Abstract
DC Microgrid has become a new research idea in the last two decades due to its advantage and simplicity over AC microgrid. However, there are still many problems in DC microgrids, like voltage regulation, current sharing, and power and energy management. This paper aims to extract the maximum potential of renewable energy sources by performing the proper energy management system and reducing the stress of storage devices by avoiding overcharging and discharging with the given voltage deviation and current sharing in a DC microgrid. The PV, wind, and battery of the islanded DC microgrid components are connected to the DC grid by their respective converters and thoroughly modeled to examine the system's operation. Solar and wind power generation have been operated in MPPT mode to extract the maximum possible energy resources. Droop control and fuzzy logic controller are modeled in detail for the DC microgrid's best distribution of dispatchable and non-dispatchable energy sources management and voltage regulation. Fuzzy logic-based energy management of dispatchable and non-dispatchable energy units in a DC microgrid with an energy storage system is explored using a combination of theoretical analysis, mathematical modeling, and simulation studies. The methodology has begun by choosing the proper control parameters for energy management after the detailed modeling of the microgrid components. Consequently, methods for managing the internal voltage stability of each converter, managing power flow between the source and load, maintaining voltage regulation, and controlling the battery state of charge are proposed. These methods include internal control, MPPT method, droop control, and fuzzy logic control. The validation of proposed approach is validated using MATLAB/Simulink simulation. Case studies are presented to analyze the result. The result shows the effectiveness of the proposed method. In general, a fuzzy logic-based energy management method combined with other controllers is an effective method for managing energy in a DC microgrid.
Similar content being viewed by others
References
Ishaq S, Khan I, Rahman S, Hussain T, Iqbal A, Elavarasan RM. A review on recent developments in control and optimization of micro grids. Energy Rep. 2022;8:4085–103. https://doi.org/10.1016/j.egyr.2022.01.080.
Kumar M, Singh SN, Srivastava SC. Design and control of smart DC microgrid for integration of renewable energy sources. IEEE Power Energy Soc General Meet. 2012. https://doi.org/10.1109/PESGM.2012.6345018.
Matayoshi H, Kinjo M, Rangarajan SS, Ramanathan GG, Hemeida AM, Senjyu T. Islanding operation scheme for DC microgrid utilizing pseudo droop control of photovoltaic system. Energy Sustain Dev. 2020;55:95–104. https://doi.org/10.1016/j.esd.2020.01.004.
Sun J, Lin W, Hong M, Loparo KA. Voltage regulation of DC-microgrid with PV and battery: a passivity method. IFAC-PapersOnLine. 2019;52(16):753–8. https://doi.org/10.1016/j.ifacol.2019.12.053.
Belkhier Y, Sahri Y, Ullah N, Aziz Al Alahmadi A, Nath Shaw R, “Adaptive Energy Management System of Hybrid Solar/Wind/Battery Power Sources Integrated in DC Microgrid for Smart University Based Fuzzy Modified Super Twisting Algorithm.” [Online]. Available: https://ssrn.com/abstract=3875174
Kumar J, Agarwal A, Singh N. “Design, operation and control of a vast DC microgrid for integration of renewable energy sources.” Renew Energy Focus. 2020;34:17–36. https://doi.org/10.1016/j.ref.2020.05.001.
Kotb KM, Elmorshedy MF, Salama HS, Dán A. Enriching the stability of solar/wind DC microgrids using battery and superconducting magnetic energy storage based fuzzy logic control. J Energy Storage. 2022;45:103751. https://doi.org/10.1016/j.est.2021.103751.
M. F. Zia, M. Nasir, E. Elbouchikhi, M. Benbouzid, J. C. Vasquez, and J. M. Guerrero, 2020 “Energy management system for an islanded renewables-based DC microgrid,” in 2020 2nd International Conference on Smart Power and Internet Energy Systems SPIES 2020, pp. 543–547.
Singh P, Lather JS. Power management and control of a grid-independent DC microgrid with hybrid energy storage system. Sustain Energy Technol Assess. 2021;43:100924. https://doi.org/10.1016/j.seta.2020.100924.
Tenti P, Caldognetto T. Master/slave power-based control of low-voltage microgrids. Microgrid Adv Control Methods Renew Energy Syst Integr. 2017. https://doi.org/10.1016/B978-0-08-101753-1.00004-8.
Peyghami S, Mokhtari H, Blaabjerg F. Hierarchical power sharing control in DC microgrids. In: Microgrid advanced control methods and renewable energy system integration. Netherlands: Elsevier; 2017. p. 63–100.
Bracale A, Caramia P, Carpinelli G, Mancini E, Mottola F. Optimal control strategy of a DC micro grid. Int J Electr Power Energy Syst. 2015;67:25–38. https://doi.org/10.1016/j.ijepes.2014.11.003.
Li M, Fan J, Qiao L. Adaptive droop control of a multibus dc microgrid based on consensus algorithm. Math Probl Eng. 2021;2021:1. https://doi.org/10.1155/2021/6634278.
Benahmed S, Riedinger P, Pierfederici S. Distributed-based Integral action for current sharing and average voltage regulation in DC microgrids. IFAC-PapersOnLine. 2021;54(9):52–9. https://doi.org/10.1016/j.ifacol.2021.06.142.
Prathyush M, Jasmin EA. “Fuzzy logic based energy management system design for AC Microgrid.” Proc Int Conf Inven Commun Comput Technol. 2018;8(10):411–4. https://doi.org/10.1109/ICICCT.2018.8473317.
Zhao H, Wu Q, Wang C, Cheng L, Rasmussen CN. Fuzzy logic based coordinated control of battery energy storage system and dispatchable distributed generation for microgrid. J Mod Power Syst Clean Energy. 2015;3(3):422–8. https://doi.org/10.1007/s40565-015-0119-x.
Arcos-Aviles D, Pascual J, Marroyo L, Sanchis P, Guinjoan F. Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans Smart Grid. 2018;9(2):530–43. https://doi.org/10.1109/TSG.2016.2555245.
Fathy A, Ferahtia S, Rezk H, Yousri D, Abdelkareem MA, Olabi AG. Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid. Energy. 2022;247:123447. https://doi.org/10.1016/j.energy.2022.123447.
Fagundes TA, Fuzato GHF, Ferreira PGB, Biczkowski M, Machado RQ. “Fuzzy controller for energy management and soc equalization in dc microgrids powered by fuel cell and energy storage units.” IEEE J Emerg Sel Top Ind Electron. 2021;3(1):90–100. https://doi.org/10.1109/jestie.2021.3088419.
Sinha S, Bajpai P. Power management of hybrid energy storage system in a standalone DC microgrid. J Energy Storage. 2020;30:101523. https://doi.org/10.1016/j.est.2020.101523.
Vivas FJ, Segura F, Andújar JM. Fuzzy logic-based energy management system for grid-connected residential DC microgrids with multi-stack fuel cell systems: a multi-objective approach. Sustain Energy Grids Netw. 2022;32:100909. https://doi.org/10.1016/j.segan.2022.100909.
Chen X, Wang L, Sun H, Chen Y. Fuzzy logic based adaptive droop control in multiterminal HVDC for wind power integration. IEEE Trans Energy Convers. 2017;32(3):1200–8. https://doi.org/10.1109/TEC.2017.2697967.
Abadi SAGK, Habibi SI, Khalili T, Bidram A. A model predictive control strategy for performance improvement of hybrid energy storage systems in DC microgrids. IEEE Access. 2022;10:25400–21. https://doi.org/10.1109/ACCESS.2022.3155668.
Cingoz F, Elrayyah A, Sozer Y. Optimized settings of droop parameters using stochastic load modeling for effective DC microgrids operation. IEEE Trans Ind Appl. 2017;53(2):1358–71. https://doi.org/10.1109/TIA.2016.2633538.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors state that they do not have any conflicts of interest.
Compliance with Ethical Standards
The study described here did not involve human or animal participation.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited byOmer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Taye, B.A., Choudhury, N.B.D. Fuzzy Logic-Based Energy Management of Dispatchable and Non-dispatchable Energy Units in DC Microgrid with Energy Storage System. SN COMPUT. SCI. 4, 576 (2023). https://doi.org/10.1007/s42979-023-02027-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02027-1