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Power Hardware-in-the-Loop: Response of Power Components in Real-Time Grid Simulation Environment

Author

Listed:
  • Moiz Muhammad

    (German Aerospace Center (DLR)—Institute of Networked Energy Systems, Carl-von-Ossietzky Strasse, 26129 Oldenburg, Germany)

  • Holger Behrends

    (German Aerospace Center (DLR)—Institute of Networked Energy Systems, Carl-von-Ossietzky Strasse, 26129 Oldenburg, Germany)

  • Stefan Geißendörfer

    (German Aerospace Center (DLR)—Institute of Networked Energy Systems, Carl-von-Ossietzky Strasse, 26129 Oldenburg, Germany)

  • Karsten von Maydell

    (German Aerospace Center (DLR)—Institute of Networked Energy Systems, Carl-von-Ossietzky Strasse, 26129 Oldenburg, Germany)

  • Carsten Agert

    (German Aerospace Center (DLR)—Institute of Networked Energy Systems, Carl-von-Ossietzky Strasse, 26129 Oldenburg, Germany)

Abstract

With increasing changes in the contemporary energy system, it becomes essential to test the autonomous control strategies for distributed energy resources in a controlled environment to investigate power grid stability. Power hardware-in-the-loop (PHIL) concept is an efficient approach for such evaluations in which a virtually simulated power grid is interfaced to a real hardware device. This strongly coupled software-hardware system introduces obstacles that need attention for smooth operation of the laboratory setup to validate robust control algorithms for decentralized grids. This paper presents a novel methodology and its implementation to develop a test-bench for a real-time PHIL simulation of a typical power distribution grid to study the dynamic behavior of the real power components in connection with the simulated grid. The application of hybrid simulation in a single software environment is realized to model the power grid which obviates the need to simulate the complete grid with a lower discretized sample-time. As an outcome, an environment is established interconnecting the virtual model to the real-world devices. The inaccuracies linked to the power components are examined at length and consequently a suitable compensation strategy is devised to improve the performance of the hardware under test (HUT). Finally, the compensation strategy is also validated through a simulation scenario.

Suggested Citation

  • Moiz Muhammad & Holger Behrends & Stefan Geißendörfer & Karsten von Maydell & Carsten Agert, 2021. "Power Hardware-in-the-Loop: Response of Power Components in Real-Time Grid Simulation Environment," Energies, MDPI, vol. 14(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:593-:d:486442
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    References listed on IDEAS

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    1. Luis Ibarra & Antonio Rosales & Pedro Ponce & Arturo Molina & Raja Ayyanar, 2017. "Overview of Real-Time Simulation as a Supporting Effort to Smart-Grid Attainment," Energies, MDPI, vol. 10(6), pages 1-24, June.
    2. Bingda Zhang & Shipei Nie & Zhao Jin, 2018. "Electromagnetic Transient-Transient Stability Analysis Hybrid Real-Time Simulation Method of Variable Area of Interest," Energies, MDPI, vol. 11(10), pages 1-15, October.
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    4. Taha Selim Ustun & Shuichi Sugahara & Masaichi Suzuki & Jun Hashimoto & Kenji Otani, 2020. "Power Hardware in-the-Loop Testing to Analyze Fault Behavior of Smart Inverters in Distribution Networks," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    5. Ron Brandl, 2017. "Operational Range of Several Interface Algorithms for Different Power Hardware-In-The-Loop Setups," Energies, MDPI, vol. 10(12), pages 1-21, November.
    6. Falko Ebe & Basem Idlbi & David E. Stakic & Shuo Chen & Christoph Kondzialka & Matthias Casel & Gerd Heilscher & Christian Seitl & Roland Bründlinger & Thomas I. Strasser, 2018. "Comparison of Power Hardware-in-the-Loop Approaches for the Testing of Smart Grid Controls," Energies, MDPI, vol. 11(12), pages 1-29, December.
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    Citations

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    Cited by:

    1. Annette von Jouanne & Emmanuel Agamloh & Alex Yokochi, 2023. "Power Hardware-in-the-Loop (PHIL): A Review to Advance Smart Inverter-Based Grid-Edge Solutions," Energies, MDPI, vol. 16(2), pages 1-27, January.
    2. Ode Bokker & Henning Schlachter & Vanessa Beutel & Stefan Geißendörfer & Karsten von Maydell, 2022. "Reactive Power Control of a Converter in a Hardware-Based Environment Using Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-12, December.
    3. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Aragón, Gustavo & Pandian, Vinoth & Krauß, Veronika & Werner-Kytölä, Otilia & Thybo, Gitte & Pautasso, Elisa, 2022. "Feasibility and economical analysis of energy storage systems as enabler of higher renewable energy sources penetration in an existing grid," Energy, Elsevier, vol. 251(C).

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