Fuzzy-Integrated Direct Model Predictive Power Control for Enhanced Stability in Renewable Energy Systems

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Original abstract

This paper presents a novel Direct Model Predictive Power Controller (DMPPC) designed to address voltage fluctuations and power quality issues arising from variable load demands and the intermittent behavior of renewable energy sources (RES).The proposed control framework incorporates a bidirectional DC-DC converter integrated with a Battery Energy Storage (BES) system to manage power flow and stabilize the DC bus voltage.A fuzzy logic-based decision-making mechanism dynamically optimizes energy exchange, enhancing system adaptability across varying operating conditions.Furthermore, the AC/DC interlinking converter is regulated in real-time based on both grid constraints and the BES state of charge, enabling efficient energy exchange and stable AC voltage support.Unlike traditional layered control structures, the DMPPC introduces an enhanced cost function that fuses predictive control with fuzzy objectives, thereby offering superior resilience to external disturbances and system nonlinearities.MATLAB/Simulink-based simulations were conducted under diverse loading scenarios, including linear and non-linear loads.Results confirm the superior performance of the proposed controller in minimizing Total Harmonic Distortion (THD), improving dynamic response, and increasing energy conversion efficiency, when compared to conventional control methods such as Sliding Mode Control (SMC).By effectively mitigating voltage instabilities and enhancing power quality, the DMPPC facilitates reliable and robust integration of RES into smart utility grids.These findings affirm its potential for advancing the management and control of renewable-powered hybrid energy systems.

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