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A hybrid architecture for volt-var control in active distribution grids

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  • Haider, Rabab
  • Annaswamy, Anuradha M.

Abstract

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

Suggested Citation

  • Haider, Rabab & Annaswamy, Anuradha M., 2022. "A hybrid architecture for volt-var control in active distribution grids," Applied Energy, Elsevier, vol. 312(C).
  • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s030626192200191x
    DOI: 10.1016/j.apenergy.2022.118735
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    References listed on IDEAS

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    1. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
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    3. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    4. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
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    Cited by:

    1. Utama, Christian & Meske, Christian & Schneider, Johannes & Ulbrich, Carolin, 2022. "Reactive power control in photovoltaic systems through (explainable) artificial intelligence," Applied Energy, Elsevier, vol. 328(C).
    2. Kabir, Farzana & Yu, Nanpeng & Gao, Yuanqi & Wang, Wenyu, 2023. "Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems," Applied Energy, Elsevier, vol. 335(C).
    3. Quy Nguyen Minh & Van-Hau Nguyen & Vu Khanh Quy & Le Anh Ngoc & Abdellah Chehri & Gwanggil Jeon, 2022. "Edge Computing for IoT-Enabled Smart Grid: The Future of Energy," Energies, MDPI, vol. 15(17), pages 1-16, August.

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