Abstract
Microgrid is a promising system for coordinating distributed energy in the future Energy Internet, where energy market is crucial for facilitating multi-directional trading. Nevertheless, the traditional solutions for energy trading usually rely on a centralized framework, which is vulnerable to high operation cost and low transparency. To address the problem, the blockchain strategy has been widely deployed in microgrid energy trading. However, existing blockchain-based systems have not formally considered: (i) collusion attacks launched by a pair of dishonest consumer and microgrid; (ii) and the demands of maximizing bilateral utility. Therefore, in this work, we propose a blockchain-based microgrid energy trading system that allows a consumer to dynamically change its service quality quotes for a microgrid. In particular, bilateral utility of requester-provider is almost maximized, with employing a Bayesian Nash equilibrium strategy to model the interaction between consumers and microgrids. Moreover, collusion attacks during the bidding process are certainly prevented from a dishonest pair of consumer and microgrid. To achieve this, we employ a request-based comparable encryption technique to compare two microgrids’ revenues in a privacy-preserving way, in which each microgrid should submit an encrypted expected revenue. In addition, a general security analysis of our system is formally discussed. To illustrate the effectiveness, we take a blockchain-based framework to build the experiment platform, where the structure of nodes and transaction process are well formalized.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61802248, 61972241, U1936213), the “Chenguang Program” supported by Shanghai Municipal Education Commission (No. 18CG62) and the Natural Science Foundation of Shanghai (18ZR1417300).
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Wang, H., Zhang, K., Wei, L., Zhang, L. (2022). Towards Requester-Provider Bilateral Utility Maximization and Collision Resistance in Blockchain-Based Microgrid Energy Trading. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_2
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