Aiming at the critical challenges of fragmented environmental-economic value tracking and inefficient multi-stakeholder coordination in green electricity trading, this study proposes a blockchain-based collaborative management method integrating environmental attributes (e.g., carbon offsets) with economic transactions. Leveraging blockchain’s decentralized, tamper-proof distributed ledger, the method ensures transaction transparency, automates settlement via smart contracts, and establishes a verifiable audit trail for environmental benefits. Experimental comparisons demonstrate that the blockchain platform reduces transaction costs by 30%, shortens settlement time by 75%, and significantly enhances market liquidity and transparency versus traditional modes. This approach optimizes resource allocation, minimizes intermediary dependencies, and provides a robust technical pathway for scaling green power adoption. Key implementation barriers include blockchain’s energy consumption, smart contract vulnerabilities, and regulatory fragmentation across jurisdictions. Future work will focus on enhancing blockchain energy efficiency and developing cross-regional regulatory frameworks for green power markets.
Ümit Cali, Annabelle Lee, Barry Hayes, Cláudio Lima · 23 authors
The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization , leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems . This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services , and peer-to-peer energy trading . The model’s rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.
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.
Energy prosumer communities offer a mechanism where prosumers can share, and trade locally produced renewable generation directly with consumers within the same energy community. Accordingly, there is need for a decentralized approaches that enables prosumers to locally balance generation and consumption. The deployment of emerging technologies such as Distributed Ledger Technologies (DLT), Internet of Things (IoT), and Artificial Intelligence (AI) can accelerate the integration of Renewable Energy Sources (RES) and advance the development of energy internet. Therefore, this article develops an energy system architecture that shows how DLT, IoT, and AI can be deployed to support the design and actualization of energy internet in Distributed Prosumer Energy Communities (DPEC). The system architecture support energy sharing and trading from energy prosumers to consumer. Findings from this study presents how DLT based smart contracts can be employed to securely manage energy transactions within the energy internet. The system architecture provides energy consumers and prosumers with a decentralized approach for sharing and trading local energy generation without requiring any central intermediary. More importantly, this study presents a use case on the applicability of DLT and AI to support micro grid operations in DPEC.
Achieving global sustainability goals requires integrating renewable energy sources (RES) into the electrical grid, but their intermittent nature poses challenges to grid stability and supply-demand balance. Energy storage systems (ESS) offer a solution, yet their deployment remains complex and costly. This study proposes an optimized conceptual model for ESS placement using blockchain-powered smart contracts to automate decentralized energy trading. The research methodology involves a comprehensive literature review and the development of a conceptual model for optimal storage placement. A thorough examination of the literature and the creation of a conceptual model for optimal storage location serve as the foundation of the research approach. Smart contracts are proposed to enhance energy trading and storage management with transparency and efficiency. Energy trading and storage management will be automated through the design and implementation of smart contracts. Expected outcomes include improved grid reliability, lower operating costs, and greater integration of renewable energy sources. A decentralized energy market is made possible by the model’s use of blockchain technology, which allows producers and consumers to exchange energy directly without the need for centralized utilities. This study advances blockchain applications in energy, offering innovative solutions and insightful information to grid operators, policymakers, and market participants, fostering a more sustainable and efficient electricity grid.
Mohammad Seyfi, Mehdi Mehdinejad, Behnam Mohammadi-Ivatloo, Jamshid Aghaei
The increasing integration of distributed renewable energy resources has transformed consumers into prosumers. Peer-to-peer (P2P) energy trading markets have emerged as a promising solution to facilitate prosumer participation in energy markets. This paper proposes a novel market model for local P2P energy token trading, integrating self-decisive retailers and smallscale prosumers with flexible loads. Within the proposed market structure, retailers as self-decisive agents, can participate in a dynamic and competitive market, ensured by demurrage mechanism, and connect local prosumers to upper energy markets. In this model, local prosumers can choose either directly trade energy tokens with retailers or with other prosumers in the P2P market. Moreover, by having both shiftable and cuttable loads, prosumers can manage their energy costs more effectively and improve their trading strategy. The simulation results showed the importance of the demand response program in promoting the independence of the local P2P energy token trading market. Furthermore, results show retailers can increase the welfare of local prosumers in a local P2P energy token market.
This thesis presents a comprehensive framework for intelligent energy management in residential environments through the development and implementation of an advanced integrated hardware and software architecture. The primary objectives were to design a modular, scalable system capable of real-time energy monitoring, predictive consumption analysis, and secure peer-to-peer energy trading; to implement this system using cost-effective hardware components and open-source software solutions; to develop a prototype demonstrating core functionalities; and to evaluate the system's performance, limitations, and potential for future enhancement within the evolving smart grid ecosystem. The methodological approach encompassed a layered architecture integrating physical hardware sensors with containerized software components. At the hardware level, a Raspberry Pi 4 platform was utilized as the central computing unit, augmented with specialized energy monitoring components including the RPICT7V1 module, SCT-013-000 current transformers, and ZMPT101B voltage sensors. This configuration enabled granular measurement of electrical parameters with ten-second sampling intervals. The software architecture was constructed on a Linux-based server environment using MicroK8s for lightweight Kubernetes orchestration. This containerized microservices framework incorporated InfluxDB for time-series data storage, Grafana for real-time visualization, and a Large Language Model (LLM) for predictive analytics. External data integration was achieved through a REE API connection that provided real-time electricity pricing information from the Spanish market. Additionally, a decentralized energy trading system was conceptually designed using the Cardano blockchain, featuring smart contracts implemented in Plutus and non-fungible tokens (NFTs) for transaction traceability. The testing and validation phase confirmed the successful deployment and operational functionality of the core system components. The hardware sensing layer demonstrated accurate data acquisition capabilities, reliably capturing voltage and current measurements across multiple channels. The containerized software infrastructure exhibited stable operation and effective inter-service communication within the Kubernetes environment. End-to-end data flow validation verified the complete pathway from sensor readings through storage in InfluxDB to visualization in Grafana dashboards. The integration of external energy market data from the REE API was successfully implemented, enabling real-time electricity price monitoring. Functional Grafana dashboards provided intuitive visualization of key energy metrics including power consumption, voltage levels, current intensity, and PVPC pricing. While computational constraints of the Raspberry Pi platform limited full LLM functionality, and the peer-to-peer trading system remained at the architectural design stage rather than full implementation, these limitations were clearly identified as areas for future development. In conclusion, this research successfully demonstrated the viability of integrating real-time monitoring, containerized microservices, and foundational blockchain technologies within a residential energy management context. The prototype represents a significant advancement toward democratized energy management through its modular, open-source architecture and cost-effective hardware implementation. Future work will focus on migrating the computational stack to more powerful hardware such as the NVIDIA Jetson Orin Nano platform to enable full LLM integration, implementing the complete peer-to-peer trading functionality, and developing advanced features including dynamic load balancing, demand response integration, and multi-site energy management. This research contributes to the evolving field of intelligent energy systems by providing a blueprint for scalable, decentralized solutions that enhance grid resilience, empower energy prosumers, and support the transition toward sustainable, efficient energy ecosystems
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.
Bitcoin's high volatility poses significant challenges for short-term price prediction, making it a critical area of study for financial forecasting. Traditional models such as Long Short-Term Memory (LSTM) networks often encounter difficulties in handling long-range dependencies and non-stationary data, limiting their predictive accuracy under volatile conditions. This study introduces the Time-Series Transformer (TST) as a novel approach to predict Bitcoin's short-term prices. By leveraging self-attention mechanisms, TST effectively captures complex temporal patterns in historical Bitcoin data, including prices and trading volume. The data was segmented into fixed-length windows to facilitate model training and testing. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Scaled Error (MASE), and R-squared (R²) demonstrated TST’s superior performance over LSTM, particularly during periods of high market fluctuation. Furthermore, TST exhibited notable computational efficiency when working with large datasets, underscoring its scalability. These findings not only highlight TST’s potential for enhancing cryptocurrency price prediction but also pave the way for future research integrating external data sources and exploring further model enhancements for more robust financial forecasting.
Abstract This chapter examines how blockchain technology can advance energy management in smart buildings and urban areas. It analyzes the theoretical foundations of blockchain solutions and their integration into energy monitoring, control, and optimization strategies. It describes how blockchain can manage peer-to-peer transactions, coordinate distributed energy resources, and facilitate trust-minimized data sharing for building operators, occupants, and regulators. It then explores digital twin models, cybersecurity considerations, interoperability approaches, and energy market mechanisms that rely on immutable ledgers and smart contracts. The chapter reviews challenges and outlines directions for future research and deployment. The discussion underscores that blockchain presents potential for transparent and automated energy management. Realizing these benefits depends on supportive policies, reliable technology, cross-sector engagement, and continued system refinement.
Ethereum smart contracts enable decentralized applications (dApps) on the blockchain and execution of each transaction on dApps incurs a gas cost (fee). While the gas cost prevents denial-of-service (DoS) attacks and incentivizes miners, unoptimized smart contracts may cause excessive gas waste, resulting in higher transaction costs and reduced scalability. Hence, this paper introduces four novel gas waste detectors integrated into the Slither static analysis framework to identify common gas wastes in Solidity smart contracts, including redundant use of address(this), division by unsigned integers, short constant strings, and unnecessary type casting. We analyzed 397 smart contracts from 53 dApps deployed on Ethereum, uncovering 250 instances of gas waste. Our findings indicate that every dApp analyzed exhibits at least one form of gas waste. Notably, division by unsigned integers emerged as the most prevalent gas waste, while half of the dApps could save gas by optimizing short constant strings. To facilitate further research, the annotated dataset of gas waste instances is available on GitHub.
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.
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.
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.
To fully utilize the energy on the user side and establish a new integrated energy trading system to realize energy transactions among users, it is imperative to conduct research on the architecture and pricing models of energy trading systems. Based on the study of the application of blockchain technology in energy trading, this paper constructs a peer-to-peer (P2P) energy trading system using blockchain technology, enabling users to conduct energy transactions without the involvement of a third party. A dynamic energy pricing method based on game theory according to the supply–demand ratio (SDR) is proposed in this paper. The pricing model considers user satisfaction and energy supply–demand comprehensively, introduces the concept of game theory, and constructs an optimized microgrid trading model under the P2P information interaction state. This paper also discusses the application scenarios and operation processes of the P2P energy system, and carries out relevant tests. The test results show that the system has high performance and efficiency, and can meet the needs of energy trading. Finally, through simulation examples, it is proved that the pricing model proposed in this paper provides users with significant benefits and technical support, and can serve as a reference for the application of blockchain in P2P energy trading.
Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate energy donations. We analyze the energy donation process by exploring various approaches and categorizing them based on both the source of donated energy and funding origins. We propose a novel Hybrid Energy Donation (HED) algorithm, which enables contributions from both external and internal donors. External donations are payments sourced from entities such as charities and organizations, where energy is sourced from the utility grid and prosumers. Internal donations, on the other hand, come from peer contributors with surplus energy. HED prioritizes donations in the following sequence: peer-sourced energy (P2D), utility-grid-sourced energy (UG2D), and direct energy donations by peers (P2PD). By merging these donation approaches, the HED algorithm increases the volume of donated energy, providing a more effective means to address energy poverty. Experiments were conducted on a dataset to evaluate the effectiveness of the proposed method. The results showed that HED increased the total donated energy by at least 0.43% (64 megawatts) compared to the other algorithms (UG2D, P2D, and P2PD).
Ezinne C Chukwuma-Eke, Olakojo Yusuff Ogunsola, Ngozi Joan Isibor
Access to affordable and reliable energy remains a significant challenge for underserved communities, particularly in developing regions. Financial constraints, lack of investment, and inadequate policy frameworks hinder the widespread adoption of modern energy solutions. This paper explores the role of financial inclusion strategies, driven by technology and policy interventions, in improving energy access for marginalized populations. By integrating digital financial services, decentralized energy systems, and innovative policy measures, this study proposes a comprehensive framework to bridge the energy gap. The proposed framework focuses on leveraging financial technology (FinTech), mobile banking, and blockchain-based microfinancing to enhance accessibility to clean energy solutions. Digital payment platforms and mobile-based credit scoring models facilitate microloans for renewable energy adoption, empowering low-income households and small enterprises. Blockchain technology ensures transparency, security, and accountability in financial transactions, reducing the risks of fraud and inefficiencies in energy financing. Policy interventions play a crucial role in fostering financial inclusion and energy accessibility. Targeted subsidies, regulatory reforms, and public-private partnerships are essential for creating an enabling environment. Governments and financial institutions must collaborate to design policies that incentivize investment in decentralized energy projects, such as mini-grids and off-grid solar solutions. Additionally, carbon credit markets and green bonds can provide sustainable financing mechanisms for long-term energy development. A case study analysis highlights successful implementations of technology-driven financial inclusion models in regions with limited energy access. Results demonstrate that integrating mobile financial services and decentralized energy solutions leads to increased energy affordability, economic empowerment, and improved quality of life. The findings underscore the need for a multi-stakeholder approach, combining technological innovation, policy support, and community engagement to drive sustainable energy inclusion. This study contributes to the discourse on financial inclusion and energy sustainability by proposing a data-driven and policy-oriented approach. Future research should explore the scalability of digital financial services in emerging markets and the long-term impact of financial inclusion strategies on energy equity.
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.
The deployment of Internet of Things (IoT) devices and edge computing has grown exponentially and has reinvented the world of data processing and making it possible to deliver low-latency applications in real-time settings. Notwithstanding, with this shift towards the use of distributed systems, we are faced with new challenges of ensuring there is effective management of energy consumption. The main aim of the proposed study was to design, deploy, and test a new decentralized edge computing framework that combines blockchain technology and artificial intelligence to achieve optimized energy efficiency. To be more precise, we intended to create AI models that are able to recognize and forecast energy usage patterns at the edge in real-time. The system of 250 edge devices on a network in this study simulated the environment of the smart infrastructure, which portrays a medium-sized U.S. urban grid. All of these devices were able to record important performance and system data on an ongoing basis over more than 30 days at a resolution of 10 seconds, and provide more than 60 million data points. Prominent variables that are recorded are CPU usage (%)/memory load (MB) and energy level (Watts), which is a reflection of the device in terms of operation strain and efficiency. So that edge workloads can be classified according to their energy consumption rates and usage trends to facilitate energy-efficient scheduling. Three supervised machine learning models were chosen: Logistic Regression, Random Forest Classifier, and Support Vector Classifier (SVC). The preprocessed dataset was divided into 80:20 train and test sets to ensure that there was no data leakage, and all three models were trained on the datasets and evaluated on the test set. Based on the measurement, Random Forest had the most accurate predictions, meaning that it tended to slightly outdo the other models in this comparison. The next two models, notably logistic Regression and SVM, respectively, had the lowest accuracy of the three models. The encountered blockchain mechanism, i.e., lightweight transaction ledgers including Hyperledger Sawtooth, offered informative transparency and traceability of energy behavior in edge networks. Introducing blockchain-based green edge computing is about to change the energy management approach in smart cities and intelligent energy grids in the U.S. The introduction of IoT-powered networks in metropolitan areas such as New York City, San Francisco, and Chicago, including traffic sensors and adaptive lighting, autonomous transportation, and Wi-Fi hotspots, has also meant that the energy requirements of distributed edge networks are being placed at a serious burden. Green edge computing with blockchain has an important role in defense and the safety of the population by assuring safe, energy-saving decision-making in the field. The DOD (U.S Department of Defense) mainly depends on mobile and distributed sensor networks to perform surveillance of the theaters of operation, environmental tracking, and real-time information. The findings of the current research add value to the potential of AI-powered methods in increasing energy efficiency in edge computing solutions, especially when combined with blockchain frameworks.
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.
This study presents a novel system for simulating an energy community utilizing advanced tools such as Simulink and the Ethereum Sepolia blockchain. Simulink is employed to simulate energy transactions within the community, while the Ethereum Sepolia blockchain is used to securely record these transactions. The primary objective of this study is to assess the feasibility of establishing an energy community that utilizes a public blockchain, such as Ethereum Sepolia, within a simulated environment using the Simulink tool. This research is distinctive because no previous study has utilized a public blockchain for this type of community model through a simulation tool like Simulink. Public blockchains, while offering numerous advantages, often suffer from high transaction costs. This study aims to address this challenge by proposing a solution that can reduce these costs without compromising security or decentralization.