| Summary: | This article presents a methodological foundation to design and experimentally test a Model Predictive Controller (MPC) to be applied in renewable source-based microgrids with hydrogen as backup. The Model Predictive Controller has been developed with the aim to guarantee the best energy distribution while the microgrid operation is optimized considering both technical and economic parameters. As a differentiating element, this proposal provides a solution to the problem of energy management in real systems, addressing technological challenges such as charge management in topologies with direct battery connection, or loss of performance associated with equipment degradation or the required dynamics in the operation of hydrogen systems. That is, the proposed Model Predictive Controller achieves the optimization of microgrid operation both in the short and in the long-term basis. For this purpose, a generalized multi-objective function has been defined that considers the energy demand, operating costs, system performance as well as the suffered and accumulated degradation by microgrid elements throughout their lifespan. The generality in the definition of the model and cost function, allows multi-objective optimization problems to be raised depending on the application, topology or design criteria to be considered. For this purpose, a heuristic methodology based on artificial intelligence techniques is presented for the tuning of the controller parameters. The Model Predictive Controller has been validated by simulation and experimental tests in a case study, where the performance of the microgrid under energy excess and deficit situations has been tested, considering the constrains defined by the degradation of the systems that make up the microgrid. The designed controller always made it possible to guarantee both the power balance and the optimal energy distribution between systems according to the predefined priority and accumulated degradation, while guaranteeing the maximum operating voltage of the system with a margin of error less than 1%. The simulation and experimental results for the case study showed the validity of the controller and the design methodology used.
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