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Nonlinear Model Predictive Control of an Autonomous Power System Based on Hydrocarbon Reforming and High Temperature Fuel Cell

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  • Alexandros Kafetzis

    (Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
    Department of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Chrysovalantou Ziogou

    (Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Simira Papadopoulou

    (Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece)

  • Spyridon Voutetakis

    (Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Panos Seferlis

    (Department of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

The integration and control of energy systems for power generation consists of multiple heterogeneous subsystems, such as chemical, electrochemical, and thermal, and contains challenges that arise from the multi-way interactions due to complex dynamic responses among the involved subsystems. The main motivation of this work is to design the control system for an autonomous automated and sustainable system that meets a certain power demand profile. A systematic methodology for the integration and control of a hybrid system that converts liquefied petroleum gas (LPG) to hydrogen, which is subsequently used to generate electrical power in a high-temperature fuel cell that charges a Li-Ion battery unit, is presented. An advanced nonlinear model predictive control (NMPC) framework is implemented to achieve this goal. The operational objective is the satisfaction of power demand while maintaining operation within a safe region and ensuring thermal and chemical balance. The proposed NMPC framework based on experimentally validated models is evaluated through simulation for realistic operation scenarios that involve static and dynamic variations of the power load.

Suggested Citation

  • Alexandros Kafetzis & Chrysovalantou Ziogou & Simira Papadopoulou & Spyridon Voutetakis & Panos Seferlis, 2021. "Nonlinear Model Predictive Control of an Autonomous Power System Based on Hydrocarbon Reforming and High Temperature Fuel Cell," Energies, MDPI, vol. 14(5), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1371-:d:509324
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    References listed on IDEAS

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    1. Haghighat Mamaghani, Alireza & Najafi, Behzad & Casalegno, Andrea & Rinaldi, Fabio, 2017. "Predictive modelling and adaptive long-term performance optimization of an HT-PEM fuel cell based micro combined heat and power (CHP) plant," Applied Energy, Elsevier, vol. 192(C), pages 519-529.
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    Cited by:

    1. Tie-Qing Zhang & Seunghun Jung & Young-Bae Kim, 2022. "Hydrogen Production System through Dimethyl Ether Autothermal Reforming, Based on Model Predictive Control," Energies, MDPI, vol. 15(23), pages 1-16, November.

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