Optimal Power Flow in DC Networks with Robust Feasibility and Stability Guarantees

Liu, Jianzhe; Cui, Bai; Molzahn, Daniel K.; Chen, Chen; Lu, Xiaonan; Qiu, Feng

doi:10.1109/TCNS.2021.3124932

Title: Optimal Power Flow in DC Networks with Robust Feasibility and Stability Guarantees

Journal Article · Thu Nov 04 00:00:00 EDT 2021 · IEEE Transactions on Control of Network Systems

DOI:https://doi.org/10.1109/TCNS.2021.3124932· OSTI ID:1834045

Liu, Jianzhe ^[1]; Cui, Bai ^[2]; Molzahn, Daniel K. ^[3]; Chen, Chen ^[4]; Lu, Xiaonan ^[5]; Qiu, Feng ^[1]

Argonne National Lab. (ANL), Lemont, IL (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Georgia Institute of Technology, Atlanta, GA (United States)
Xi'an Jiaotong Univ., Shaanxi (China)
Temple Univ., Philadelphia, PA (United States)

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in DC networks such that stability and operational constraint satisfaction are of concern. Most existing DC network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. Here, in contrast, this paper studies a DN-OPF formulation which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this paper's robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the stability set using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator set-points are obtained by optimizing over the proposed convex stability set. The validity and effectiveness of the propose algorithm is demonstrated through various DC networks adapted from IEEE test cases.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308; AC02-06CH11357

OSTI ID:: 1834045

Alternate ID(s):: OSTI ID: 1960312

Report Number(s):: NREL/JA-5D00-81591; MainId:82364; UUID:07a415db-3b6f-4158-bb5c-eecda9a5c4b8; MainAdminID:63386

Journal Information:: IEEE Transactions on Control of Network Systems, Vol. 9, Issue 2; ISSN 2325-5870

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
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