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

Feng Liang (), Ying Mu, Dashun Guan, Dongliang Zhang and Wenliang Yin
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Feng Liang: School of Electrical and Electronic Engineering, Shandong University of Technology, Zhangdian District, Zibo 255000, China
Ying Mu: Economic & Technology Research Institute, State Grid Shandong Electric Power Company, No. 111 Weishi Road, Jinan 250021, China
Dashun Guan: Economic & Technology Research Institute, State Grid Shandong Electric Power Company, No. 111 Weishi Road, Jinan 250021, China
Dongliang Zhang: Economic & Technology Research Institute, State Grid Shandong Electric Power Company, No. 111 Weishi Road, Jinan 250021, China
Wenliang Yin: School of Electrical and Electronic Engineering, Shandong University of Technology, Zhangdian District, Zibo 255000, China

Energies, 2025, vol. 18, issue 14, 1-25

Abstract: 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-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs.

Keywords: transmission expansion planning; bi-level optimization; adversarial scenario generation; generative adversarial networks; distributionally robust optimization; renewable integration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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