Abstract
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.
Highlights
The residential smart homes’ market size is expected to be more than $50 billion before the year 2023
An energy-buying EVs (EBEVs) selects energyselling EVs (ESEVs) with the highest reputation that is presented at the top of the list
To find the perfect match, EBEV communicates with all the closest Electric Vehicles (EVs) when using the bichromatic mutual nearest neighbor (BMNN)-matching process
Summary
The residential smart homes’ market size is expected to be more than $50 billion before the year 2023. A central control unit is used that manages, processes, and regulates energy transactions This approach has some challenges, such as a single point of failure and security-privacy-related problems. The work provides an overview of the proposed strategies for wireless communication, distributed P2P energy trading, and P2P power grid control unit that enables the smart grid operations. The authors in [3] propose an energy trading model between islanded microgrids using distributed convex optimization techniques. In [4], the authors propose a virtual framework incorporated with communication constraints, which considers its impact on energy trading cost. The authors modify the distributed energy trading framework considered in the literature with more communication constraints, where the impact of the resulting virtualized microgrid framework is investigated on the overall trading costs. Adversary users heavily threaten the security and privacy of the system through many malicious exploitations [6], e.g., node impersonation, falsification, privacy leakages, and advertising fraudulent energy services
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