4.7 Article

A Study on the Strong Duality of Second-Order Conic Relaxation of AC Optimal Power Flow in Radial Networks

期刊

IEEE TRANSACTIONS ON POWER SYSTEMS
卷 37, 期 1, 页码 443-455

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3087639

关键词

Reactive power; Planning; Optimization; Numerical models; Distribution networks; Computational modeling; Topology; AC optimal power flow; strong duality; second-order conic program; radial network

资金

  1. National Key R&D Program of China [2018YFB0905000]
  2. Science and Technology Project of SGCC [SGTJDK00DWJS1800232]
  3. China Postdoctoral Science Foundation [2019M663722]
  4. Fundamental Research Funds for the Central Universities [xpt012020010, xxj022019033]
  5. U.S. National Science Foundation [CMMI 1635472]

向作者/读者索取更多资源

This paper investigates the popular second-order conic program (SOCP) formulation of AC optimal power flow (OPF) in radial networks, revealing its lack of strong duality in general and proposing closed-form sufficient conditions to ensure strong duality through restrictive reformulations. Numerical studies on test networks and real-world distribution systems confirm the presence of non-negligible duality gaps in this SOCP formulation, validating the proposed sufficient conditions for closing the duality gap.
For the popular second-order conic program (SOCP) formulation of AC optimal power flow (OPF) in a radial network, this paper first shows that it does not have the strong duality property in general. Then, through a series of restrictive reformulations, we derive a set of closed-form sufficient conditions on network parameters that ensure its strong duality. Numerical studies on IEEE 33-bus, 69-bus test networks and two real-world distribution systems confirm that non-negligible duality gaps do exist in this SOCP formulation, and also demonstrate the validity of the proposed sufficient conditions on closing the duality gap. Our results provide an analytical tool to ensure the strong duality of the SOCP power flow formulation and to support algorithm developments for its complex extensions.

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