Energy Exchange Management in Two-Layer Microgrids Using the Hiking Optimization Algorithm and Blockchain Technology

Subject Areas : Electrical and Computer Engineering

Mohamad Mahdi Erfani Majd ¹ , Reza Davarzani ² , Mahmoud Samiei Moghaddam ³ , Ali Asghar Shojaei ⁴ , Mojtaba Vahedi ⁵

1 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran.
2 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran.
3 - Department of Electrical Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran.
4 - Department of Electrical Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.
5 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran.

Received: 2024-11-29 Accepted : 2025-04-01 Published : 2026-09-22

Keywords: Microgrids, Transaction security, Blockchain, Energy sharing, Internet of thing.,

Abstract :

This study introduces a novel system for energy exchange management in a two-layer network of microgrids. In this framework, the first layer facilitates energy exchange among multiple microgrids, while the second layer enables energy sharing among users within each microgrid. The proposed model employs a multi-objective optimization framework based on the Hiking Optimization Algorithm and ensures transaction security and transparency using a multi-blockchain architecture. Simulation results, utilizing real-world data from five microgrids in Guizhou Province, China, reveal significant performance improvements compared to traditional methods. Specifically, the average utility values of users increased from 17.3 to 31.4 (a 96.2% improvement), while those of microgrid operators rose from 28.2 to 38.98 (a 34.58% enhancement). Moreover, the average energy transaction price dropped by up to 45% with increased distributed energy resources. These findings demonstrate the superior performance of the proposed HOA and blockchain-based method in competitive scenarios, offering greater flexibility in pricing and energy distribution. This work establishes an effective approach to sustainable energy management.

References:

[1] M. Tostado-Véliz, D. Icaza-Alvarez, and F. Jurado, “A novel methodology for optimal sizing photovoltaic‐battery systems in smart homes considering grid outages and demand response,” Renewable Energy, vol. 170, pp. 884–896, 2021, doi: 10.1016/j.renene.2020.09.073.
[2] M. Tostado-Véliz, M. Bayat, A. A. Ghadimi, and F. Jurado, “Home energy management in off-grid dwellings: Exploiting flexibility of thermostatically controlled appliances,” J. Clean. Prod., vol. 310, art. no. 127507, 2021, doi: 10.1016/j.jclepro.2021.127507.
[3] M. J. M. Al Essa, “Home energy management of thermostatically controlled loads and photovoltaic‐battery systems,” Energy, vol. 176, pp. 742–752, 2019, doi: 10.1016/j.energy.2018.12.045.
[4] G. Wang, Y. Zhou, Z. Lin, S. Zhu, R. Qiu, Y. Chen, and J. Yan, “Robust energy management through aggregation of flexible resources in multi‐home micro energy hub,” Appl. Energy, vol. 357, art. no. 122471, 2024, doi: 10.1016/j.apenergy.2024.122471.
[5] A. Ajitha, G. Akhilesh, T. Rajkumar, S. Radhika, and S. Goel, “Design and implementation of smart home energy management system for Indian residential sector,” Energy Convers. Manag., vol. 307, art. no. 118369, 2024, doi: 10.1016/j.enconman.2024.118369.
[6] F. Ghanavati, J. C. O. Matias, and G. J. Osório, “Towards sustainable smart cities: Integration of home energy management system for efficient energy utilization,” Sust. Cities Soc., vol. 111, art. no. 105579, 2024, doi: 10.1016/j.scs.2024.105579.
[7] W. Pinthurat, T. Surinkaew, and B. Hredzak, “An overview of reinforcement learning based approaches for smart home energy management systems with energy storages,” Renew. Sustain. Energy Rev., vol. 202, art. no. 114648, 2024, doi: 10.1016/j.rser.2024.114648.
[8] R. Xu, S. Khan, W. Jin, A. N. Khan, Q. W. Khan, S. Lim, and D. H. Kim, “A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment,” Appl. Soft Comput., vol. 161, art. no. 111689, 2024, doi: 10.1016/j.asoc.2024.111689.
[9] M. S. Aliero, K. N. Qureshi, M. F. Pasha, and G. Jeon, “Smart Home Energy Management Systems in Internet of Things networks for green cities demands and services,” Environ. Technol. Innov., vol. 22, art. no. 101443, 2021, doi: 10.1016/j.eti.2021.101443.
[10] M. M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, and A. Anvari-Moghaddam, “Risk-Based stochastic scheduling of resilient microgrids considering demand response programs,” IEEE Syst. J., vol. 15, no. 1, pp. 971–980, Mar. 2021, doi: 10.1109/JSYST.2020.3026142.
[11] T. Khalili, A. Jafari, M. Abapour, and B. Mohammadi-Ivatloo, “Optimal battery technology selection and incentive-based demand response program utilization for reliability improvement of an insular microgrid,” Energy, vol. 169, pp. 92–104, 2019, doi: 10.1016/j.energy.2018.12.024.
[12] F. Shariatzadeh, P. Mandal, and A. K. Srivastava, “Demand response for sustainable energy systems: A review, application and implementation strategy,” Renew. Sustain. Energy Rev., vol. 45, pp. 343–350, 2015, doi: 10.1016/j.rser.2015.01.062.
[13] J. Dong, F. Gao, X. Guan, Q. Zhai, and J. Wu, “Storage-reserve sizing with qualified reliability for connected high renewable penetration micro-grid,” IEEE Trans. Sustain. Energy, vol. 7, no. 2, pp. 732–743, 2016, doi: 10.1109/TSTE.2015.2498599.
[14] D. P. Birnie III, “Optimal battery sizing for storm-resilient photovoltaic power island systems,” Sol. Energy, vol. 109, pp. 165–173, 2014, doi: 10.1016/j.solener.2014.08.016.
[15] M. Tavakoli, F. Shokridehaki, M. F. Akorede, M. Marzband, I. Vechiu, and E. Pouresmaeil, “CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids,” Int. J. Electr. Power Energy Syst., vol. 100, pp. 1–9, 2018, doi: 10.1016/j.ijepes.2018.02.022.
[16] S. Tsianikas, J. Zhou, D. P. Birnie III, and D. W. Coit, “Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems,” Appl. Energy, vol. 256, art. no. 113892, 2019, doi: 10.1016/j.apenergy.2019.113892.
[17] F. Benavente, A. Lundblad, P. E. Campana, Y. Zhang, S. Cabrera, and G. Lindbergh, “Photovoltaic/battery system sizing for rural electrification in Bolivia: Considering the suppressed demand effect,” Appl. Energy, vol. 235, pp. 519–528, 2019, doi: 10.1016/j.apenergy.2018.10.084.
[18] E. Quiles, C. Roldán-Blay, G. Escrivá-Escrivá, and C. Roldán-Porta, “Accurate sizing of residential stand-alone photovoltaic systems considering system reliability,” Sustainability, vol. 12, no. 3, p. 1274, 2020, doi: 10.3390/su12031274.
[19] A. Lagrange, M. de Simón-Martín, A. González-Martínez, S. Bracco, and E. Rosales-Asensio, “Sustainable microgrids with energy storage as a means to increase power resilience in critical facilities: An application to a hospital,” Int. J. Electr. Power Energy Syst., vol. 119, art. no. 105865, 2020, doi: 10.1016/j.ijepes.2020.105865.
[20] NREL, “Renewable, Energy Integration & Optimization,” National Renewable Energy Laboratory, Denver, 2018. [Online]. Available: https://reopt.nrel.gov/tool. [Accessed: Jan. 8, 2021].
[21] S. Xian and X. Feng, “Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems,” Expert Syst. with Appl., vol. 231, art. no. 120482, 2023, doi: 10.1016/j.eswa.2023.120482.
[22] Smart Home Dataset with Weather Information, 2018. [Online]. Available: https://www.kaggle.com/taranvee/smart-home-dataset-with-weather-information. [Accessed: Sep. 19, 2020].
[23] S. O. Oladejo, S. O. Ekwe, and S. Mirjalili, “The Hiking Optimization Algorithm: A novel human-based metaheuristic approach,” Knowl.-Based Syst., vol. 296, art. no. 111880, 2024, doi: 10.1016/j.knosys.2024.111880.
[24] M. Liu, M. Wang, J. Men, and D. Yang, “Microgrid trading game model based on blockchain technology and optimized particle swarm algorithm,” IEEE Access, vol. 8, pp. 225602–225612, 2020, doi: 10.1109/ACCESS.2020.3009697.
[25] M. Goranović, M. Meisel, L. Fotiadis, S. Wilker, A. Treytl, and T. Sauter, “Blockchain applications in microgrids: An overview of current projects and concepts,” in Proc. 43rd Annu. Conf. IEEE Ind. Electron. Soc., Oct. 2017, pp. 6153–6158.
[26] J. Yang, A. Paudel, and H. B. Gooi, “Compensation for power loss by a proof-of-stake consortium blockchain microgrid,” IEEE Trans. Ind. Informat., vol. 17, no. 5, pp. 3253–3262, May 2020, doi: 10.1109/TII.2020.2977213.
[27] J. M. Sukhwani, J. M. Martinez, X. Chang, K. S. Trivedi, and A. Rindos, “Performance modeling of PBFT consensus process for permissioned blockchain network (Hyperledger Fabric),” in Proc. IEEE 36th Symp. Rel. Distrib. Syst. (SRDS), Sep. 2017, pp. 253–255.
[28] N. Koblitz, A. Menezes, and S. A. Vanstone, “The state of elliptic curve cryptography,” Designs Codes Cryptogr., vol. 19, no. 2, pp. 173–193, 2000, doi: 10.1023/A:1010007512298.
[29] V. G. Martínez, L. H. Encinas, and A. Q. Dios, “Security and practical considerations when implementing the elliptic curve integrated encryption scheme,” Cryptologia, vol. 39, no. 3, pp. 244–269, Jul. 2015, doi: 10.1007/s10516-015-9346-6.
[30] W. Tushar et al., “Three-party energy management with distributed energy resources in smart grid,” IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2487–2498, Apr. 2015, doi: 10.1109/TIE.2014.2369123.
[31] L. Norton, R. Simon, H. D. Brereton, and A. Bogden, “Predicting the course of gompertzian growth,” Nature, vol. 264, no. 5586, pp. 542–545, 1976.
[32] S. Xian and X. Feng, “Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems,” Expert Syst. with Appl., vol. 231, art. no. 120482, Jun. 2023, doi: 10.1016/j.eswa.2023.120482.
[33] Y. Gu, S. Shi, and M. Jiang, “Network payment system based on hybrid cross-chain,” U.S. Patent 10,363,623, 2020.
[34] X. Huang, Y. Zhang, D. Li, and L. Han, “A solution for bilayer energy-trading management in microgrids using multiblockchain,” IEEE Internet Things J., vol. 9, no. 15, pp. 13886–13900, Aug. 2022, doi: 10.1109/JIOT.2022.3142815.
[35] A. Mouat and R. Tamagawa, “Docker,” Amazon, Tech. Rep., 2016. [Online]. Available: https://aws.amazon.com/docker/ [Accessed: Sep. 22, 2023].
[36] V. Kulkarni, S. Surwase, K. Pingale, S. Sarage, and A. Karve, “Prediction of disease using machine learning,” IRJET, vol. 7, no. 5, pp. 56–72, May 2020.
[37] M. M. H. Onik, S. Aich, J. Yang, C. Kim, and H. Kim, “Blockchain in healthcare: Challenges and solutions,” in Big Data Analytics for Intelligent Healthcare Management, New York, NY, USA: Academic, 2019, ch. 8, pp. 197–226.
[38] T. Mudarri and S. A. Al-Rabeei, “Security fundamentals: Access control models,” Interdisciplinarity Theory Pract., pp. 1–4, Aug. 2015.