A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow

Penetration enhancement of renewable energy sources is a core component of Korean green-island microgrid projects. This approach calls for a robust energy management system to control the stochastic behavior of renewable energy sources. Therefore, in this paper, we put forward a novel reinforcement learning-driven optimization solution for the convex problem arrangement of the Gasa island microgrid energy management as one of the prominent pilots of the Korean green islands project. We manage the convergence speed of the alternating direction method of multipliers solution for this convex problem by accurately estimating the penalty parameter with the soft actor-critic technique. However, in this arrangement, the soft actor-critic faces sparse reward hindrance, which we address here with the normalizing flow policy. Furthermore, we study the effect of demand response implementation in the Gasa island microgrid to reduce the diesel generator dependency of the microgrid and provide benefits, such as peak-shaving and gas emission reduction.

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“…We deploy the decompose technique introduced in 23 to solve our ADMM problem. To this end, we consider two penalty parameter vectors as follows.…”

Section: Proposed Methodsmentioning

confidence: 99%

A robust energy management system for Korean green islands project

Tightiz

Yoo

2022

Sci Rep

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“…2) We will enhance the learning performance of IIADMM by adaptively updating algorithm parameters such as penalty ρ t and proximity ζ t . In addition to existing techniques, ML approaches (e.g., reinforcement learning [30]) can be used for updating such parameters. 3) Computation of the sensitivity parameter ∆ used in Section IV is key to achieving greater learning performance while preserving data privacy.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

Ryu¹,

Kim²,

Kim³

et al. 2022

Preprint

Self Cite

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Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in classical machine learning. The FL capability is especially important to domains such as biomedicine and smart grid, where data may not be shared freely or stored at a central location because of policy challenges. Thanks to the capability of learning from decentralized datasets, FL is now a rapidly growing research field, and numerous FL frameworks have been developed. In this work, we introduce APPFL, the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users to leverage implemented privacy-preserving algorithms, implement new algorithms, and simulate and deploy various FL algorithms with privacy-preserving techniques. The modular framework enables users to customize the components for algorithms, privacy, communication protocols, neural network models, and user data. We also present a new communication-efficient algorithm based on an inexact alternating direction method of multipliers. The algorithm requires significantly less communication between the server and the clients than does the current state of the art. We demonstrate the computational capabilities of APPFL, including differentially private FL on various test datasets and its scalability, by using multiple algorithms and datasets on different computing environments.

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A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow

Cited by 2 publications

References 22 publications

A robust energy management system for Korean green islands project

A robust energy management system for Korean green islands project

APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

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