Adaptive Fuzzy Load Prioritization for Energy Management in Hybrid Wind-Solar Microgrids
DOI:
https://doi.org/10.59247/jfsc.v4i2.401Keywords:
Artificial Intelligence, Fuzzy Logic Control, Hybrid Microgrid, Load Prioritization, Renewable Energy, Wind PowerAbstract
Hybrid renewable microgrids are increasingly becoming popular among renewable energy generation schemes for small-scale power. However, the intermittent nature of both these sources leads to frequent power imbalances between generation and load demand. Conventional control strategies often rely on battery storage for compensation in these cases or employ advanced controls. This paper presents an adaptive Fuzzy logic-based load prioritization strategy for better energy management in such microgrids. The proposed method adjusts the non-critical loads dynamically based on real-time power availability instead of relying on storage only. A fuzzy-based decision technique is used to determine load shedding levels using inputs such as power mismatch, system voltage deviation, and state-of-charge (SOC) of the battery. The proposed control improves system stability and reduces dependency on battery storage. Suitable simulations backed by laboratory-scale experiments demonstrate that the proposed method improves voltage regulation and minimizes load disruption. It significantly reduces voltage deviation by almost 66%, battery current by 43%, and improves power utilization to 97% compared to conventional control systems.
References
M. R. Patel, Wind and solar power systems: Design, analysis, and operation, second edition, 3rd ed. UK: CRC Press, 2005. https://www.doi.org/10.2134/jeq2006.0001br.
A. Chatterjee, “Investigation of self-excited induction generator for supporting domestic loads and its extension to a microgrid,” Energy Storage and Conversion, vol. 2, no. 2, p. 1321, 2024, https://www.doi.org/10.59400/esc.v2i2.1321.
A. Chatterjee, “Wind-PV based Generation with Smart Control Suitable for Grid-isolated Critical Loads in Onshore India,” Journal of The Institution of Engineers (India): Series B, vol. 106, no. 3, pp. 1021–1031, 2025, https://www.doi.org/10.1007/s40031-022-00827-2.
A. Chatterjee, S. Ghosh, and A. Mitra, “Hybrid generation scheme for delivering irrigation loads and other critical loads with smart IoT based control,” IEEE Transactions on Industry Applications, vol. 60, no. 1, pp. 828–837, 2024, https://www.doi.org/10.1109/TIA.2023.3322114.
A. Chatterjee, “Analysis of a wind-PV hybrid system with smart control for grid-secluded critical loads in Onshore Indian area,” in Lecture Notes in Electrical Engineering, in Lecture Notes Elect. Eng, vol. 815, Singapore: Springer, pp. 495–503, 2022, https://www.doi.org/10.1007/978-981-16-7011-4_47.
W. E. Vanco, F. B. Silva, C. M. R. De Oliveira, J. R. B. A. Monteiro, and J. M. M. De Oliveira, “A proposal of expansion and implementation in isolated generation systems using self-excited induction generator with synchronous generator,” IEEE Access, vol. 7, pp. 117188–117195, 2019, https://www.doi.org/10.1109/ACCESS.2019.2937229.
A. Chatterjee, “Analysis of a self-excited induction generator with fuzzy PI controller for supporting domestic loads in a microgrid,” Journal of Fuzzy Systems and Control, vol. 1, no. 2, pp. 61–65, 2023, https://www.doi.org/10.59247/jfsc.v1i2.42.
A. Chatterjee and A. K. Mukherjee, “IoT-enabled supervisory control of wind-driven induction generators using interval type-2 fuzzy logic for dynamic load management in off-grid systems,” International Journal of Smart Grid and Clean Energy, vol. 14, no. 2, pp. 14–24, 2025, https://www.doi.org/10.12720/sgce.14.2.14-24.
M. Mohamed et al., “Improved control approaches for dynamic voltage restoration in doubly fed induction generator-based wind turbines,” IEEE Access, vol. 14, pp. 10245–10261, 2026, https://www.doi.org/10.1109/ACCESS.2026.3654897.
A. Chatterjee and S. Ghosh, “Grid-isolated hybrid generation for critical loads supported by electric spring system with smart load control,” IEEE Internet of Things Journal, vol. 12, no. 21, pp. 45043–45053, 2025, https://www.doi.org/10.1109/JIOT.2025.3601508.
C. W. Gellings, “Evolving practice of demand-side management,” Journal of Modern Power Systems and Clean Energy, vol. 5, no. 1, pp. 1–9, 2017, https://www.doi.org/10.1007/s40565-016-0252-1.
T. Kitamura, Y. Wang, and J. Kondoh, “autonomous load control for frequency regulation in low-inertia systems: an experimental demonstration,” IEEE Access, vol. 14, pp. 35721–35741, 2026, https://www.doi.org/10.1109/ACCESS.2026.3670359.
M. Saha, S. S. Thakur, A. Bhattacharya, and B. Dey, “Profit maximization of distribution network stakeholders facilitating EV demands with adaptive demand side management policies,” IEEE Open Journal of Industry Applications, vol. 7, pp. 65–78, 2025, https://www.doi.org/10.1109/OJIA.2025.3647633.
P. Mondal et al., “Dynamic pricing with Bi-LSTM load forecasting: a path towards grid stability,” IEEE Open Journal of Industry Applications, vol. 7, pp. 303–311, 2026, https://www.doi.org/10.1109/OJIA.2026.3663645.
D. Qiu, W. He, J. Zi, B. Zhang, Y. Chen, and Y. Zeng, “A battery-free and noncooperative smart load based on back-to-back converter,” IEEE Transactions on Industrial Electronics, vol. 73, no. 3, pp. 4110–4121, 2025, https://www.doi.org/10.1109/TIE.2025.3621666.
L. Yin, Y. Ye, and X. Zhang, “deep learning-based approach for accelerated economic dispatch in hierarchical distributed power systems with internet of things,” IEEE Internet of Things Journal, vol. 12, no. 23, pp. 51542–51557, 2025, https://www.doi.org/10.1109/JIOT.2025.3614203.
A. Chatterjee, “Wind power forecasting using type-2 fuzzy control and its optimization based on artificial neural network for small scale wind power,” Journal of Fuzzy Systems and Control, vol. 2, no. 3, pp. 170–175, 2024, https://www.doi.org/10.59247/jfsc.v2i3.259.
A. Chatterjee, “Wind power generation for isolated loads with IoT-based smart load controller,” Journal of Fuzzy Systems and Control, vol. 2, no. 2, pp. 92–96, 2024, https://www.doi.org/10.59247/jfsc.v2i2.210.
A. N. Akpolat et al., “Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control,” IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 999–1010, 2022, https://www.doi.org/10.1109/TEC.2021.3118664.
A. Chatterjee and S. Ghosh, “PV based Isolated Irrigation System with its Smart IoT Control in Remote Indian Area,” in 2020 International Conference on Computer, Electrical and Communication Engineering, ICCECE 2020, Kolkata, India, pp. 1–5, 2020, https://www.doi.org/10.1109/ICCECE48148.2020.9223110.
S. Ghosh, A. Chatterjee, and D. Chatterjee, “Extraction of statistical features for type-2 fuzzy NILM with IoT enabled control in a smart home,” Expert Systems with Applications, vol. 212, p. 118750, 2023, https://www.doi.org/10.1016/j.eswa.2022.118750.
A. Chatterjee, S. Paul, and B. Ganguly, “Multi-Objective Energy Management of a Smart Home in Real Time Environment,” IEEE Transactions on Industry Applications, vol. 59, no. 1, pp. 138–147, 2023, https://www.doi.org/10.1109/TIA.2022.3209170.
S. Ghosh, D. Manna, A. Chatterjee, and D. Chatterjee, “Remote appliance load monitoring and identification in a modern residential system with smart meter data,” IEEE Sensors Journal, vol. 21, no. 4, pp. 5082–5090, 2021, https://www.doi.org/10.1109/JSEN.2020.3035057.
F. J. Vivas, F. Segura, and J. M. Andújar, “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 and Networks, vol. 32, p. 100909, 2022, https://www.doi.org/10.1016/j.segan.2022.100909.
Ş. Ataç and M. Güçyetmez, “Fuzzy Logic-Based Load Analysis of Hybrid Microgrid System Including Fuel Cell, Wind Turbine, Wave, and Photovoltaic Energies,” Fuel Cells, vol. 26, no. 2, 2026, https://www.doi.org/10.1002/fuce.70093.
M. Steczek, A. Chatterjee, and D. Chatterjee, “Optimisation of current harmonics for threelevel VSI based induction motor drive suitable for traction application,” IET Power Electronics, vol. 11, no. 9, pp. 1529–1536, 2018, https://www.doi.org/10.1049/iet-pel.2017.0181.
G. V. Ramos, D. A. D. L. Brandao, T. M. Parreiras, S. M. Silva, and B. J. C. Filho, “A Zero Harmonic Distortion Grid-Forming Converter for Medium Voltage Islanded Microgrids,” IEEE Transactions on Industry Applications, vol. 61, no. 1, pp. 809–820, 2025, https://www.doi.org/10.1109/TIA.2024.3462913.
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Arunava Chatterjee
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
