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May 28, 2025Results in Engineering6 citationsOpen Access

Decentralized model predictive control of hybrid renewable microgrids for maximizing the power extraction and enhancing system operation, using a novel enumeration based-weighting factor determination method

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EJEtoju JacobHFHooman Farzaneh

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Abstract

The study introduces a decentralized control approach by developing an advanced Continuous Control Set Model Predictive Control (CCS-MPC) scheme to optimize power extraction and regulate the DC bus voltage in hybrid renewable (solar-wind) microgrids coupled to battery storage with DC/DC converters. The cost function of CCS-MPCs typically includes multiple performance metrics, and determining the appropriate weights for these metrics is a significant challenge. To this aim, the developed CCS-MPC in this study utilizes a novel enumeration-based method for determining the weight factors. This helps to precisely control the dynamic behavior of microgrid systems. The proposed CCS-MPC demonstrates better performance in voltage regulation, with lower overshoot and reduced ripple compared to the conventional PI controller. Simulations in a 500 V DC microgrid show 99.96 % power tracking accuracy and improved stability, while also achieving enhanced power extraction (2.19 % for the PV system, 3.46 % for the WEC system) compared to the 12 V DC PO-based experimental method. The developed CCS-MPC provides robust, precise control under dynamic conditions, offering a reliable alternative to PI controllers in DC microgrid applications.

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Jacob et al. (2025) studied this question.

synapsesocial.com/papers/6a8c448acc5fa73295d48e08https://doi.org/10.1016/j.rineng.2025.105477
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