Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

• URL: http://arxiv.org/abs/2208.14814v1
• Date: Tue, 30 Aug 2022 09:27:59 GMT
• Title: Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
• Authors: Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
• Abstract summary: The paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions.
• Score: 57.70237375696411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic success, the AC CC-OPF problem is highly nonlinear and computationally demanding, which limits its practical impact. For improving the AC-OPF problem complexity/accuracy trade-off, the paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions compared to the state-of-the-art methods.

Related papers

• Physics-Informed GNN for non-linear constrained optimization: PINCO a solver for the AC-optimal power flow [0.0]
  This work explores a physics-informed graph neural network, PINCO, to solve the AC-OPF. PINCO generalizes effectively across a diverse set of loading conditions in the power system. It can function both as a solver and as a hybrid universal function approximator.
  arXiv  Detail & Related papers  (2024-10-07T08:08:36Z)
• Data-Driven Stochastic AC-OPF using Gaussian Processes [0.0]
  The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the alternating current (AC) chance-constrained (CC) Power Flow (OPF) problem.
  arXiv  Detail & Related papers  (2024-02-17T19:30:33Z)
• GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow [54.94701604030199]
  The Gaussian Process (GP) based Chance-Constrained Optimal Flow (CC-OPF) is an open-source Python code for economic dispatch (ED) problem in power grids. The developed tool presents a novel data-driven approach based on the CC-OP model for solving the large regression problem with a trade-off between complexity and accuracy.
  arXiv  Detail & Related papers  (2023-02-16T17:59:06Z)
• Data-Driven Stochastic AC-OPF using Gaussian Processes [54.94701604030199]
  Integrating a significant amount of renewables into a power grid is probably the most a way to reduce carbon emissions from power grids slow down climate change. This paper presents an alternative data-driven approach based on the AC power flow equations that can incorporate uncertainty inputs. The GP approach learns a simple yet non-constrained data-driven approach to close this gap to the AC power flow equations.
  arXiv  Detail & Related papers  (2022-07-21T23:02:35Z)
• DeepOPF-V: Solving AC-OPF Problems Efficiently [12.512036656559683]
  Deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to find feasible solutions with high computational efficiency. DeepOPF-V achieves speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
  arXiv  Detail & Related papers  (2021-03-22T12:59:06Z)
• Load Embeddings for Scalable AC-OPF Learning [46.79747973916068]
  AC Optimal Power Flow (AC-OPF) is a building block in power system optimization. Recent work has shown that deep learning can be effective in providing highly accurate approximations of AC-OPF. This paper addresses these scalability limitations and proposes a load embedding scheme using a 3-step approach.
  arXiv  Detail & Related papers  (2021-01-11T15:28:38Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow [49.2540510330407]
  AC-OPF is a key building block in many power system applications. Motivated by increased penetration of renewable sources, this paper explores deep learning to deliver efficient approximations to the AC-OPF.
  arXiv  Detail & Related papers  (2020-06-29T20:22:16Z)

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.