This paper proposes a Hybrid Microgrid (HμG) model including distributed generation (DG) and a hydrogen-based storage system, controlled through a tailored control strategy. The HμG is composed of three DG units, two of them supplied by solar and wind sources, and the latter one based on the exploitation of theProton Exchange Membrane (PEM) technology. Furthermore, the system includes an alkaline electrolyser, which is used as a responsive load to balance the excess of Variable Renewable Energy Sources (VRES) production, and to produce the hydrogen that will be stored into the hydrogen tank and that will be used to supply the fuel cell in case of lack of generation. The main objectives of this work are to present a validated dynamic model for every component of the HμG and to provide a strategy to reduce as much as possible the power absorption from the grid by exploiting the VRES production. The alkaline electrolyser and PEM fuel cell models are validated through real measurements. The State of Charge (SoC) of the hydrogen tank is adjusted through an adaptive scheme. Furthermore, the designed supervisor power control allows reducing the power exchange and improving the system stability. Finally, a case, considering a summer load profile measured in an electrical substation of Politecnico di Torino, is presented. The results demonstrates the advantages of a hydrogen-based micro-grid, where the hydrogen is used as medium to store the energy produced by photovoltaic and wind systems, with the aim to improve the self-sufficiency of the system
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Hybrid Energy Storage System (HESS) have the potential to offer better flexibility to a grid than any single energy storage solution. However, sizing a HESS is challenging, as the required capacity, power and ramp rates for a given application are difficult to derive. This paper proposes a method for splitting a given load profile into several storage technology independent sub-profiles, such that each of the sub-profiles leads to its own requirements. This method can be used to gain preliminary insight into HESS requirements before a choice is made for specific storage technologies. To test the method, a household case is investigated using the derived methodology, and storage requirements are found, which can then be used to derive concrete storage technologies for the HESS of the household. Adding a HESS to the household case reduces the maximum import power from the connected grid by approximately 7000 W and the maximum exported power to the connected grid by approximately 1000 W. It is concluded that the method is particularly suitable for data sets with a high granularity and many data points.
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