Risk–Cost Equilibrium for Grid Reinforcement Under High Renewable Penetration: A Bi-Level Optimization Framework with GAN-Driven Scenario Learning

Risk–Cost Equilibrium for Grid Reinforcement Under High Renewable Penetration: A Bi-Level Optimization Framework with GAN-Driven Scenario Learning

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservativ...

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Bibliographic Details
Main Authors: Feng Liang, Ying Mu, Dashun Guan, Dongliang Zhang, Wenliang Yin
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
Subjects:
transmission expansion planning
bi-level optimization
adversarial scenario generation
generative adversarial networks
distributionally robust optimization
renewable integration
Online Access:https://www.mdpi.com/1996-1073/18/14/3805
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