This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation ...consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model's performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model's results show satisfactory performance for privacy and security of the system.
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do ...the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.
With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a ...blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.
The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great ...deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.
Electricity theft (ET) is an utmost problem for power utilities because it threatens public safety, disturbs the normal working of grid infrastructure and increases revenue losses. In the literature, ...many machine learning (ML), deep learning (DL) and statistical based models are introduced to detect ET. However, these models do not give optimal results due to the following reasons: curse of dimensionality, class imbalance problem, inappropriate hyper-parameter tuning of ML and DL models, etc. Keeping the aforementioned concerns in view, we introduce a hybrid DL model for the efficient detection of electricity thieves in smart grids. AlexNet is utilized to handle the curse of dimensionality issue while the final classification of energy thieves and normal consumers is performed through adaptive boosting (AdaBoost). Moreover, class imbalance problem is resolved using an undersampling technique, named as near miss. Furthermore, hyper-parameters of AdaBoost and AlexNet are tuned using artificial bee colony optimization algorithm. The real smart meters' dataset is used to assess the efficacy of the hybrid model. The substantial amount of simulations proves that the hybrid model obtains the highest classification results as compared to its counterparts. Our proposed model obtains 88%, 86%, 84%, 85%, 78% and 91% accuracy, precision, recall, F1-score, Matthew correlation coefficient and area under the curve receiver operating characteristics, respectively.
A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of ...the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs’ charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain-based privacy-preserving energy trading system for 5G-deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation-based distributed matching algorithm for EVs and a Reward-based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short-term load forecasting model for EVs’ charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity-based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs’ component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers’ component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs’ charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy-preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug-free against the common vulnerabilities’ attacks. The results show that the smart contracts are secure against both internal and external attacks.
In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient ...charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.
Local energy trading has attracted the attention of many researchers as a result of its promising benefits. These benefits include minimizing gas emission, reducing power shortage, and establishing a ...competitive energy market. However, the energy trading between several prosumers causes trust, security, and privacy challenges in energy systems. On the other hand, a single point of failure and an increase in overall system cost occur when the energy system is managed using a centralized model. Therefore, to tackle the mentioned issues, this work proposes a two-layered secure Peer-to-Peer (P2P) energy trading model based on blockchain. The proposed model has two layers: authentication, and secure energy trading. In the authentication layer, in order to protect the proposed model from impersonation attacks, a mutual authentication process is implemented. In the energy trading layer, a new consensus mechanism is proposed to minimize the number of malicious validators. Afterwards, an incentive-punishment algorithm is introduced to motivate energy prosumers to contribute more energy in the model. Next, a dynamic contract theory based on supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing schemes. It also preserves the privacy of the actual energy consumption behavior of the trading participants. Furthermore, a consensus mechanism validators' selection model is proposed. The aim of the proposed work is to have an efficient and secure P2P energy trading platform. Simulations are executed to show the performance of the proposed model in terms of communication and computational costs, reputation, energy contributed, reward, and prices. The results for the authentication process show 7.45 ms computational cost and 1152 bits communication cost, which are better than the existing works. In the consensus process, 66.67% of the validators are selected to conduct the consensus for every transaction. This selection efficiently improves the consensus process and minimizes the number of malicious validators. In the proposed model, the increase in reward is observed for increased energy contribution, decreased non-malicious transactions and adjustment of energy consumption. The proposed model shows a satisfactory performance in terms of trust, security, and privacy.
This paper presents a hybrid model, named as hybrid deep neural network, which combines convolutional neural network, particle swarm optimization, and gated recurrent unit, termed as convolutional ...neural network-particle swarm optimization-gated recurrent unit model. The major aims of the model are to perform accurate electricity theft detection and to overcome the issues in the existing models. The issues include overfitting and inability of the models to handle imbalanced data. For this purpose, the electricity consumption data of smart meters is taken from state grid corporation of China. An electric utility company gathers the data from the intelligent antenna-based smart meters installed at the consumers’ end. The dataset contains real-time data with missing values and outliers. Therefore, it is first preprocessed to get the refined data followed by feature engineering for selection and extraction of the finest features from the dataset using convolutional neural network. The classification of electricity consumers is performed by dividing them into honest and fraudulent classes using the proposed particle swarm optimization-gated recurrent unit model. The proposed model is evaluated by performing simulations in terms of several performance measures that include accuracy, area under the curve, F1-score, recall, and precision. The comparison between the proposed hybrid deep neural network and benchmark models is also performed. The benchmark models include gated recurrent unit, long short term memory, logistic regression, support vector machine, and genetic algorithm-based gated recurrent unit. The results indicate that the proposed hybrid deep neural network model is more efficient in handling class imbalanced issues and performing electricity theft detection. The robustness, accuracy, and generalization of the model are also analyzed in the proposed work.
The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart ...communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.
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