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Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Author

Listed:
  • Jude Suchithra

    (Australian Power Quality Research Centre, University of Wollongong, Wollongong 2522, Australia)

  • Amin Rajabi

    (Australian Power Quality Research Centre, University of Wollongong, Wollongong 2522, Australia)

  • Duane A. Robinson

    (Australian Power Quality Research Centre, University of Wollongong, Wollongong 2522, Australia)

Abstract

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

Suggested Citation

  • Jude Suchithra & Amin Rajabi & Duane A. Robinson, 2024. "Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control," Energies, MDPI, vol. 17(20), pages 1-28, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5037-:d:1495893
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    References listed on IDEAS

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