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Found 10 papers, 1 papers with code
Compact Optimization Learning for AC Optimal Power Flow
This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA).
Bucketized Active Sampling for Learning ACOPF
This paper considers optimization proxies for Optimal Power Flow (OPF), i. e., machine-learning models that approximate the input/output relationship of OPF.
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e. g., wind/solar), dispatchable devices (e. g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization.
Spatial Network Decomposition for Fast and Scalable AC-OPF Learning
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training.
Load Encoding for Learning AC-OPF
The AC Optimal Power Flow (AC-OPF) problem is a core building block in electrical transmission system.
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
It is non-convex and NP-hard, and computationally challenging for large-scale power systems.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Bilevel Optimization for Differentially Private Optimization in Energy Systems
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive.
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems.
Privacy-Preserving Obfuscation of Critical Infrastructure Networks
The paper studies how to release data about a critical infrastructure network (e. g., the power network or a transportation network) without disclosing sensitive information that can be exploited by malevolent agents, while preserving the realism of the network.
Differential Privacy for Power Grid Obfuscation
To address these concerns, this paper presents a novel differential privacy mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated network.
