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State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin

Author

Listed:
  • Rachid Darbali-Zamora

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Jay Johnson

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Adam Summers

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • C. Birk Jones

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Clifford Hansen

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Chad Showalter

    (Connected Energy, Pittsburgh, PA 15220, USA)

Abstract

Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.

Suggested Citation

  • Rachid Darbali-Zamora & Jay Johnson & Adam Summers & C. Birk Jones & Clifford Hansen & Chad Showalter, 2021. "State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin," Energies, MDPI, vol. 14(3), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:774-:d:491432
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    References listed on IDEAS

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    1. Adam Summers & Jay Johnson & Rachid Darbali-Zamora & Clifford Hansen & Jithendar Anandan & Chad Showalter, 2020. "A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations," Energies, MDPI, vol. 13(14), pages 1-26, July.
    2. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    3. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    4. Nallapaneni Manoj Kumar & Aritra Ghosh & Shauhrat S. Chopra, 2020. "Power Resilience Enhancement of a Residential Electricity User Using Photovoltaics and a Battery Energy Storage System under Uncertainty Conditions," Energies, MDPI, vol. 13(16), pages 1-26, August.
    5. Ahl, A. & Yarime, M. & Goto, M. & Chopra, Shauhrat S. & Kumar, Nallapaneni Manoj. & Tanaka, K. & Sagawa, D., 2020. "Exploring blockchain for the energy transition: Opportunities and challenges based on a case study in Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
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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. Yuriy Bilan & Marcin Rabe & Katarzyna Widera, 2022. "Distributed Energy Resources: Operational Benefits," Energies, MDPI, vol. 15(23), pages 1-7, November.
    3. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    4. Zhao Song & Christoph M. Hackl & Abhinav Anand & Andre Thommessen & Jonas Petzschmann & Omar Kamel & Robert Braunbehrens & Anton Kaifel & Christian Roos & Stefan Hauptmann, 2023. "Digital Twins for the Future Power System: An Overview and a Future Perspective," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    5. Sri Nikhil Gupta Gourisetti & Sraddhanjoli Bhadra & David Jonathan Sebastian-Cardenas & Md Touhiduzzaman & Osman Ahmed, 2023. "A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications," Energies, MDPI, vol. 16(13), pages 1-58, June.
    6. Luis Vargas & Henrry Moyano, 2023. "A Novel Multi-Area Distribution State Estimation Approach with Nodal Redundancy," Energies, MDPI, vol. 16(10), pages 1-19, May.

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