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This paper presents a Renewable Energy Monitoring System (REMS) for a hybrid microgrid using a Model Predictive Control (MPC) technique implemented in MATLAB/Simulink. The proposed microgrid integrates a solar photovoltaic (PV) array, wind turbine with Permanent Magnet Synchronous Generator (PMSG), Battery Energy Storage System (BESS), DC-link, and grid-connected inverter for efficient renewable power management. The developed MPC controller continuously predicts system behavior and regulates the power flow between renewable sources, storage units, and load demand under varying operating conditions. A real-time monitoring framework is designed to observe renewable generation, DC-bus performance, inverter output, battery charging/discharging condition, and grid power exchange. Simulation results obtained from the developed Simulink model show stable operation under variable solar irradiance and wind speed conditions. The proposed MPC-based system achieves improved DC-link voltage regulation, reduced power fluctuations at the Point of Common Coupling (PCC), faster transient response, and enhanced renewable energy utilization compared to conventional control approaches. The obtained output waveforms and grid power analysis also demonstrate effective load sharing and reliable microgrid operation in both grid-connected and standalone modes. The overall results confirm that the proposed REMS with MPC provides an efficient, intelligent, and scalable solution for hybrid renewable microgrid applications with improved system stability, monitoring capability, and energy management performance.
Keywords:
Microgrid, Renewable Energy Monitoring System (REMS), Model Predictive Control (MPC), Solar Photovoltaic (PV), Wind Turbine, Battery Energy Storage System (BESS), MATLAB/Simulink, Hybrid Renewable Energy System, DC-Link Control, Energy Management System (EMS).
Cite Article:
"Renewable Energy Monitoring System for Microgrid Using Novel Model Predictive Control", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b663-b671, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605176.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator