INTELLIGENT ENERGY MANAGEMENT IN INTERCONNECTED DC MICROGRIDS USING FUZZY LOGIC AND MODEL PREDICTIVE CONTROL
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This paper presents an intelligent energy management framework for interconnected Autonomous DC Microgrids (ADCMGs) employing a hybrid Fuzzy Logic (FL) and Model Predictive Control (MPC) strategy. Unlike conventional droop-based or centralized communication-dependent schemes, the proposed approach exploits bus voltage deviation and State of Charge (SoC) feedback to make decentralized, anticipatory control decisions without dedicated communication links. The fuzzy inference engine dynamically tunes the MPC weighting coefficients in real time, enabling adaptive power dispatch among photovoltaic (PV) sources, battery storage units, and interconnected microgrids. The proposed controller was validated through MATLAB/Simulink-based real-time simulations under variable irradiation, sudden load transients, and fault injection scenarios. Results demonstrate voltage deviation below 1%, settling time of 115 ms, and overall energy efficiency of 97.3%, outperforming conventional droop, standalone MPC, and standalone fuzzy logic controllers. The framework is particularly relevant for remote and off-grid applications including rural electrification, telecom base stations, and data centers
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Wagchoure, S. J. (2026). Intelligent Energy Management in Interconnected DC Microgrids using Fuzzy Logic and Model Predictive Control. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.094
Wagchoure, Somnath. "Intelligent Energy Management in Interconnected DC Microgrids using Fuzzy Logic and Model Predictive Control." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.094.
Wagchoure, Somnath. "Intelligent Energy Management in Interconnected DC Microgrids using Fuzzy Logic and Model Predictive Control." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.094.
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