Searcharxiv⌕ Search

arXiv · 2610.25052

Generalized and Unified Power Flow Framework for Converter-Dominated AC/DC Power Grids

Abstract

Modern power grids increasingly rely on power electronic interfaces, requiring steady-state power flow analysis to accommodate diverse operating modes and network structures. Conventional approaches often rely on system-specific formulations and sequential approaches to handle such complexity. However, these approaches may limit applicability to diverse grid configurations and lead to convergence and computational issues. To address these limitations, this paper proposes a generalized and unified Newton-Raphson (NR) power flow framework for AC/DC hybrid grids. For generalized operational modeling, conventional and droop operating modes of generators and interlinking converters are represented through a common set of coefficients. For generalized structural modeling, models of AC transformers and DC/DC converters accommodate different transformer winding configurations and converter operating modes. For a unified solution, augmented variables are introduced to incorporate droop deadbands, output limits, and converter station losses into a single NR formulation, eliminating the need for sequential outer loops. To validate the effectiveness of the proposed framework, its results are compared with those obtained from existing power flow methods and electromagnetic transient simulations across transmission networks, distribution networks, and microgrids. The implementation is made publicly available to facilitate its use in analyzing AC/DC hybrid grids.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hamin Chang, Fei Teng, Gyu-Sub Lee, Jae-Won Chang. 2026-10-18. Generalized and Unified Power Flow Framework for Converter-Dominated AC/DC Power Grids. https://arxiv.org/abs/2610.25052

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Multiplexed Label-Free Cytokine Sensing Using a 128x128 CMOS ISFET Array with Flexdym Microfluidic Partitioning

Cytokine-mediated inflammation disorders such as sepsis and cytokine release syndrome demand bedside multiplex panels that resolve pro- and anti-inflammatory dynamics in minutes, yet label-free FET cytokine sensors remain limited to few devices, single analytes, and PDMS packaging that sorbs small molecules and leaches oligomers. We present a CMOS platform pairing a 128 x 128 ion-sensitive field-effect transistor (ISFET) array (SkyWater 130 nm, SiN gate) with a FlexDym 4 x 1 micro-well overlay that partitions the membrane into four chemically independent regions (approximately 2,176 pixels each, square-root-of-N averaging). Two wells carry capture antibodies (anti-IL-6, anti-IL-10), one is a non-specific reference, and one tests specificity; the transduced signal is defined as the differential current relative to the reference signal (Delta I_D(W_n) - Delta I_D(W_3)). Single-analyte dose-response measurements spanning 100 fM to 10 nM successfully resolve both IL-6 and IL-10 above the non-specific detection floor, including at healthy-serum baseline concentrations. The array shows a per-pixel pH sensitivity of 1.13 microamperes per pH unit (CV approximately 14%). Across two chips, device-to-device variation is dominated by transconductance spread (g_m CV approximately 62%); a one-time per-device g_m calibration reduces analyte-measurement variance to approximately 16%. We further validate the surface functionalization, characterize device-to-device variation, and demonstrate the FlexDym integration workflow. Simultaneous multiplexed cytokine measurements resolve IL-6 (W1) and IL-10 (W2) against the chemistry-only reference (W3). At the same time, cross-specificity characterization is assessed by applying IL-6 to an anti-IL-10 well (W4), establishing a scalable label-free ISFET architecture for multiplexed cytokine sensing.

eess.SY↗

Data-Driven Minimum Energy Control for Stochastic Linear Systems with Finite Sample Guarantees

The open-loop minimum-energy control (MEC) problem asks for control inputs that minimize the control energy while ensuring the terminal state reaches a desired value. In this paper, we study the open-loop MEC problem for stochastic linear time-invariant systems. We consider a variety of stochastic constraints that enforce the terminal error to be \emph{small}, and show that Stochastic MEC (SMEC) problem can be formulated as a QCQP. We obtain the optimal solution for SMEC and show that it generalizes the well-known Deterministic MEC problem by introducing novel stochastic variants of controllability and Gramian matrices and show that there exists a fundamental lower bound on the optimal control energy which cannot be overcome by increasing the time horizon of the problem. We then extend the formulation to systems with unknown dynamics using maximum-likelihood estimation and conformal calibration, and compare these approaches with direct sample-based estimation that do not incorporate structural information of SMEC problem into account. Further, with the help of bias-variance analysis, we show when incorporating structural information proves to be better than direct sample based approach. Finally, we establish finite-sample high-probability upper bounds on optimal-cost sub-optimality and original constraint-violation probability under the data-driven controller, quantifying their dependence on the number of experiments, number of samples per experiment, system dimension, input sequence dimension, and noise-to-signal ratio. Using simulations, we validate the theoretical results numerically.

eess.SY↗

Anytime-Valid Runtime Monitoring of Learned Barrier Certificates

Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ from those used for training. We propose an anytime-valid runtime monitor that audits a learned barrier certificate using observed closed-loop trajectories. The monitor is intended to detect loss of statistical support for the deployed barrier condition; it does not by itself guarantee detection before unsafe entry or safety of a subsequent intervention. Specifically, the conditional barrier-drift condition is formulated as an episode-wise sequential hypothesis test, and observed barrier increments are transformed into a nonnegative test supermartingale, yielding finite-sample false-alarm control under arbitrary stopping times. Inspired by finite-horizon safety verification, we further introduce a deterministic temporal shaping of the monitoring boundary, making the monitor more conservative early in a prescribed deployment horizon while preserving anytime-validity. The monitor is designed to detect statistical evidence that the safety certificate is no longer consistent with the observed dynamics, rather than to predict unsafe entry. To assess the monitor empirically, we evaluate empirical alarm behavior under valid and invalid certificates. Code and data are available at https://github.com/FormalCert-AI/runtime-barrier-monitor.

eess.SY↗