Scalable Optimization Methods for Distribution Networks With High PV Integration

Guggilam, Swaroop S.; Dall'Anese, Emiliano; Chen, Yu Christine; Dhople, Sairaj V.; Giannakis, Georgios B.

doi:10.1109/tsg.2016.2543264

Title: Scalable Optimization Methods for Distribution Networks With High PV Integration

Journal Article · Mon Apr 04 00:00:00 EDT 2016 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2016.2543264· OSTI ID:1329994

Guggilam, Swaroop S. ^[1]; Dall'Anese, Emiliano ^[2]; Chen, Yu Christine ^[3]; Dhople, Sairaj V. ^[1]; Giannakis, Georgios B. ^[1]

University of Minnesota, Minneapolis, MN (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
University of British Columbia, Vancouver, BC (Canada)

Here this paper proposes a suite of algorithms to determine the active- and reactive-power setpoints for photovoltaic (PV) inverters in distribution networks. The objective is to optimize the operation of the distribution feeder according to a variety of performance objectives and ensure voltage regulation. In general, these algorithms take a form of the widely studied ac optimal power flow (OPF) problem. For the envisioned application domain, nonlinear power-flow constraints render pertinent OPF problems nonconvex and computationally intensive for large systems. To address these concerns, we formulate a quadratic constrained quadratic program (QCQP) by leveraging a linear approximation of the algebraic power-flow equations. Furthermore, simplification from QCQP to a linearly constrained quadratic program is provided under certain conditions. The merits of the proposed approach are demonstrated with simulation results that utilize realistic PV-generation and load-profile data for illustrative distribution-system test feeders.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF); University of Minnesota

Grant/Contract Number:: AC36-08GO28308; CCF 1423316; CyberSEES 1442686; ECCS-1453921; RL-0010-13

OSTI ID:: 1329994

Report Number(s):: NREL/JA-5D00-66169

Journal Information:: IEEE Transactions on Smart Grid, Vol. 7, Issue 4; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 98 works

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