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A Linearized Power Flow Model for Optimization in Unbalanced Distribution Systems

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arxiv 1606.04492 v2 pith:HVAWO6TJ submitted 2016-06-14 math.OC

classification math.OC
keywords linearizedpowerdistributionflowformulationrelaxationssystemsunbalanced
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Optimal Power Flow (OPF) is an important tool used to coordinate assets in electric power systems to ensure customer voltages are within pre-defined tolerances and to improve distribution system operations. While convex relaxations of Optimal Power Flow (OPF) problems have been proposed for both balanced and unbalanced networks, these approaches do not provide universal convexity guarantees and scale inefficiently as network size and the number of constraints increase. To address these issues, we have recently explored a novel linearized OPF formulation for unbalanced distribution systems. Our approach is made possible in part by approximating the ratio of voltages across phases throughout the network. In this work, we generalize the previous formulation to allow arbitrary complex numbers to approximate voltage ratios at different locations in the feeder. Furthermore, we continue the analysis of the linearized OPF via comparison to results obtained through convex relaxations and Semi-Definite Programming (SDP). Simulations of IEEE test feeders show that the proposed formulation produces control decisions that closely approximate those obtained via SDP relaxations. In a specific case, we show that the linearized OPF is capable of solving certain problems which cannot be formulated as SDPs.

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Cited by 4 Pith papers

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    Dist3Flow is a non-approximated three-phase branch flow model using real/imaginary voltage components and P/Q flows, solved via backward/forward sweep with ZIP loads and P-Q DER control, validated on OpenDSS for open-...

  3. Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints

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  4. Threshold Optimization and Dynamic Adaptation of Distributed Optimal Power Flow in 5G Networks

    eess.SY 2026-06 unverdicted novelty 4.0 of 10

    An experimental study demonstrates that dynamic threshold adaptation in ADMM-based distributed optimal power flow over 5G reduces convergence time by 26.42% compared to static thresholds.

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