Secure and Efficient Energy Trading using Homomorphic Encryption on the Green Trade Platform
Keywords:
Homomorphic encryption, Energy trading, Secure energy trading, GreenTrade platform, Simulation frameworkAbstract
Energy trading helps buyers and sellers trade energy in today’s dynamic energy markets. However, protecting sensitive energy trade data has become a priority as digital platforms become more important. Traditional encryption methods may not be enough for energy trading applications. This study proposes using homomorphic encryption to solve these problems. Energy trade data is protected by the suggested encryption technique, which uses the RSA algorithm, OAEP padding scheme, and SHA256 hash algorithm. Python is used to create a simulation framework to test the suggested strategy. Random energy needs, supply, and buyer and seller prices are produced to simulate energy trading transactions. Homomorphic encryption is secure and efficient, as shown through simulations. The research investigates the scheme’s performance using homomorphic techniques to reduce computing overheads. The simulation code also assures correctness and reliability throughout the evaluation. Energy trading system buyers’ entire value is visualised using bar charts. The results derived from the simulations demonstrate the efficacy of the proposed methodology. The homomorphic encryption scheme effectively maintains the confidentiality of sensitive trading data throughout its transmission and storage. The encrypted data is securely stored and shared within the blockchain-based GreenTrade platform, establishing a reliable and trustworthy environment for participants engaged in energy trading. The assessment of the system’s performance showcases the effectiveness of the encryption scheme, rendering it a feasible resolution for energy trading scenarios in real-world scenarios. This research article thoroughly investigates a unique hybrid strategy for safe and efficient energy trading employing homomorphic encryption. The suggested encryption technique addresses energy trade data security and privacy. The simulation findings demonstrate the approach’s viability and promise to improve energy trading security and privacy. The research advances energy trading by using modern cryptography.
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K. H. Cao, H. S. Qi, C. H. Tsai, C. K. Woo, and J. Zarnikau, “Energy trading efficiency in the US Midcontinent electricity markets,” Applied Energy, vol. 302, pp. 117 505–117 505, 2021.
B. Ata, P. M. Seufert, C. Heinze, F. Alobaid, and B. Epple, 2021. [Online]. Available: https://scite.ai/reports/10.3390/en14217285
L. Du, “Development and Validation of an Energy Consumption Model for Animal Houses Achieving Precision Livestock Farming,” Animals, vol. 12, no. 19, 2022.
L. Fu, X. Fu, and H. I. Marrani, 2021. [Online]. Available: https://scite.ai/reports/10.1080/21642583.2020.1870580
Y. H. Yacouba, Training, A. Diabagate, A. Maiga, and A. Coulibaly, 2021. [Online]. Available: https://scite.ai/reports/10.5815/ijitcs.2021.01.02
S. Eom and J. Huh, 2018. [Online]. Available: https://scite.ai/reports/1 0.3390/math6100202
P. Singh, M. Masud, M. S. Hossain, and A. Kaur, “Blockchain and homomorphic encryption-based privacy-preserving data aggregation model in smart grid,” Computers & Electrical Engineering, vol. 93, pp.
107 209–107 209, 2021.
W. Li, H. Liu, P. Yang, and W. Xie, 2016. [Online]. Available: https://scite.ai/reports/10.1371/journal.pone.0156479
V. K. Ponnusamy, P. Kasinathan, R. M. Elavarasan, V. Ramanathan, R. K. Anandan, U. Subramaniam, . . Hossain, and E, “A comprehensive review on sustainable aspects of big data analytics for the smart grid,” Sustainability, vol. 13, no. 23, pp. 13 322–13 322, 2021.
O. M. Butt, M. Zulqarnain, and T. M. Butt, “Recent advancement in smart grid technology: Future prospects in the electrical power network,” Ain Shams Engineering Journal, vol. 12, no. 1, pp. 687–695, 2021.
A. Khan, A. A. Laghari, M. Rashid, H. Li, A. R. Javed, and T. R. Gadekallu, “Artificial intelligence and blockchain technology for secure smart grid and power distribution Automation: A State-of-the-Art Review,” Sustainable Energy Technologies and Assessments, vol. 57, pp. 103 282–103 282, 2023.
O. I. Abiodun, M. Alawida, A. E. Omolara, and A. Alabdulatif, 2022.[13] X. Chu and Wen, 2020. [Online]. Available: https://scite.ai/reports/10.3390/en13195229
S. Nzuva, “Smart contracts implementation, applications, benefits, and limitations,” Journal of Information Engineering and Applications, vol. 9, no. 5, pp. 63–75, 2019.
H. Li, F. Xiao, L. Yin, and F. Wu, “Application of blockchain technology in energy trading: A review,” Frontiers in Energy Research, vol. 9, pp. 671 133–671 133, 2021.
M. Niu and Y. Yang, “Research on capacity allocation of optical storage system based on supply demand balance under the background of green power trading,” E3S Web of Conferences, vol. 385, 2023.
O. J. Olujobi, U. E. Okorie, E. S. Olarinde, and A. D. Aina-Pelemo, 2023.
J. Li, G. Li, and P. Zhao, “Research on the market-oriented trading of renewable energy power based on BlockChain technology under the new quota system,” IOP Conference Series: Earth and Environmental Science, vol. 702, pp. 12 031–12 031, 2021.
J. Li and D. Huang, “The impact mechanism of China’s carbon emission trading policy on industrial energy efficiency under multiple innovation approaches,” Frontiers in Energy Research, vol. 10, pp. 1 000 429–1 000 429, 2023.
Zhou, L. Cheng, X. Su, P. Xuan, C. Tang, H. Yu, and J. Wang, “Simulation Analysis of the Hydro and Thermal Electricity Replacement Trading Model Between Yunnan and Guizhou Provinces Based on PROPHET,” IEEE 5th Conference on Energy Internet and Energy System Integration, no. EI2, pp. 3627–3633, 2021.
W. Liu, L. Guo, Y. Kou, Y. Wang, B. Li, H. Zhao, and C. Li, 2022. [22] A. Agarkar and H. Agrawal, “A review and vision on authentication and privacy preservation schemes in smart grid network,” Security and Privacy, vol. 2, no. 2, 2019.
Masthan, K. Sharma, and K. V, “Secured information sharing in mobile cloud computing using access controls,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 1559–1564, 2019.
M. K. Thukral, “Emergence of blockchain-technology application in peer-to-peer electrical-energy trading: A review,” Clean Energy, vol. 5, no. 1, pp. 104–123, 2021.
M. K. Hasan, A. Alkhalifah, S. Islam, N. B. Babiker, A. K. Habib, A. H. Aman, and M. A. Hossain, “Blockchain technology on smart grid, energy trading, and big data: Security issues, challenges, and recommendations,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–26, 2022.
Chiarini and L. Compagnucci, “Blockchain, data protection and P2P energy trading: A review on legal and economic challenges,” Sustainability, vol. 14, no. 23, pp. 16 305–16 305, 2022.
P. Sharma, R. Senapati, and A. Swetapadma, “Review of blockchainbased energy trading models,” 2021 International Conference in Advances in Power, Signal, and Information Technology, 2021.
M. Bhuvanesh, M. Shanmugamayan, M, and H. S, “Blockchain-Based Crowd Funding Platform”,” International Journal of Computer Engineering in Research Trends, vol. 10, no. 5, pp. 40–47, 2023.
M, P. D. S. Divya, and K, “ICN Scheme and Proxy Re-Encryption for Privacy Data Sharing on the Block Chain”,” International Journal of Computer Engineering in Research Trends, vol. 10, no. 4, pp. 172–76, 2023.
T. B. Malla, A. Bhattarai, A. Parajuli, A. Shrestha, B. B. Chhetri, and K. Chapagain, pp. 8571–8571, 2022. [Online]. Available: https://doi.org/10.3390/en15228571
Jaruwatcharaset, C. . (2023). Effects of Using a Temperature Control System in Bandicota indica Stalls with Internet of Things Technology. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 166–170. https://doi.org/10.17762/ijritcc.v11i4s.6324
Shanthi, D. N. ., & J, S. . (2021). Machine Learning Architecture in Soft Sensor for Manufacturing Control and Monitoring System Based on Data Classification. Research Journal of Computer Systems and Engineering, 2(2), 01:05. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/24
Mandal, D., Shukla, A., Ghosh, A., Gupta, A., & Dhabliya, D. (2022). Molecular dynamics simulation for serial and parallel computation using leaf frog algorithm. Paper presented at the PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 552-557. doi:10.1109/PDGC56933.2022.10053161 Retrieved from www.scopus.com
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