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A GPU-based Distributed Algorithm for Linearized Optimal Power Flow in Distribution Systems
Optimization and Control · Submitted 13 Oct 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our results affirm the scalability and superior speed of our GPU-based approach compared to the CPU-based counterpart.
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The abstract, sentence by sentence
We propose a GPU-based distributed optimization algorithm, aimed at controlling optimal power flow in multi-phase and unbalanced distribution systems. Typically, conventional distributed optimization algorithms employed in such scenarios rely on parallel execution with multiple CPUs. However, this often leads to significant computation time primarily due to the need for optimization solvers to solve subproblems for every iteration of the algorithms. To address this computational challenge, we propose a distributed optimization algorithm that eliminates solver dependencies and harnesses GPU acceleration. The central idea involves decomposing networks to yield subproblems with closed-form solutions based on matrix operations that GPU can efficiently handle. We demonstrate the computational performance of our approach through numerical experiments on four IEEE test instances ranging from 13 to 8500 buses. Our results affirm the scalability and superior speed of our GPU-based approach compared to the CPU-based counterpart.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We propose a GPU-based distributed optimization algorithm, aimed at controlling optimal power flow in multi-phase and unbalanced distribution systems. | contribution 1.00 | 0.83 | 0.09 | asserted 0.99 |
| s1 | Typically, conventional distributed optimization algorithms employed in such scenarios rely on parallel execution with multiple CPUs. | background 1.00 | 0.05 | 0.02 | asserted 0.95 |
| s2 | However, this often leads to significant computation time primarily due to the need for optimization solvers to solve subproblems for every iteration of the algorithms. | background 0.75 | 0.13 | 0.03 | asserted 0.82 |
| s3 | To address this computational challenge, we propose a distributed optimization algorithm that eliminates solver dependencies and harnesses GPU acceleration. | contribution 0.93 | 0.80 | 0.11 | asserted 0.99 |
| s4 | The central idea involves decomposing networks to yield subproblems with closed-form solutions based on matrix operations that GPU can efficiently handle. | method 0.97 | 0.71 | 0.07 | asserted 1.00 |
| s5 | We demonstrate the computational performance of our approach through numerical experiments on four IEEE test instances ranging from 13 to 8500 buses. | method 0.78 | 0.97 | 0.07 | asserted 1.00 |
| s6 | Our results affirm the scalability and superior speed of our GPU-based approach compared to the CPU-based counterpart. | result 0.61 | 0.96 | 0.82 | asserted 1.00 |
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The paper
Original abstract
We propose a GPU-based distributed optimization algorithm, aimed at controlling optimal power flow in multi-phase and unbalanced distribution systems. Typically, conventional distributed optimization algorithms employed in such scenarios rely on parallel execution with multiple CPUs. However, this often leads to significant computation time primarily due to the need for optimization solvers to solve subproblems for every iteration of the algorithms. To address this computational challenge, we propose a distributed optimization algorithm that eliminates solver dependencies and harnesses GPU acceleration. The central idea involves decomposing networks to yield subproblems with closed-form solutions based on matrix operations that GPU can efficiently handle. We demonstrate the computational performance of our approach through numerical experiments on four IEEE test instances ranging from 13 to 8500 buses. Our results affirm the scalability and superior speed of our GPU-based approach compared to the CPU-based counterpart.
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What this paper does with the arXiv papers it cites
What it cites · 2 sentences without a confident label
Uses the method or tool of 2 papers
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Specifically, we employ the linearization technique developed for computing OPF solution in the unbalanced distribution systems [ref] and propose a distributed algorithm that is free from optimization solvers because each subproblem to solve for each iteration of the algorithm admits a closed-form solution expression based on matrix operations, leading to extremely fast computation.
Linearized Optimal Power Flow for Multiphase Radial Networks with Delta Connections · Introduction · 0.95
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To solve the models, a trust-region Newton algorithm [ref] is employed, which fully utilizes multiple GPUs.
Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition · Introduction · 0.93
Cites as background 1 paper
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Distributed OPF in both distribution and transmission systems has been widely studied in the literature (e.g., [ref] ).
Differentially Private Distributed Convex Optimization · Introduction · 1.00
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