Differentiable Power-Flow Optimization

• URL: http://arxiv.org/abs/2603.28203v1
• Date: Mon, 30 Mar 2026 09:19:43 GMT
• Title: Differentiable Power-Flow Optimization
• Abstract summary: Differentiable Power-Flow (DPF) is a reformulation of the AC power-flow problem as a differentiable simulation.<n>DPF enables end-to-end propagation from the physical power mismatches to the underlying simulation parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and computationally demanding. Conventional AC-power-flow simulations, which use the Newton-Raphson (NR) method, suffer from poor scalability, making them impractical for emerging use cases such as joint transmission-distribution modeling and global grid analysis. At the same time, purely data-driven surrogate models lack physical guarantees and may violate fundamental constraints. In this work, we propose Differentiable Power-Flow (DPF), a reformulation of the AC power-flow problem as a differentiable simulation. DPF enables end-to-end gradient propagation from the physical power mismatches to the underlying simulation parameters, thereby allowing these parameters to be identified efficiently using gradient-based optimization. We demonstrate that DPF provides a scalable alternative to NR by leveraging GPU acceleration, sparse tensor representations, and batching capabilities available in modern machine-learning frameworks such as PyTorch. DPF is especially suited as a tool for time-series analyses due to its efficient reuse of previous solutions, for N-1 contingency-analyses due to its ability to process cases in batches, and as a screening tool by leveraging its speed and early stopping capability. The code is available in the authors' code repository.

Related papers

• GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis [6.9646837562713]
  We present GENCO, a unified solver for steady-state transmission grid analysis.<n>It handles power flow (PF), optimal power flow (OPF), runtime state estimation (SE) within a single architecture.<n>We release large-scale datasets with millions PF and OPF scenarios across diverse grid topologies to support reproducible benchmarking.
  arXiv  Detail & Related papers  (2026-08-10T17:57:49Z)
• FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow [4.037525873726113]
  AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints.<n>This paper presents FMOPF, a framework that resolves the conflation of two tasks within a single model.<n>Experiments on four IEEE test systems demonstrate that FMOPF provides the most effective Newton-Raphson warm starts.
  arXiv  Detail & Related papers  (2026-07-24T12:49:09Z)
• EFF-Grasp: Energy-Field Flow Matching for Physics-Aware Dexterous Grasp Generation [54.09734511705173]
  EFF-Grasp is a Flow-Matching synthesis-based framework for physics-aware dexterous grasp generation.<n>We reformulate grasp as a deterministic ordinary differential equation (ODE) process, which enables efficient and stable generation through smooth probability flows.<n> EFF-Grasp achieves superior performance in grasp quality and physical feasibility, while requiring substantially fewer sampling steps than diffusion-based baselines.
  arXiv  Detail & Related papers  (2026-03-17T06:11:56Z)
• Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation [7.958594167693376]
  We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM)<n>The strategy attains accuracy comparable to full retraining while requiring only a fraction of the computational time.<n>We show that, for the dynamical system considered, the dominant source of error in out-of-sample forecasts stems from distortions of the latent manifold.
  arXiv  Detail & Related papers  (2026-02-26T16:43:28Z)
• Parallel Diffusion Solver via Residual Dirichlet Policy Optimization [88.7827307535107]
  Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature.<n>Existing solver-based acceleration methods often face significant image quality degradation under a low-dimensional budget.<n>We propose the Ensemble Parallel Direction solver (dubbed as EPD-EPr), a novel ODE solver that mitigates these errors by incorporating multiple gradient parallel evaluations in each step.
  arXiv  Detail & Related papers  (2025-12-28T05:48:55Z)
• PF$Δ$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations [0.055997926295092294]
  Power flow calculations are the backbone of real-time grid operations.<n> PF$Delta$ is a benchmark dataset for power flow that captures diverse variations in load, generation, and topology.
  arXiv  Detail & Related papers  (2025-10-24T22:09:09Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• Likelihood-Free Variational Autoencoders [21.092166159353702]
  Variational Autoencoders (VAEs) typically rely on a probabilistic decoder with a predefined likelihood, most commonly an isotropic Gaussian, to model the data conditional on latent variables.<n>We propose EnVAE, a novel likelihood-free generative framework that has a deterministic decoder and employs the energy score--a proper scoring rule--to build the reconstruction loss.<n>Our framework offers a general, scalable, and statistically principled alternative for flexible and nonparametric distribution learning in generative modeling.
  arXiv  Detail & Related papers  (2025-04-24T14:44:46Z)
• Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers [0.37109226820205005]
  We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p using Reinforcement Learning (RL) We develop an offline training approach, decoupled from the main solver, which shows minimal overcost when performing simulations. We derive an inexpensive RL-based error estimation approach that enables the quantification of local discretization errors.
  arXiv  Detail & Related papers  (2024-07-26T17:55:23Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Principal Component Density Estimation for Scenario Generation Using Normalizing Flows [62.997667081978825]
  We propose a dimensionality-reducing flow layer based on the linear principal component analysis (PCA) that sets up the normalizing flow in a lower-dimensional space. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
  arXiv  Detail & Related papers  (2021-04-21T08:42:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.