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Electrolyzers Enhancing Flexibility in Electric Grids

Author

Listed:
  • Manish Mohanpurkar

    (Idaho National Laboratory, 2525 Fremont Ave, Idaho Falls, ID 83402, USA)

  • Yusheng Luo

    (Idaho National Laboratory, 2525 Fremont Ave, Idaho Falls, ID 83402, USA)

  • Danny Terlip

    (National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA)

  • Fernando Dias

    (Idaho National Laboratory, 2525 Fremont Ave, Idaho Falls, ID 83402, USA)

  • Kevin Harrison

    (National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA)

  • Joshua Eichman

    (National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA)

  • Rob Hovsapian

    (Idaho National Laboratory, 2525 Fremont Ave, Idaho Falls, ID 83402, USA)

  • Jennifer Kurtz

    (National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA)

Abstract

This paper presents a real-time simulation with a hardware-in-the-loop (HIL)-based approach for verifying the performance of electrolyzer systems in providing grid support. Hydrogen refueling stations may use electrolyzer systems to generate hydrogen and are proposed to have the potential of becoming smarter loads that can proactively provide grid services. On the basis of experimental findings, electrolyzer systems with balance of plant are observed to have a high level of controllability and hence can add flexibility to the grid from the demand side. A generic front end controller (FEC) is proposed, which enables an optimal operation of the load on the basis of market and grid conditions. This controller has been simulated and tested in a real-time environment with electrolyzer hardware for a performance assessment. It can optimize the operation of electrolyzer systems on the basis of the information collected by a communication module. Real-time simulation tests are performed to verify the performance of the FEC-driven electrolyzers to provide grid support that enables flexibility, greater economic revenue, and grid support for hydrogen producers under dynamic conditions. The FEC proposed in this paper is tested with electrolyzers, however, it is proposed as a generic control topology that is applicable to any load.

Suggested Citation

  • Manish Mohanpurkar & Yusheng Luo & Danny Terlip & Fernando Dias & Kevin Harrison & Joshua Eichman & Rob Hovsapian & Jennifer Kurtz, 2017. "Electrolyzers Enhancing Flexibility in Electric Grids," Energies, MDPI, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1836-:d:118301
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    References listed on IDEAS

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    1. Nan Zhou & Nian Liu & Jianhua Zhang & Jinyong Lei, 2016. "Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response," Energies, MDPI, vol. 9(8), pages 1-24, July.
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    4. Jaeyong Chae & Sung-Kwan Joo, 2017. "Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market," Energies, MDPI, vol. 10(7), pages 1-14, June.
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    Cited by:

    1. Welbert A. Rodrigues & Thiago R. Oliveira & Lenin M. F. Morais & Arthur H. R. Rosa, 2018. "Voltage and Power Balance Strategy without Communication for a Modular Solid State Transformer Based on Adaptive Droop Control," Energies, MDPI, vol. 11(7), pages 1-20, July.
    2. Damien Guilbert & Gianpaolo Vitale, 2020. "Improved Hydrogen-Production-Based Power Management Control of a Wind Turbine Conversion System Coupled with Multistack Proton Exchange Membrane Electrolyzers," Energies, MDPI, vol. 13(5), pages 1-18, March.
    3. Damien Guilbert & Gianpaolo Vitale, 2019. "Dynamic Emulation of a PEM Electrolyzer by Time Constant Based Exponential Model," Energies, MDPI, vol. 12(4), pages 1-17, February.

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