Abstract
The data security and the efficiency of energy scheduling are two main challenges for the practical application of microgrid. The existing researches have disadvantages of a single system component, unreasonable scheduling, and the lack of analysis on the main grid. In light of this, we formalize an efficient microgrid system based on Ethereum blockchain. We first use the blockchain network to upload the IoT data and predict the total load in some region and the energy generation of renewable energy, and then complete the deployment of energy based on the predicted results under the restrictions of relevant resources. In particular, we introduce a credit bidding mechanism that is based on Etheruem smart contracts to optimize energy allocation. It maximizes the proportional-fairness participation of all parties and avoids the waste of energy. In addition, we adopt group signature to preserve the privacy of user data. Simulation results show that the proposed scheme can significantly reduce users’ cost, increase the profit rate, enforce proportional fairness, and improve the operation stability of the main grid.
Supported by the National Natural Science Foundation of China under Grant 61771373.
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References
Payne, J.E.: A survey of the electricity consumption-growth literature. Appl. Energy 87(3), 723–731 (2010)
Hittinger, E., Wiley, T., Kluza, J., Whitacre, J.: Evaluating the value of batteries in microgrid electricity systems using an improved energy systems model. Energy Convers. Manage. 89, 458–472 (2015)
Khan, A.R., Mahmood, A., Safdar, A., Khan, Z.A., Khan, N.A.: Load forecasting, dynamic pricing and DSM in smart grid: a review. Renew. Sustain. Energy Rev. 54(C), 1311–1322 (2016)
Tushar, M.H.K., Zeineddine, A.W., Assi, C.: Demand-side management by regulating charging and discharging of the EV, ESS, and utilizing renewable energy. IEEE Trans. Industr. Inf. 14(1), 117–126 (2018)
Chaouachi, A., Kamel, R.M., Andoulsi, R., Nagasaka, K.: Multiobjective intelligent energy management for a microgrid. IEEE Trans. Industr. Electron. 60(4), 1688–1699 (2013)
Tushar, M.H.K., Assi, C., Maier, M., Uddin, M.F.: Smart microgrids: optimal joint scheduling for electric vehicles and home appliances. IEEE Trans. Smart Grid 5(1), 239–250 (2014)
Rabiee, A., Sadeghi, M., Aghaeic, J., Heidari, A.: Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and pv units uncertainties. Renew. Sustain. Energ. Rev. 57, 721–739 (2016)
Ju, L., Zhang, Q., Tan, Z., Wang, W., Xin, H., Zhang, Z.: Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy. Energy 157(C), 1035–1052 (2018)
Tian Wang, Md., Bhuiyan, Z.A., Wang, G., Qi, L., Jie, W., Hayajneh, T.: Preserving balance between privacy and data integrity in edge-assisted internet of things. IEEE Internet Things J. 7(4), 2679–2689 (2020)
Du, X., Xiao, Y., Ci, S., Guizani, M., Chen, H.-H.: A routing-driven key management scheme for heterogeneous sensor networks. In: 2007 IEEE International Conference on Communications, pp. 3407–3412 (2007)
Goranović, A., Meisel, M., Fotiadis, L., Wilker, S., Treytl, A., Sauter, T.: Blockchain applications in microgrids an overview of current projects and concepts. In: IECON 2017–43rd Annual Conference of the IEEE Industrial Electronics Society, pp. 6153–6158 (2017)
Chaum, D., van Heyst, E.: Group signatures. In: Davies, D.W. (ed.) EUROCRYPT 1991. LNCS, vol. 547, pp. 257–265. Springer, Heidelberg (1991). https://doi.org/10.1007/3-540-46416-6_22
Sun, L., Zhou, K., Zhang, X., Yang, S.: Outlier data treatment methods toward smart grid applications. IEEE Access 6, 39849–39859 (2018)
Tian, W., et al.: Propagation modeling and defending of a mobile sensor worm in wireless sensor and actuator networks. Sensors 17(1), 139 (2017)
Shelby, Z.: The constrained application protocol (coap). Draft-ietfcore-coap-08, 47(5), 9–11 (2010)
Chang, G.W., Lu, H.J., Chang, Y.R., Lee, Y.D.: An improved neural network-based approach for short-term wind speed and power forecast. Renewable Energy 105(MAY), 301–311 (2017)
Wang, T., Luo, H., Zeng, X., Yu, Z., Liu, A., Sangaiah, A.K.: Mobility based trust evaluation for heterogeneous electric vehicles network in smart cities. IEEE Trans. Intell. Transp. Syst. 22(3), 1797–1806 (2020)
Wood, D.D.: Ethereum: a secure decentralised generalised transaction ledger (2014)
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Zeng, Y., Deng, L., Zhang, H. (2022). An Intelligent Allocation Mechanism Based on Ethereum Blockchain in Microgrid. 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 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_45
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