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Optimization of energy management in Malaysian microgrids using fuzzy logic-based EMS scheduling controller

. 2025 Jan 06 ; 15 (1) : 995. [epub] 20250106

Status PubMed-not-MEDLINE Language English Country England, Great Britain Media electronic

Document type Journal Article

Links

PubMed 39762244
PubMed Central PMC11704056
DOI 10.1038/s41598-024-82360-4
PII: 10.1038/s41598-024-82360-4
Knihovny.cz E-resources

The microgrid (MG) faces significant security issues due to the two-way power and information flow. Integrating an Energy Management System (EMS) to balance energy supply and demand in Malaysian microgrids, this study designs a Fuzzy Logic Controller (FLC) that considers intermittent renewable sources and fluctuating demand patterns. FLC offers a flexible solution to energy scheduling effectively assessed by MATLAB/Simulink simulations. The microgrid consists of PV, battery, grid, and load. A Maximum Power Point Tracking (MPPT) controller helps to extract the maximum PV output and manages the power storage by providing or absorbing excess power. System analysis is performed by observing the State of Charge (SoC)of the battery and output power for each source. The grid supplies additional power if the battery and PV fail to meet the load demand. Total Harmonic Distortion (THD) analysis compares the performance of the Proportional-Integral Controller (PIC) and FLC. The results show that the PI controller design reduces the THD in the current signal, while FLC does not reduce the THD of the grid current when used in the EMS. However, FLC offers better control over the battery's SOC, effectively preventing overcharging and over-discharging. While PI reduces THD, FLC provides superior SOC control in a system comprising PV, battery, grid, and load. The findings demonstrate that the output current is zero when the SOC is higher than 80% or lower than 20%, signifying that no charging or discharging takes place to avoid overcharging and over-discharging. The third goal was accomplished by comparing and confirming that the grid current's THD for the EMS designed with both the PI Controller and the FLC is maintained below 5%, following the IEEE 519 harmonic standard, using the THD block in MATLAB Simulink. This analysis highlights FLC's potential to address demand-supply mismatches and renewable energy variability, which is crucial for optimizing microgrid performance.

See more in PubMed

Chen, Y., Wu, Y., Song, C. & Chen, Y. Design and implementation of energy management system with fuzzy control for DC microgrid systems. IEEE Trans. Power Electron.28(4), 1563–1570. 10.1109/tpel.2012.2210446 (2013).

Olaleye, T. A., Olatomiwa, L., Longe, O. M. & Jack, K. E. An energy management scheme for hybrid energy system using fuzzy logic controller. Nigerian J. Technological Dev. 20(1). 10.4314/njtd.v20i1.1292 (2023).

Balasubramanyam, P. & Sood, V. K. A novel hybrid swarm intelligence and cuckoo search based microgrid EMS for optimal energy scheduling. Distributed Generation Alternative Energy Journal38(04), 1119–1148. 10.13052/dgaej2156-3306.3843 (2023).

Wang, S., Tan, Q., Ding, X. & Li, J. Efficient microgrid energy management with neural-fuzzy optimization. Int. J. Hydrog. Energy64, 269–281. 10.1016/j.ijhydene.2024.03.291 (2024).

Zhang, H., Ma, Y., Yuan, K., Khayatnezhad, M. & Ghadimi, N. Efficient design of energy microgrid management system: a promoted Remora optimization algorithm-based approach. Heliyon10(1), e23394. 10.1016/j.heliyon.2023.e23394 (2024). PubMed PMC

Vivas, F. J., Segura, F. & Andújar, J. M. Fuzzy logic-based energy management system for grid-connected residential DC microgrids with multi-stack fuel cell systems: A multi-objective approach. Sustainable Energy Grids Networks32, 100909. 10.1016/j.segan.2022.100909 (2022).

Patil, A. & Patil, S. R. Energy management system for microgrid system using improved Grey Wolf optimization algorithm. Int. J. Recent. Innov. Trends Comput. Communication. 11(5), 266–272. 10.17762/ijritcc.v11i5.6613 (2023).

Phani Ranga Raja, B., Chinni Krishna, A., Pavani, M., Naga Ramesh, V. & Sathvik, V. L. S. Fuzzy logic controller based efficiency power management of grid operated microgrid. IJFMR 5(2). 10.36948/ijfmr.2023.v05i02.2052 (2023).

Ibrahim, O. et al. Development of fuzzy logic-based demand-side energy management system for hybrid energy sources. Energy Conversion and Management: X18, 100354. 10.1016/j.ecmx.2023.100354 (2023).

Basantes, J. A., Paredes, D. E., Llanos, J. R., Ortiz, D. E. & Burgos, C. D. Energy management system (EMS) based on model predictive control (MPC) for an isolated DC microgrid. Energies16, 2912. 10.3390/en16062912 (2023).

García, P., Torreglosa, J., Fernández, L. & Jurado, F. Optimal energy management system for stand-alone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic. Int. J. Hydrog. Energy. 38(33), 14146–14158. 10.1016/j.ijhydene.2013.08.106 (2013).

Hosseinzadeh, M. & Salmasi, F. Power management of an isolated hybrid AC/DC micro-grid with fuzzy control of battery banks. IET Renew. Power Gener.9(5), 484–493. 10.1049/iet-rpg.2014.0271 (2015).

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 Grid9(2), 530–543. 10.1109/tsg.2016.2555245 (2018).

Kyriakarakos, G., Dounis, A., Arvanitis, K. & Papadakis, G. A fuzzy logic energy management system for polygeneration microgrids. Renew. Energy41, 315–327. 10.1016/j.renene.2011.11.019 (2012).

Zhang, Y., Liu, Y. & Kang, W. M2SUM: Multi-granularity scale-adaptive video summarizer towards informative context representation learning. In ICASSP 2024–2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 3410–3414 (2024). 10.1109/ICASSP48485.2024.10446527.

Zhang, Y., Liu, T., Yu, P., Wang, S. & Tao, R. SFSANet: Multiscale object detection in remote sensing image based on semantic fusion and scale adaptability. IEEE Transactions on Geoscience and Remote Sensing62, 1–10. 10.1109/TGRS.2024.3387572 (2024).

Zhang, Y., Wu, C., Guo, W., Zhang, T. & Li, W. CFANet: Efficient detection of UAV image based on cross-layer feature aggregation. IEEE Transactions on Geoscience and Remote Sensing61, 1–11. 10.1109/TGRS.2023.3273314 (2023).

Zhang, Y., Wu, C., Zhang, T. & Zheng, Y. Full-scale feature aggregation and grouping feature reconstruction-based UAV image target detection. IEEE Transactions on Geoscience and Remote Sensing62, 1–11. 10.1109/TGRS.2024.3392794 (2024).

Peninsular Malaysia Electricity Supply Industry Outlook 2019. Putrajaya: Suruhanjaya Tenaga (Energy Commission, 2019). https://www.st.gov.my/en/contents/files/download/106/Peninsular_Malaysia_Electricity_Supply_Industry_Outlook_2019_compressed.pdf. Accessed 9 Dec 2024.

Report on Peninsular Malaysia Generation Development Plan. St.gov.my, 2021 (2020); accessed on 1 October 2021. https://www.st.gov.my/ms/contents/files/download/169/Report_on_Peninsular_Malaysia_Generation_Development_Plan_2020_(2021-2039)-FINAL1.pdf.

Abdul Latif, S. et al. The trend and status of energy resources and greenhouse gas emissions in the Malaysia power generation mix. Energies14(8), 2200. 10.3390/en14082200 (2021).

Roslan, M., Hannan, M., Ker, P. & Uddin, M. Microgrid control methods toward achieving sustainable energy management. Appl. Energy240, 583–607. 10.1016/j.apenergy.2019.02.070 (2019).

Mohammed Shakeel, F. Adaptive hierarchical control in V2G integrated micro-grids; accessed on 02 August 2022. Prism.ucalgary.ca (2022). https://prism.ucalgary.ca/handle/1880/113141.

Unamuno, E. & Barrena, J. A. Hybrid ac/DC microgrids – part II: Review and classification of control strategies. Renew. Sustain. Energy Rev.52, 1123–1134 (2015).

Zhao, H., Hong, M., Lin, W. & Loparo, K. A. Voltage and frequency regulation of microgrid with battery energy storage systems. IEEE Trans. Smart Grid3053(c), 1–12 (2017).

De Muro, A. G., Jimeno, J. & Anduaga, J. Architecture of a microgrid energy management system. Eur. Trans. Electr. Power21(2), 1142–1158 (2011).

Hassan, M. A. & Abido, M. A. Optimal design of microgrids in autonomous and griDConnected modes using particle swarm optimization. IEEE Trans. Power Electron.26(3), 755–769 (2011).

Hamad, A. A. & El-Saadany, E. F. Multi-agent supervisory control for optimal economic dispatch in DC microgrids. Sustain. Cities Soc.27, 129–136 (2016).

Alkahtani, A. et al. Power quality in microgrids including supraharmonics: Issues, standards, and mitigations. IEEE Access8, 127104–127122. 10.1109/access.2020.3008042 (2020).

Hannan, M., Hoque, M., Mohamed, A. & Ayob, A. Review of energy storage systems for electric vehicle applications: Issues and challenges. Renew. Sustain. Energy Rev.69, 771–789. 10.1016/j.rser.2016.11.171 (2017).

Momoh, J. Smart grid (Wiley-Blackwell, Berlin, 2012).

Arun, S. L. & Selvan, M. P. Intelligent residential energy management for dynamic demand response in smart buildings. IEEE Syst. J.99, 1–12 (2017).

Dunn, B., Kamath, H. & Tarascon, J. M. Electrical energy storage for the grid: A battery of choices. Science334(6058), 928–935 (2011). PubMed

Salas, J. Mpc-based energy management system for hybrid renewable energies, Dialnet (2022); accessed on 03 August 2022; https://dialnet.unirioja.es/servlet/tesis?codigo=295772.

Sharma, K. A case study on application of fuzzy logic based controller for peak load shaving in a typical household’s per day electricity consumption, ScholarWorks@GVSU (2022), accessed on 04 August 2022; https://scholarworks.gvsu.edu/theses/900/.

Han, Y., Zhang, K., Li, H., Coelho, E. A. & Guerrero, J. M. MAS-based distributed coordinated control and optimization in microgrid and microgrid clusters: A comprehensive overview. IEEE Trans Power Electron 8993(c) (2017).

Dou, C., Lv, M., Zhao, T., Ji, Y. & Li, H. Decentralised coordinated control of microgrid based on multi-agent system. IET Gener Transm Distrib.9(16), 2474–2484 (2015).

Meng, L. et al. Microgrid supervisory controllers and energy management systems: A literature review. Renew. Sustain. Energy Rev.60, 1263–1273 (2016).

Rana, M. J. & Abido, M. A. Energy management in DC microgrid with energy storage and model predictive controlled AC–DC converter. IET Gener Transm Distrib.11(15), 3694–3702 (2017).

Diaz-Mendez, S. E., Patiño-Carachure, C. & Herrera-Castillo, J. A. Reducing the energy consumption of an earth–air heat exchanger with a PID control system. Energy Convers. Manage.77, 1–6 (2014).

Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P. & Guinjoan, F. Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Transactions on Smart Grid3053(c), 1 (2016).

Kolokotsa, D., Tsiavos, D., Stavrakakis, G., Kalaitzakis, K. & Antonidakis, E. Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal-visual comfort and indoor air quality satisfaction. Energy Build.33(6), 531–543 (2001).

Abdulla, N. Application of artificial neural networks for prediction of concrete properties. Magazine of Civil Engineering110(2), 11007. 10.34910/MCE.110.7 (2022).

Parvin, K. et al. Intelligent controllers and optimization algorithms for building energy management towards achieving sustainable development: Challenges and prospects. IEEE Access9, 41577–41602. 10.1109/access.2021.3065087 (2021).

Chung, J. et al. Home-legacy device intelligent control using ANFIS with data regeneration and resampling. In Proc. 15th Int. Conf. Control, Autom. Syst. (ICCAS) 1294–1296 (2015).

Irmak, E. & Güler, N. Application of a high efficient voltage regulation system with MPPT algorithm. Int. J. Electr. Power Energy Syst.44(1), 703–712. 10.1016/j.ijepes.2012.08.011 (2013).

Irmak, E. & Güler, N. A model predictive control-based hybrid MPPT method for boost converters. Int. J. Electron.107(1), 1–16 (2019).

Subramani, G. et al. Techno-economic optimization of grid-connected photovoltaic (PV) and battery systems based on maximum demand reduction (MDRed) modelling in Malaysia. Energies12(18), 3531 (2019).

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