This study proposes a multi-level model predictive control (MPC) for a grid-connected wind farm paired to a hydrogen-based storage system (HESS) to produce hydrogen as a fuel for commercial road vehicles while meeting electric and contractual loads at the same time. In particular, the integrated system (wind farm þ HESS) should comply with the “fuel production” use case as per the IEA-HIA report, where the hydrogen production for fuel cell electric vehicles (FCEVs) has the highest unconditional priority among all the objectives. Based on models adopting mixed-integer constraints and dynamics, the problem of external hydrogen consumer requests, optimal load demand tracking, and electricity market participation is solved at different timescales to achieve a long-term plan based on forecasts that then are adjusted at real-time. The developed controller will be deployed onto the management platform of the HESS which is paired to a wind farm established in North Norway within the EU funded project HAEOLUS. Numerical analysis shows that the proposed controller efficiently manages the integrated system and commits the equipment so as to comply with the requirements of the addressed scenario. The operating costs of the devices are reduced by 5%, which corresponds to roughly 300 commutations saved per year for devices.

Two-stage model predictive control for a hydrogen-based storage system paired to a wind farm towards green hydrogen production for fuel cell electric vehicles

Shehzad M. F.;Mariani V.;Liuzza D.;Glielmo L.
2022-01-01

Abstract

This study proposes a multi-level model predictive control (MPC) for a grid-connected wind farm paired to a hydrogen-based storage system (HESS) to produce hydrogen as a fuel for commercial road vehicles while meeting electric and contractual loads at the same time. In particular, the integrated system (wind farm þ HESS) should comply with the “fuel production” use case as per the IEA-HIA report, where the hydrogen production for fuel cell electric vehicles (FCEVs) has the highest unconditional priority among all the objectives. Based on models adopting mixed-integer constraints and dynamics, the problem of external hydrogen consumer requests, optimal load demand tracking, and electricity market participation is solved at different timescales to achieve a long-term plan based on forecasts that then are adjusted at real-time. The developed controller will be deployed onto the management platform of the HESS which is paired to a wind farm established in North Norway within the EU funded project HAEOLUS. Numerical analysis shows that the proposed controller efficiently manages the integrated system and commits the equipment so as to comply with the requirements of the addressed scenario. The operating costs of the devices are reduced by 5%, which corresponds to roughly 300 commutations saved per year for devices.
Energy management
Energy storage
Hydrogen energy conversion
Multi-level model predictive control
Multi-objective optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/56261
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