in smart grids, with a strong emphasis on post-quantum cryptography to safeguard transactions against quantum threats. We explore post-quantum cryptographic techniques, particularly lattice-based algorithms due to its compact signature sizes and strong security capability for the future-proof blockchain enabled smart grids. The proposed system model ensures secure and decentralized energy trading while incorporating off-chain signature validation to enhance computational efficiency. Unlike previous studies that primarily focus on market structure or consensus protocols, this work introduces a quantum-resilient architecture with an off-chain transaction validation mechanism, enabling high-throughput trading secured against future cryptographic vulnerabilities. The novel feature of the proposed model is the integration of off-chain post-quantum cryptographic verification into a blockchain energy trading architecture that is practically deployable on embedded hardware. Compared to existing solutions, the proposed method ensures quantum-resilient authentication while reducing gas costs by 40% and improving computational efficiency achieving a signing time of 0.327 ms and verification time of 0.127 ms. The proposed framework represents a significant step toward future-proofing blockchain-enabled smart grids while maintaining performance, transparency, and resilience. The outcome of this research work presents a quantum-safe solution that strengthens the resilience of smart grid operations and ensures the security of decentralized energy trading.
Joan Ferré-Queralt, Jordi Castellà‐Roca, Alexandre Viejo
Smart grid technology has transformed electricity generation , distribution, and consumption by incorporating advanced communication systems and distributed energy resources , including solar panels and energy storage solutions . This integration enables prosumers to actively participate in energy markets, benefiting from real-time monitoring, dynamic pricing , and load balancing. However, the detailed data collected during these processes raise significant privacy concerns, as it may expose sensitive information about users’ lifestyles. This work presents an innovative energy trading system operating within decentralized energy distribution networks . The system leverages blockchain-based hierarchical smart contracts to enhance privacy protection for users. It automates energy trades, ensures accurate transaction verification, and obscures user identities and energy consumption patterns through its hierarchical structure, preventing unauthorized profiling or data breaches. Additionally, mechanisms to detect and penalize dishonest behavior are incorporated, ensuring the integrity and fairness of the energy market. The feasibility of the proposed system is experimentally evaluated in an IoT environment through a small-scale implementation using actual IoT devices, yielding positive results in terms of scalability and privacy features. Lastly, a comparative analysis is presented to demonstrate the advantages of the proposed system over existing state-of-the-art solutions.
In an era where the intersection of artificial intelligence (AI) and energy finance drives critical infrastructure decision-making, designing resilient AI architectures has become imperative.Predictive energy finance systems-spanning investment forecasting, carbon pricing, and grid demand-supply modeling-face mounting complexity due to shifting policy landscapes, data sovereignty regulations, and the escalating risk of adversarial threats.This paper presents a multidisciplinary framework for constructing AI architectures that maintain operational integrity, adaptability, and security in volatile environments.At a macro level, the study outlines the integration of federated learning, edge analytics, and privacy-preserving AI techniques to ensure compliance with crossborder data governance regimes while enabling decentralized energy financial modeling.It further examines adversarial machine learning risks-such as data poisoning and model inversion-that compromise predictive validity in high-stakes financial applications.Through threat modeling and robust training paradigms, the architecture includes defense-in-depth strategies like adversarial regularization, ensemble resilience, and real-time anomaly detection.The paper also analyzes the effects of dynamic policy shiftssuch as carbon credit revaluation and renewable energy subsidies-on model reliability and system adaptation.A scenario-based approach illustrates how the proposed architecture adjusts to policy-induced discontinuities through modular retraining, real-time policy rule parsing, and simulation-informed decision loops.Case studies from green energy bonds, smart grid investment portfolios, and climate-linked derivatives are used to validate the architectural robustness under varying policy, regulatory, and cyber conditions.Ultimately, this work provides a systems-engineered blueprint for resilient AI in predictive energy finance, enabling trustworthy, secure, and sovereign-compliant deployment.
Pierre Sedi Nzakuna, Vincenzo Paciello, A. Lay-Ekuakille, Angelo Kuti Lusala · 6 authors
The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol-a leaderless, feeless DAG designed for the IoT-with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT.
Ahmad M. Almasabi, Ahmad B. Alkhodre, Maher Khemakhem, Fathy Eassa · 6 authors
IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.
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.
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.
The integration of Distributed Ledger Technologies (DLT), such as blockchain, in power system management is rapidly gaining attention for its potential to enhance grid transparency, security, and operational efficiency. This paper explores the role of DLT in power systems, focusing on decentralized energy trading, grid data integrity, and smart contract-based automation for demand response. It highlights the benefits of DLT in managing distributed energy resources (DERs), securing transactions, and improving trust among stakeholders. Challenges related to scalability, energy consumption, and regulatory compliance are also discussed. Case studies from pilot projects demonstrate promising results, underscoring DLT’s transformative potential in modernizing power grids.
The DC-Microgrids (DC-MGs) are increasingly prone to various cyber-attacks due to the advancement of intelligent controlling, monitoring, operation methods. A typical DC-MGs integrates components like batteries, super capacitors, electronic devices, Photovoltaic (PV) systems, and loads. Given these vulnerabilities, cyber-attack detection, and the security of data exchanged in smart DC-MGs, similar to Cyber-Physical Systems (CPS), have become critical areas to focus. This paper proposes a novel approach to detect false data injection attack (FDIAs) in DC-MGs using Wavelet transform and Support Vector Machines (SVMs) with Blockchain technology. The analysis shows that the output voltage dropped from 350 V to 300 V during the False Data Injection Attack (FDIA) at 0.4 s and returned to 350 V by 0.7 s. Significant oscillations observed between 0.4 and 0.7 s and detection model achieved 400 true negatives, 191 true positives, 10 false negatives, and no false positives, demonstrating high accuracy in identifying FDIA instances.
Urooj Waheed, Sadiq Ali Khan, Muhammad I. Masud, Huma Jamshed · 6 authors
The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control.