In the backdrop of advancing communication technology and the adoption of decarbonization initiatives, peer-to-peer (P2P) electricity trading has evolved into a consequential avenue for the reliable utilization of clean energy resources. Most efforts have focused on the design of P2P distributed mechanisms to ensure that the security constraints of the grid can be adhered to. However, ensuring the assurance of the correct operation of the distributed mechanisms is also essential but has received less attention. A common assumption is that all participants in the P2P market are honest and make reasonable bids at market prices. Such an assumption could be risky because the P2P market clearing process relies on a coordination process of market participants and the clearing outcome of the P2P market is susceptible to manipulation by dishonest participants. In this work, we propose a new architecture for the P2P market by adding a verification layer based on zero-knowledge proof technology to identify dishonest bidding information of market participants without collecting their private cost information. In addition, we introduce an asynchronous market mechanism, which can greatly guide the P2P market clearing results in a dishonest environment to be close to the theoretical optimal results. Case studies demonstrate the advantages of our approach in resisting dishonesty, preserving privacy, and enhancing market robustness, which can help build a more credible and resilient P2P market environment.
The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere. This significantly jeopardizes community health in addition to exacerbating climate change, thus it is essential need to move swiftly to incorporate renewable energy sources by employing advanced information and communication technologies. However, this change brings up several security issues emphasizing the need for innovative cyber threats detection and prevention solutions. Consequently, this study presents bigdata sets obtained from the solar and wind powered distributed energy systems through the blockchain-based energy networks in the smart grid (SG). A hybrid machine learning (HML) model that combines both the Deep Learning (DL) and Long-Short-Term-Memory (LSTM) models characteristics is developed and applied to identify the unique patterns of Denial of Service (DoS) and Distributed Denial of Service (DDoS) cyberattacks in the power generation, transmission, and distribution processes. The presented big datasets are essential and significantly helps in identifying and classifying cyberattacks, leading to predicting the accurate energy systems behavior in the SG.
This proceeding volume has been retracted from the publication because we found some solid reasons to believe that it has infringed our integrity criteria and now presents a risk for our journal and scholarly science in general. Different types of malpractice are involved, in particular citation manipulation and inappropriate references. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. For further details, please refer to our publishing ethics policies . If you have any questions, please contact us at contact@webofconferences.org See the retraction notice E3S Web of Conferences 505, 00001 (2024), https://doi.org/10.1051/e3sconf/202450500001
Aakanksha Bedi, J. Ramprabhakar, R. S. Anand, U Kumaran · 6 authors
Smart grids (SGs) are technology-powered electricity networks that support bidirectional power and data flows. This allows real-time monitoring of demand and enhances the grid’s capability to dynamically adjust the generation and reduce the gap between supply and demand. However, implementing a smart grid in the power network comes with its own set of security challenges, such as cyber-security, distrust in participants, and lack of customer engagement due to various cyber-attacks. Such cyber-attacks will create distrust among consumers/prosumers to adopt the smart grid and distributed energy resources (DER) framework. To circumvent this, a blockchain-supported hybrid authentication and handshake algorithm (BSHAHA) for smart grids is proposed in this work, which authenticates data communication between peer-to-peer, aggregators, virtual power plants, and the grid. The algorithm was developed incorporating elliptic-curve cryptography (ECC) and advanced encryption standards (AES) to enhance privacy and session security. The proposed algorithms are verified and tested using formal cyber-security tools, such as the Random oracle model and AVISPA as well as by informal security analysis. Furthermore, to simulate a real-time test environment, this paper utilized ns-3 network simulator to simulate different smart meter scenarios, and the proposed algorithms are tested for power consumption and scalability, and results are presented. Moreover, blockchain simulation was first done in the local blockchain using Ganache and Truffle IDE and later using the Holesky Ethereum test network and remix-IDE. Lastly, this paper presented a comparison analysis of power consumption for different consensus mechanisms.
Jan 1, 2024·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Ali Menati, Yuting Cai, Rayan El Helou, Chao Tian · 5 authors
We model the operation of a cryptocurrency mining facility with heterogeneous mining devices participating in ancillary services as an optimization problem. We propose a general formulation for the cryptominers to maximize their profit by strategically participating in ancillary services and controlling the loss of mining revenue, which requires taking into account the disparity in the efficiency of the mining machines. The optimization formulation is considered for both offline and online scenarios, and optimal algorithms are proposed to solve these problems. As a special case of our problem, we investigate cryptominers' participation in frequency regulation, where the miners benefit from their fast-responding devices and contribute to grid stability. Simulation results based on real-world Electric Reliability Council of Texas (ERCOT) traces show more than 20\\% gain in profit, highlighting the advantage of our proposed algorithms.
N. B. Sai Shibu, Aryadevi Remanidevi Devidas, S. Balamurugan, Seshaiah Ponnekanti · 5 authors
Power outages can severely affect individuals, businesses, and communities, leading to disruptions, economic losses, and safety risks. The existing power recovery strategies often fail to adequately address the challenges associated with such outages. These challenges encompass a range of complexities, including resource allocation disparities, efficient prosumer integration, energy demand variability, and isolated generators. This paper presents a microgrid-centric power recovery strategy that leverages IoT, blockchain, smart contracts, and optimisation techniques for peer-to-peer energy sharing within the microgrid. The proposed strategy comprehensively addresses the challenges associated with the existing power recovery strategies. The paper outlines the system architecture for IoT and blockchain-enabled microgrids, discusses the mathematical modelling for energy sharing, and explores cost-optimal power restoration strategies. An incentive mechanism motivates prosumers to support restoration strategies during outages. Furthermore, the paper describes a blockchain smart contract facilitating peer-to-peer energy exchange in regions affected by power outages. This approach can mitigate the disruptive impact of power outages by providing reliable and community-centric power recovery solutions. Through validation with real-world data from our university’s distribution grid test bed, Mean Time To Recover (MTTR) analysis and performance evaluations using the Hyperledger Caliper benchmark tool, this paper demonstrates its feasibility and effectiveness, paving the way for enhanced power recovery strategies and increased resilience in the face of energy disruptions.
Marco Gerardi, Francesca Fallucchi, Fabio Orecchini
The growing adoption of renewable energy sources and the need for more efficient and secure energy grids are revolutionizing the energy sector. Electricity monitoring becomes an issue of utmost importance, as current traditional energy meters have several problems in terms of lack of transparency, very high operational costs, and the possibility of being easily tampered with. This paper proposes a new system for electricity production metering that leverages blockchain and IoT for decentralized and secure data recording while protecting user privacy and reducing operational costs. The architecture results in improvements over the traditional energy meter. The system also contributes to the generation of big data that is reliable, traceable, error-proof, and highly resistant to cyber attacks. The architectural project outputs are a smart energy meter, a smart contract on the Ethereum blockchain, and a decentralized application to manage the information recording. The experimental prototype outcomes confirm the use of these new technologies to improve energy metering, enhancing efficiency, transparency, and traceability, with reduced costs and increased user privacy.
Ziqiang Xu, Ahmad Salehi Shahraki, Carsten Rudolph
The smart grid optimises energy transmission efficiency and provides practical solutions for energy saving and life convenience. Along with a decentralised, transparent and fair trading model, the smart grid attracts many users to participate. In recent years, many researchers have contributed to the development of smart grids in terms of network and information security so that the security, reliability and stability of smart grid systems can be guaranteed. However, our investigation reveals various malicious behaviours during smart grid transactions and operations, such as electricity theft, erroneous data injection, and distributed denial of service (DDoS). These malicious behaviours threaten the interests of honest suppliers and consumers. While the existing literature has employed machine learning and other methods to detect and defend against malicious behaviour, these defence mechanisms do not impose any penalties on the attackers. This paper proposes a management scheme that can handle different types of malicious behaviour in the smart grid. The scheme uses a consortium blockchain combined with the best–worst multi-criteria decision method (BWM) to accurately quantify and manage malicious behaviour. Smart contracts are used to implement a penalty mechanism that applies appropriate penalties to different malicious users. Through a detailed description of the proposed algorithm, logic model and data structure, we show the principles and workflow of this scheme for dealing with malicious behaviour. We analysed the system’s security attributes and tested the system’s performance. The results indicate that the system meets the security attributes of confidentiality and integrity. The performance results are similar to the benchmark results, demonstrating the feasibility and stability of the system.
The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.
Xiaole Su, Yuanchao Hu, Liu Wei, Zhipeng Jiang · 7 authors
Abstract The extension of emerging renewable energy sources such as wind and water turbines, solar panels, and the increasing usage of electric vehicles requires the supply and distribution of energy in a small device on local scale and it has created new methods of supplying and selling electricity. Middle buyers and end users can obtain the local energy with the peer‐to‐peer trading method in this large and hierarchical market. This method enables market to manage and exchange the electricity between major suppliers and medium and local levels. Blockchain technology is developing in peer‐to‐peer exchange of electricity and acts as a reliable, efficient, and safe technology in the electricity trading market. In this method, while preserving the privacy of electricity users, by using smart contracts and by removing intermediaries in the energy supply and demand market, direct commercial interactions between energy suppliers and consumers are done. The blockchain technology, while creating trust between the parties in the energy market, reduces the cost of electricity trading and increases its scalability with using the intermediate energy aggregators. In this research, the blockchain‐based model, is presented for distribution and peer‐to‐peer transactions in the energy market. The suggested model provides the possibility of registration low‐cost instant transactions at the power grid in any specific period of time. The above method, unlike periodic payments, provides immediate access to bills and small payments. Since the transactions outside the blockchain chain are not recorded, this system guarantees its honest and independent operation without fraud and failure. The smart contract method based on blockchain, reduces the transaction fees and speeds up electricity trading. Also, the experimental investigation in 20 nodes shows the time required to determine the exchange contract in the blockchain method. The average is improved by 49.7% in this method. Also, the negotiation convergence time has become 47% faster.
Gehui Li, Jing Yang, Tao Yu, Fuquan Yang · 6 authors
In China, the promulgation of a green power identification system has gradually shifted from electricity power generation to consumption. However, there is no mature digital identification system for green power consumption enterprises. Exploiting the open, transparent, and immutable characteristics of blockchains, this study establishes a digital identification system for green power consumption based on blockchain technology. This system uses expert scoring and the entropy weight method as the evaluation algorithm for the green power consumption chain, issues non-fungible tokens as digital identification for consumption enterprises, and realizes the automatic generation of identification through smart contracts. These functions guarantee the credibility of the certification process and the uniqueness of the generated identification. The results of the experiments show that, in the case of multi-user concurrent requests, the number of concurrent users that the system could handle at optimal processing efficiency was 500, and block generation was stable. The proposed system has high practicability and stability.
Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink, Aaron Werth · 5 authors
Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.
The total number of solar power-producing facilities whose Feed-in Tariff (FIT) Program-based ten-year contracts will expire by 2023 is expected to reach approximately 1.65 million in Japan. If the facilities that produce or consume renewable energy would increase to reach a large number, e.g., two million, blockchain would not be capable of processing all the transactions. In this work, we propose a blockchain-based electricity-tracking platform for renewable energy, called ‘ZGridBC,’ which consists of mutually cooperative two novel decentralized schemes to solve scalability, storage cost, and privacy issues at the same time. One is the electricity production resource management, which is an efficient data management scheme that manages electricity production resources (EPRs) on the blockchain by using UTXO tokens extended to two-dimension (period and electricity amount) to prevent double-spending. The other is the electricity-tracking proof, which is a massive data aggregation scheme that significantly reduces the amount of data managed on the blockchain by using zero-knowledge proof (ZKP). Thereafter, we illustrate the architecture of ZGridBC, consider its scalability, security, and privacy, and illustrate the implementation of ZGridBC. Finally, we evaluate the scalability of ZGridBC, which handles two million electricity facilities with far less cost per environmental value compared with the price of the environmental value proposed by METI (=0.3 yen/kWh).
As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.
We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it overcame a 1.25% error rate mislabeled contracts, and--the student surpassing the teacher--found vulnerable contracts that Slither mislabeled. DLVA is much faster than other smart contract vulnerability detectors: DLVA checks contracts for 29 vulnerabilities in 0.2 seconds, a 10-1,000x speedup. DLVA has three key components. First, Smart Contract to Vector (SC2V) uses neural networks to map smart contract bytecode to a high-dimensional floating-point vector. We benchmark SC2V against 4 state-of-the-art graph neural networks and show that it improves model differentiation by 2.2%. Second, Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55.7% of the contracts in our test set, it has a Slither-predictive accuracy of 97.4% with a false positive rate of only 0.1%. Third, Core Classifier (CC) uses neural networks to infer vulnerable contracts regardless of vector distance. We benchmark DLVA's CC with 10 ML techniques and show that the CC improves accuracy by 11.3%. Overall, DLVA predicts Slither's labels with an overall accuracy of 92.7% and associated false positive rate of 7.2%. Lastly, we benchmark DLVA against nine well-known smart contract analysis tools. Despite using much less analysis time, DLVA completed every query, leading the pack with an average accuracy of 99.7%, pleasingly balancing high true positive rates with low false positive rates.
Blockchain is the fundamental component of smart contract applications. Testing technology plays a special and irreplaceable role in the development of smart contract applications in blockchain-based applications, especially in distributed renewable energy transaction scenarios in the energy industry. In practice, the formulation of blockchain technology as a standard infrastructure is an essential means to improve the reliability of blockchain-based applications in the energy industry. However, the quality of the organization-level blockchain still encounters many challenges, such as password attacks, and double spending attacks, which attract much attention from both research and academic area. Much research has focused on quality improvement through testing to fulfill the requirement toward functional, performance, and security requirements of the industry. However, the framework to accomplish the specific testing task was not present comprehensively yet. In This paper, an investigation was given on the supervision and testing of blockchain-based applications in the energy industry after its operation online. The existing testing indicator, model, and application scenario combined with the practice in the state grid industry were illustrated, in which the process, models, and methods were shown, and also suggestions were given on promoting the blockchain-based smart contract testing evaluation in the energy industry.
Tehseen Mazhar, Hafiz Muhammad Irfan, Sunawar Khan, Inayatul Haq · 7 authors
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.
Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable and renewable energy sources. Due to cyber infiltration and a lack of transparency, the traditional transaction process is inefficient, unsafe and expensive. Smart grid systems are now efficient, safe and transparent owing to the development of blockchain (BC) technology and its smart contract (SC) solution. In this study, federated learning extreme gradient boosting (FL-XGB) framework has been developed along with BC to learn the intrusion inside the smart energy system. FL is best suited for a decentralized BC-enabled system to adapt learning models for trustworthy and reliable transactions. Many features and attributes of the Third International Knowledge Discovery and Data mining Tools Competition (KDD Cup 1999) dataset have been used in this study to perform experimental analysis. The likelihood of intrusions in the network is mathematically stated. The participant nodes run the BC based FL-Smart Contract (SC) algorithms to detect network intrusions. FL provided aggregated learning results from the experiment that was 99% accurate in predicting network intrusion. The experimentally determined block storage gain and retrieval gain were 97.5% and 95.4% respectively. The intrusion in the smart grid network was evaluated, and the data indicated that there was 1.2% illegal access. Moreover, the learning system’s accuracy, retrieval and storage intrusions, legal access and transaction processing times were considered for comparison. The proposed system outperformed contemporary research-developed systems targeted for the same application. Therefore, this study provides a guaranteed intrusion learning system and secure transaction system for smart grids.
With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly, most prior studies have used contract source code as raw features, but many smart contracts only exist in bytecode. To address these issues, we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet (ECA-EfficientNet) and data enhancement. Our method begins by converting bytecode into Red Green Blue (RGB) three-channel images and then applying channel exchange data enhancement. We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images. Our proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi, honeypot, gambling, and phishing smart contracts. The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection. Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.
Bitcoin has a reputation of being used for unlawful activities, such as money laundering, dark web transactions, and payments for ransomware in the context of smart cities. Blockchain technology prevents illegal transactions, but cannot detect these transactions. Anomaly detection is a fundamental technique for recognizing potential fraud. The heuristic and signature-based approaches were the foundation of earlier detection techniques, but tragically, these methods were insufficient to explore the entire complexity of anomaly detection. Machine Learning (ML) is a promising approach to anomaly detection, as it can be trained on large datasets of known malware samples to identify patterns and features of the transactions. Researchers are focusing on determining an efficient fraud and security threat detection model that overcomes the drawbacks of the existing methods. Therefore, ensemble learning can be applied to anomaly detection in Bitcoin by combining multiple ML classifiers. In the proposed model, the ADASYN-TL (Adaptive Synthetic + Tomek Link) balancing technique is used for data balancing. Random search, grid search and Bayesian optimization are used for hyperparameter tuning. The hyperparameters have a great impact on the performance of the model. For classification, we used the stacking model by combining Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. We used SHapley Additive exPlanation (SHAP) to interpret the predictions of the stacking model. The model also explores the performance of different classifiers using accuracy, F1-score, Area Under Curve-Receiver Operating Characteristic (AUC-ROC), precision, recall, False Positive Rate (FPR) and execution time, and ultimately selects the ideal model. The proposed model contributes to the development of effective fraud detection models that address the limitations of the existing algorithms. Our stacking model, which combines the prediction of multiple classifiers, achieved the highest F1-score of 97%, precision of 96%, recall of 98%, accuracy of 97%, AUC-ROC of 99% and FPR of 3%.
This paper reviews the methods to identify unauthorized cryptocurrency mining customers in distribution networks. Hence, various methods and models were compared. Also, the research conducted in the field of cryptocurrencies was evaluated in the four categories of energy, pollution, theft, and mining.
We investigate the use of deep learning to classify smart contract code vulnerabilities. We use different variants of Convolutional Neural Networks (CNNs) and a Long Short-Term Memory (LSTM) neural network. Five classes of vulnerabilities were employed. Our results suggest that the CNNs are able to provide a good level of accuracy, thus showing the viability of the proposed approach.
Blockchain technology has changed the global trading of assets. A blockchain can be viewed as a connected ledger managed by a distributed peer-topeer (P2P) network. Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others. These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) The increasing desire for technological advancements stimulated the development of BT.