Privacy-Preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, in this article, a novel federated deep generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least...

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Vydané v:IEEE transactions on industrial informatics Ročník 18; číslo 4; s. 2310 - 2320
Hlavní autori: Li, Yang, Li, Jiazheng, Wang, Yi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Collaborative work
Data models
Decision making
Deep generative models (DGMs)
Federated learning
Generative adversarial networks
Generators
least square generative adversarial networks (GANs)
Least squares
Privacy
renewable energy
Renewable energy sources
Scenario generation
Servers
Training
uncertainty modeling
Wind power generation
ISSN:1551-3203, 1941-0050
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Shrnutí:Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, in this article, a novel federated deep generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least square generative adversarial networks (LSGANs) for renewable scenario generation. Specifically, federated learning learns a shared global model in a central server from renewable sites at network edges, which enables the Fed-LSGAN to generate scenarios in a privacy-preserving manner without sacrificing the generation quality by transferring model parameters, rather than all data. Meanwhile, the LSGANs-based deep generative model generates scenarios that conform to the distribution of historical data through fully capturing the spatial-temporal characteristics of renewable powers, which leverages the least squares loss function to improve the training stability and generation quality. The simulation results demonstrate that the proposal manages to generate high-quality renewable scenarios and outperforms the state-of-the-art centralized methods. Besides, an experiment with different federated learning settings is designed and conducted to verify the robustness of our method.
Bibliografia:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3098259