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.
Voting-Based Distributed Ledger Technologies proved efficiency and security in protecting IoT networks. They allow faster approval time, identify malicious information, and isolate adversaries through repetitive queries to adjacent peers asking their opinions about the validity of each transaction. They enable a decentralized scheme that securely constructs and stores all data types, implying either monetary values, system logs, analytical information, or triggered actions. Several consensus algorithms were introduced to enrich distributed IoT networks with data integrity, transparency, resilience, and trust. This work surveys the main challenges for deploying distributed ledgers in IoT environments. It also introduces an adaptive DLT-based framework that standardizes the fundamental required modules for securing communication among heterogeneous IoT devices. The framework comprises four integrated modules that work simultaneously to ensure data integrity and network security. Practical simulation is also performed to evaluate the effectiveness of this framework through a parameterized model. The experimental results conclude that integrating the four functional modules is essential for network efficiency, reliability, and resilience.
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.
Hafiz Muhammad Sanaullah Badar, Shafiq Ahmed, Nadeem Iqbal Kajla, Gaojuan Fan · 5 authors
The evolution of Smart Grids, a cornerstone of the Internet of Things (IoT), has revolutionized the electricity energy sector by enabling efficient, scalable, and secure energy management. As critical components of Smart Grid infrastructure, smart meters facilitate real-time data exchange between end users and service providers. However, transmitting sensitive data within these networks poses significant security challenges, particularly in the face of emerging quantum computing threats. Existing lightweight authentication and key exchange (AKE) protocols often fail to provide identity anonymity and impose substantial computational overhead, rendering them unsuitable for resource-constrained devices like smart meters. This paper presents a novel secure communication architecture for the Edge computing-assisted consumer devices and IoT (EACI) ecosystem, integrating Post-Quantum Cryptography (PQC) to ensure robust data integrity and access control against classical and quantum-era threats. The architecture employs a lightweight consortium blockchain, implemented using the Hyperledger Fabric framework, to maintain tamper-proof records of authentication events and energy transactions. Regional Edge Gateways (REGs) preprocess data and perform localized cryptographic operations, minimizing computational demands on resource-constrained devices. Furthermore, an AI-driven Intrusion Detection System (IDS) enhances the framework’s resilience by proactively detecting and mitigating security threats in real-time. The proposed architecture undergoes rigorous formal security analysis, demonstrating its robustness against quantum and classical cyber threats. Performance evaluations reveal a reduction of 31% in computational overhead, a 45% decrease in communication overhead, and a 15% improvement in energy efficiency compared to existing lightweight protocols, underscoring its suitability for resource-limited environments like smart meters. These findings establish the proposed architecture as a scalable, secure, and efficient solution for Smart Grids and other IoT environments, ensuring long-term data protection, system reliability, and operational sustainability in the quantum computing era.
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.
Xiaotian Zhou, Kimia Honari, Hao Liang, Sara Rouhani · 5 authors
In this paper, a blockchain-based decentralized stochastic energy management scheme is proposed for smart grid-connected households with photovoltaic generation and battery energy storage systems. The proposed scheme autonomously solves the joint optimization problem of maximizing individual household benefits while also minimizing total line loss in the distribution system. Internet-of-Things (IoT) technology is essential for the optimization as continuous sensing of the household power and battery states, coupled with autonomous bidding in voltage regulation auctions, are vital ingredients of this energy management scheme. A double-auction algorithm for coordinated voltage regulation with IoT access control is designed and implemented as a smart contract, which enables proactive voltage issue diagnosis while preserving privacy. The proposed blockchain-based decentralized stochastic energy management scheme consists of two subschemes, corresponding to individual households and the distribution system, respectively. Two decentralized stochastic energy management schemes are proposed for the blockchain platform. Specifically, an individual household energy management scheme is developed to maximize the benefit for each residence, while a collaborative energy management scheme is proposed to optimize the system’s joint benefit by minimizing the total line loss. Both schemes are modeled as decentralized Markov decision processes, thus avoiding the need for centralized computation. To enhance the scalability of the smart contract, a novel pruning-based auction algorithm for voltage regulation is developed. A case study on the IEEE 123-Node Test Feeder indicates that residential voltage regulation can be coordinated effectively through the proposed scheme. The algorithm is computationally efficient, with computation time scaling logarithmically with the number of participants.
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.
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.
Minlibe LAMBONI, Eyouléki Tcheyi Gnadi Palanga, Kossi TEPE
The management of smart grids requires enhanced transparency and efficient optimization of energy transactions. While blockchain technology is widely used to ensure traceability and decentralization, existing solutions primarily focus on commercial aspects, often overlooking detailed monitoring of energy flows. This study proposes a hybrid blockchain model combining Ethereum and Hyperledger Fabric to integrate secure transaction execution with real-time energy flow tracking. The adopted approach involves identifying key components of decentralized energy management, conducting a comparative analysis of blockchain architectures, and performing experimental simulations to evaluate their performance in terms of latency, security, and scalability. Specific metrics, such as transaction throughput, block validation time, and energy data granularity, were utilized to assess the efficiency of the proposed model. The results demonstrate that Hyperledger Fabric excels in energy flow monitoring and auditability, whereas Ethereum optimizes transaction execution through its consensus mechanism and broad adoption. The integration of both technologies enables optimal complementarity, ensuring effective interoperability and significantly improving overall system transparency and efficiency. The proposed hybrid model establishes a scalable and resilient architecture that enhances coordination among network participants and optimizes energy governance. It fosters trust among stakeholders by ensuring the integrity and immutability of exchanges while enhancing the management of distributed energy resources. By integrating Ethereum and Hyperledger Fabric, this solution provides an innovative and applicable framework for decentralized energy infrastructures, optimizing transaction management, improving energy flow traceability, and reinforcing the resilience of smart grids against increasing demands for flexibility and sustainability.
Konstantinos Tsiounis, Kostas Kontogiannis, Dimitris Lyras, John Mylopoulos
Complex enterprise software industrial systems encompass large volumes of transactions and complex business logic that must comply with various context-aware policies. Ensuring compliance across transaction chains is a challenging and error-prone task. This paper presents a conceptual architecture and prototype implementation to address these challenges. The architecture is based on two key principles, first, abstracting the APIs of underlying services with stub components, and second, allowing these stubs to invoke relevant policies as a service before proceeding with the transaction. If the policies are validated, the stub calls the stub version of the callee, which then delegates the operation to the actual callee, acting as a driver for the transaction. The approach introduces key innovations, simplifying integration by removing the need for transacting parties to know policies in advance. Policies are evaluated within the context of prior transactions, ensuring consistency and compliance across the transaction chain. Additionally, smart contracts execute these policy assessments, with outcomes securely logged on the blockchain ledger to create an immutable audit trail. Beyond enforcement, the policies can also be used as domain-specific security “challenges” that must be resolved before transactions proceed, making it thus difficult for intruders to bypass. This dual functionality addresses both compliance and cybersecurity, providing a robust solution for complex systems.
The integration of blockchain , Internet of Things devices, and distributed energy resources is revolutionizing peer-to-peer energy trading by enabling decentralized, efficient, and transparent transactions. However, existing solutions face challenges related to privacy, interoperability, and scalability. This paper presents PrGChain, a privacy-preserving blockchain-enabled energy trading framework within the smart grid, incorporating a decentralized ZKOracle to securely connect blockchain networks with off-chain energy data sources . The proposed ZKOracle employs zero-knowledge proofs to verify energy data without exposing sensitive information , ensuring compliance with privacy regulations, while leveraging a distributed network of oracle nodes for enhanced reliability and interoperability. To improve security and efficiency, PrGChain utilizes smart contracts , decentralized applications (dApps), and leverages stablecoins to mitigate cryptocurrency volatility. Performance evaluations demonstrate that our system achieves improved decentralization and privacy without sacrificing efficiency. This is particularly true when deployed on Layer 2 blockchain networks like Polygon, where transaction latency and costs are significantly reduced.
Aakanksha Bedi, J. Ramprabhakar, R. Anand, Veerpratap Meena · 5 authors
Energy is the basic prerequisite of any industry, but global warming, climate change, and pollution are increasing due to digitization and industrialization. Rising electricity demand, combined with the integration of electric vehicles, has made it increasingly challenging to rely solely on conventional centralized power systems. To meet this current changing energy scenario, it becomes essential to introduce green energy into conventional power systems and allow bi-directional energy flow. Microgrids, smart grids, and virtual power plants will play an important role in making this massive shift from a centralized system to a decentralized power system. A virtual power plant is a cloud-based energy system incorporating various microgrids, energy storage, distributed energy resources, and weather forecasting. Since this system is virtual, it could lead to cyber threats. To the best of the authors’ knowledge, this review article complies with recent data from ten major research libraries, offering consolidated insights into the virtual power plant (VPP) framework that will enhance customer participation and encourage them to become prosumers. Additionally, a blockchain-based VPP framework is presented along with two very prominent scenarios of blockchain-based distributed VPP involving P2P transactions and NW trading discussed for building futuristic NZEGs aimed at reducing carbon footprints and providing a foundation for future net-zero energy grids (NZEZs).
Aditya R. Malhotra1, Kavya S. Ahuja2, Vihaan P. Bansal3, Tanvi R. Kapoor4
The integration of blockchain technology, particularly Bitcoin-inspired decentralized protocols, into smart grid systems offers innovative solutions for energy management, security, and peer-to-peer (P2P) energy trading. Traditional centralized grids face challenges including inefficiencies, security vulnerabilities, and lack of real-time transactional transparency. By leveraging Bitcoin-like blockchain mechanisms, smart grids can facilitate secure, automated, and auditable energy transactions between distributed producers and consumers. This paper explores the design principles of Bitcoin-enabled smart grids, including consensus protocols, cryptographic transaction verification, and integration with IoT-based energy meters. It also examines case studies and simulation models demonstrating the potential for reduced energy losses, enhanced security, and decentralized grid optimization. Finally, challenges related to scalability, transaction speed, and energy consumption of blockchain networks are discussed, highlighting directions for future research in sustainable and efficient decentralized energy systems.
With the widespread application of blockchain technology across various fields, the security of smart contracts has become increasingly important. In the field of anomaly detection, particularly in smart contract vulnerability detection, pre-trained models have demonstrated tremendous potential, improving the accuracy and efficiency of vulnerability detection. However, these models typically focus on a single modality, which limits their applicability. While integrating multiple models can mitigate this issue, effectively fusing features from different pretrained models remains a challenge that needs to be addressed.To tackle this problem, this paper proposes a feature fusion method for smart contract vulnerability detection based on pre-trained models (PFSCV). The method uses contrastive learning (CL) to capture fine-grained relational information between smart contracts, generating sample pairs based on the relationships between contracts to guide the fine-tuning of the pre-trained model CodeBERT, enabling it to learn contextual information from the source code. At the same time, the UnixCoder model is fine-tuned to extract data flow information from the contracts. Subsequently, we introduce an att-BiLSTM (Attention-based Bidirectional Long Short-Term Memory) model to fuse and integrate features extracted from different pre-trained models. Finally, extensive experiments on real-world smart contract datasets validate the effectiveness and reliability of the proposed method.
This paper presents an innovative access control model for decentralized power systems by integrating zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs). The proposed model addresses the growing need for secure, privacy-preserving management of access rights within distributed energy infrastructures. By leveraging zk-SNARKs, the system ensures that authorization can be validated without disclosing sensitive information, thereby enhancing both security and operational transparency. The model is rigorously analyzed through a combination of theoretical proofs and simulation experiments, demonstrating its effectiveness in mitigating unauthorized access and reducing computational overhead. Our findings indicate that the zk-SNARK-based approach not only fortifies the security framework of decentralized power systems but also facilitates scalable and efficient access management. This work provides a critical step towards the realization of robust, privacy-aware energy networks in the era of smart grids and distributed generation.
Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an immutable record of CPS identities and transactions in federated environments. However, using Distributed Ledger (DL) within a CPS raises some important questions regarding scalability, throughput, latency, and potential bottlenecks, which require effective modeling of DL performance. This paper proposes a novel approach to modeling distributed ledgers using Colored Timed Petri Nets (CPNs). We focus on the performance modeling of Hyperledger Fabric (HLF), a permissioned distributed ledger technology which provides a backbone for a Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI), a novel framework for secure communication between CPS devices. We implement our model using CPN Tools, a widely adopted CPN modeling software that provides advanced simulation, analysis, and performance monitoring features. Our model offers a robust framework for studying distributed ledger systems’ synchronization, throughput, and response time. It supports flexibility in modeling transaction validation and consensus algorithms, which provides an opportunity for adapting the model to future changes in HLF and modeling other DLs. We successfully validate our CPN model by comparing simulation results with experimental measurements obtained from a LAAFFI prototype.
The integration of Zero-Trust Architecture (ZTA) and Blockchain-based Security Models in IoT-driven industrial power electronics systems has emerged as a transformative approach to mitigating cyber threats and ensuring robust access control. Traditional security mechanisms, which rely on perimeter-based defenses, are increasingly ineffective against advanced persistent threats (APTs), insider attacks, and lateral movement techniques within industrial IoT (IIoT) environments. Zero-Trust security enforces continuous verification, least-privilege access, and micro-segmentation, ensuring that no device or user was inherently trusted. Implementing ZTA in resource-constrained IoT ecosystems presents significant challenges related to computational overhead, authentication latency, and secure data transmission. To address these limitations, blockchain technology enhances decentralized identity management, immutable access logs, and tamper-resistant security frameworks, fortifying Zero-Trust-based access control. Privacy-preserving cryptographic techniques, including zero-knowledge proofs (ZKPs) and homomorphic encryption, safeguard sensitive industrial data while maintaining compliance with evolving regulatory frameworks. AI-driven anomaly detection models reinforce continuous authentication and behavior-based threat monitoring, enabling proactive defense mechanisms against zero-day exploits and sophisticated cyber intrusions. This chapter presents a comprehensive analysis of Zero-Trust implementation models for IIoT systems, highlighting the role of secure communication protocols, distributed ledger-based identity verification, and adaptive security automation. The integration of blockchain-enabled access control and AI-powered real-time security analytics ensures a resilient security posture for industrial power electronics networks, mitigating risks associated with unauthorized access, data breaches, and operational disruptions. The proposed framework enhances scalability, privacy, and computational efficiency, paving the way for next-generation cybersecure industrial ecosystems.
Smart grids integrate IoT devices and advanced communication technologies, enabling decentralized energy trading and optimizing energy management. However, widespread adoption faces challenges related to privacy, security, transparency, and scalability, which hinder the trust and efficiency required for such systems. Blockchain technology, particularly Ethereum, offers a promising solution by providing a decentralized and immutable ledger combined with smart contract capabilities for automated, trustless transactions. This study presents a blockchain-driven framework tailored for energy trading within IoT-enabled smart grids, focusing on privacy-preserving mechanisms, secure and tamper-proof transactions, and scalable solutions to handle high-frequency trading efficiently. The proposed system includes key operational phases, such as user registration, transaction validation, and block creation, ensuring robust data integrity and trust. By leveraging Ethereum’s decentralized platform, this framework promotes real-time monitoring, automated settlements, and secure energy trading while addressing scalability challenges through hybrid blockchain models. The proposed approach not only enhances energy management efficiency but also provides a sustainable and transparent pathway for the future of decentralized energy markets.