Optimum Energy Management in The Radial Distribution Network by Considering Multiple Microgrids, Uncertainties, and Resilience Index using the Modified Harris Hawk Optimization Algorithm

marzieh poshtyafteh ¹ ( Department of electrical engineering, Dezful Brancb, Islamic Azad University, Dezful, Iran )
Hassan Barati ² ( Department of electrical engineering, Dezful Brancb, Islamic Azad University, Dezful, Iran )
Ali Darvish Falehi ³ ( Department of Electrical Engineering, Shadegan Branch, Islamic Azad University, Shadegan, Iran )

Submited date : 2024-06-28 Accepted date : 2024-10-20

Keywords: Multiple-Microgrids, Multi-objective energy management, Uncertainties, Modified Harris Hawk Optimization, Distribution Network, Resilience, Reconfiguration,

Abstract :

In this paper, an optimal energy management for a multiple microgrid (MMG) connected to a distribution network (DN) is proposed, in which various objective functions including network cost, pollutant reduction and losses, and distribution network resilience are considered. Also, the effect of placement of distributed generation sources along with the reconfiguration of the distribution network in the optimization process with the aim of reducing losses, increasing reliability and resilience are considered. Uncertainties are formulated using Information Gap Decision Theory (IGDT). The decision variables, including the location of resources and microgrids, installation capacity, power factor, and uncertainty radius, have been optimally determined using the Modified Harris Hawk Optimization algorithm (MHHO) and the CPLEX solver. In the MHHO algorithm, the rabbit energy parameter (E) changes dynamically with the behavior and value of the objective function. Finally, the proposed method on the IEEE 33-bus Radial Distribution System in the first stage in a 24-hour time horizon including three micro-grids with different renewable energy sources to determine the structure of the network due to the buses connecting micro-grids and scattered sources by the placement algorithm and in the next stage in time Different resilience indicators are investigated due to the disconnection of the distribution network with the upstream network. The simulation results show the MHHO algorithm's optimal performance in placing microgrids, distributed generation sources, and network reconfiguration to improve the optimal energy management and resilience index.

References:

[1] H. Farzin, M. Fotuhi-firuzabad, and M. Moeini-aghtaie, “Enhancing Power System Resilience through Hierarchical Outage Management in Multi-Microgrids,” vol. 3053, no. c, 2016.
[2] مرتضی حق شناس، رحمت اله هوشمند، و مهدی قلی پور، “برنامه¬ریزی تصادفی بهبود تاب¬آوری سیستم¬های توزیع انرژی الکتریکی در مقابل طوفان¬های گرد و غبار شدید،” مهندسی برق و مهندسی کامپیوتر ایران - الف مهندسی برق، vol. 20، no. 2 ، pp. 108–120، 1401
[3] J. Wang, N. Xie, W. Wu, D. Han, C. Wang, and B. Zhu, “Resilience enhancement strategy using microgrids in distribution network,” Glob. Energy Interconnect., vol. 1, no. 5, pp. 537–543, 2018.
[4] Y. Bian and Z. Bie, “Multi-Microgrids for Resilience Multi-Microgrids for Resilience Multi-Microgrids for Resilience Multi-Microgrids for Hazards Multi-Microgrids for Frequent,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 61–66.
[5] Z. Li, M. Shahidehpour, F. Aminifar, A. Alabdulwahab, and Y. Al-Turki, “Networked Microgrids for Enhancing the Power System Resilience,” Proc. IEEE, vol. 105, no. 7, pp. 1289–1310, 2017. [6] H. Karimi and S. Jadid, “Response Programs : A Stochastic Multi-Objective Framework,” Energy, 2020.
[7] C. Chen, J. Wang, F. Qiu, and D. Zhao, “Resilient Distribution System by Microgrids Formation after Natural Disasters,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 958–966, 2016.
[8] S. Yao, P. Wang, and T. Zhao, “Transportable Energy Storage for More Resilient Distribution Systems with Multiple Microgrids,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3331–3341, 2019. [9] Z. Wang and J. Wang, “Self-Healing Resilient Distribution Systems Based on Sectionalization into Microgrids,” IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3139–3149, 2015.
[10] F. H. Aghdam, S. Ghaemi, and N. T. Kalantari, “Evaluation of loss minimization on the energy management of multi-microgrid based smart distribution network in the presence of emission constraints and clean productions,” J. Clean. Prod., 2018.
[11] M. Choobineh, S. Member, S. Mohagheghi, and S. Member, “Robust Optimal Energy Pricing and Dispatch for a Multi-Microgrid Industrial Park Operating Based on Just-In-Time Strategy,” IEEE Trans. Ind. Appl., vol. PP, no. c, p. 1, 2019.
[12] J. W. Z. Wang, B. Chen, “Decentralized energy management system for network microgrids in grid-connected and island modes,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 1097–1105, 2016.
[13] G. G. Q. Jiang, M. Xue, “Energy management of microgrid in grid-connected and stand-alone modes,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3380–3389, 2013.
[14] S. A. Arefifar, M. Ordonez, and Y. A. I. Mohamed, “Energy Management in Multi-Microgrid Systems — Development and Assessment,” vol. 8950, no. c, pp. 1–12, 2016.
[15] Jordehi, A.R., Mansouri, S.A., Tostado-Véliz, M., Ahmarinejad, A., Jurado, F.: Resilience-oriented placement of multi-carrier microgrids in power systems with switchable transmission lines. Int. J. Hydrogen Energy 50, 175–185 (2024). https://doi.org/10.1016/j.ijhydene.2023.07.277
[16] Alobaidi, A.H., Khodayar, M.E., Shahidehpour, M.: Decentralized energy management for unbalanced networked microgrids with uncertainty. IET Gener. Transm. Distrib. 15, 1922–1938 (2021). https://doi.org/10.1049/ gtd2.12145
[17] R. N. H. Haddadian, “multi-microgrids approach for design and operation of future distribution networks based on novel technical indices,” Appl. Energy, vol. 185, pp. 650–663, 2017.
[18] T. Ding, Y. Lin, Z. Bie, and C. Chen, “A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and to,” Appl. Energy, vol. 199, no. August, pp. 205–216, 2017.
[19] C. Chen, J. Wang, S. Member, and F. Qiu, “Resilient Distribution System by Microgrids Formation After Natural Disasters,” pp. 1–9, 2015.
[20] S. Bagheri, H. Talebi, and A. Fereidunian, “Resilient , Comfort and Economic Operation of Smart Nanogrid,” 2008.
[21] T. K. ] Liu G, Xu Y, “Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization,” IEEE Trans Smart Grid, vol. 7, pp. 227–237, 2016.
[22] S. Nikkhah and A. Rabiee, “Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner ’ s and DisCo ’ s perspectives,” vol. 132, 2019.
[23] E. Hooshmand and A. Rabiee, “Energy management in distribution systems , considering the impact of reconfiguration, RESs, ESSs, and DR : A trade-off between cost and reliability,” Renew. Energy, vol. 139, pp. 346–358, 2019.
[24] Karimi, H., Jadid, S.: Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework. Energy 195, 116992 (2020). https://doi.org/10.1016/j.energy. 2020.116992.
[25] N. Rezaei, A. Ahmadi, A. Khazali, and J. Aghaei, “Multiobjective Risk-Constrained Optimal Bidding Strategy of Smart Microgrids: An IGDT-Based Normal Boundary Intersection Approach,” IEEE Trans. Ind. Informatics, vol. 15, no. 3, pp. 1532–1543, 2019.
[26] N. Rezaei, A. Ahmadi, A. H. Khazali, and J. M. Guerrero, “Energy and Frequency Hierarchical Management System Using Information Gap Decision Theory for Islanded Microgrids,” IEEE Trans. Ind. Electron., vol. 65, no. 10, pp. 7921–7932, 2018.
[27] M. Nasr and S. H. Hosseinian, “Risk-Averse Energy Management System for Isolated Microgrids Considering Risk-Averse Energy Management System for Isolated Microgrids Considering Generation and Demand Uncertainties Based on Information Gap Decision Theory,” no. January, 2019.
[28] K. Z. Xuemei Dai 1, Ying Wang 1, Shengchun Yang 2, “IGDT-based economic dispatch considering the uncertainty of wind and demand response,” IET, 2019.
[29] A. Khazali, N. Rezaei, A. Ahmadi, and B. Hredzak, “Information Gap Decision Theory Based Preventive/Corrective Voltage Control for Smart Power Systems With High Wind Penetration,” IEEE Trans. Ind. Informatics, vol. 14, no. 10, pp. 4385–4394, 2018.
[30] مرتضی حق شناس، رحمت اله هوشمند، و مهدی قلی پور، “تخصیص بهینه منابع تجدیدپذیر در شبکه¬های توزیع با درنظرگرفتن عدم قطعیت براساس تئوری تصمیم¬گیری شکاف اطلاعاتی با استفاده از الگوریتم اجتماع سالپ بهبودیافته” مجله مدل¬سازی در مهندسی ، vol. 20، no. 68 ، pp. 207–223، 1401.
[31] M. Poshtyafteh, H. Barati, and A. D. Falehi “Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm." IET Generation, Transmission & Distribution, 2024, vol 18, no. 4, pp.809-833, 2024.
[32] Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: Algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019). https://doi.org/10.1016/j.future.2019.02.028